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Ellmann S, von Rohr F, Komina S, Bayerl N, Amann K, Polifka I, Hartmann A, Sikic D, Wullich B, Uder M, Bäuerle T. Tumor grade-titude: XGBoost radiomics paves the way for RCC classification. Eur J Radiol 2025; 188:112146. [PMID: 40334367 DOI: 10.1016/j.ejrad.2025.112146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2025] [Revised: 04/21/2025] [Accepted: 04/28/2025] [Indexed: 05/09/2025]
Abstract
This study aimed to develop and evaluate a non-invasive XGBoost-based machine learning model using radiomic features extracted from pre-treatment CT images to differentiate grade 4 renal cell carcinoma (RCC) from lower-grade tumours. A total of 102 RCC patients who underwent contrast-enhanced CT scans were included in the analysis. Radiomic features were extracted, and a two-step feature selection methodology was applied to identify the most relevant features for classification. The XGBoost model demonstrated high performance in both training (AUC = 0.87) and testing (AUC = 0.92) sets, with no significant difference between the two (p = 0.521). The model also exhibited high sensitivity, specificity, positive predictive value, and negative predictive value. The selected radiomic features captured both the distribution of intensity values and spatial relationships, which may provide valuable insights for personalized treatment decision-making. Our findings suggest that the XGBoost model has the potential to be integrated into clinical workflows to facilitate personalized adjuvant immunotherapy decision-making, ultimately improving patient outcomes. Further research is needed to validate the model in larger, multicentre cohorts and explore the potential of combining radiomic features with other clinical and molecular data.
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Affiliation(s)
- Stephan Ellmann
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; Radiologisch-Nuklearmedizinisches Zentrum (RNZ), Martin-Richter-Straße 43, 90489 Nürnberg, Germany.
| | - Felicitas von Rohr
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany
| | - Selim Komina
- Institute of Pathology, Faculty of Medicine, Ss Cyril and Methodius University ul. 50 Divizija bb 1000 Skopje, North Macedonia
| | - Nadine Bayerl
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany
| | - Kerstin Amann
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, Germany
| | - Iris Polifka
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; Humanpathologie Dr. Weiß MVZ GmbH, Am Weichselgarten 30a, 91058 Erlangen-Tennenlohe, Germany
| | - Arndt Hartmann
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; Comprehensive Cancer Center Erlangen - EMD, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, Germany
| | - Danijel Sikic
- Clinic of Urology and Pediatric Urology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, Germany
| | - Bernd Wullich
- Clinic of Urology and Pediatric Urology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany; Comprehensive Cancer Center Erlangen - EMD, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, Germany
| | - Michael Uder
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; Comprehensive Cancer Center Erlangen - EMD, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, Germany
| | - Tobias Bäuerle
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; University Medical Center of Johannes Gutenberg-University Mainz, Department of Diagnostic and Interventional Radiology, Langenbeckstr. 1, 55131 Mainz, Germany
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Donatello D. An overview of the use of cutting-edge artificial intelligence (AI) modeling to produce synthetic medical data (SMD) in decentralized clinical machine learning (ML) for ovarian cancer(OC) and ovarian lymphoma(OL). J Ultrasound 2025; 28:483-492. [PMID: 39841353 PMCID: PMC12145400 DOI: 10.1007/s40477-025-00983-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2024] [Accepted: 01/07/2025] [Indexed: 01/23/2025] Open
Abstract
AIM o point out how novel analysis tools of AI can make sense of the data acquired during OL and OC diagnosis and treatment in an effort to help improve and standardize the patient pathway for these disease. MATERIAL AND METHODS ultilizing programmed detection of heterogeneus OL and OC habitats through radiomics and correlate to imaging based tumor grading plus a literature review. RESULTS new analysis pipelines have been generated for integrating imaging and patient demographic data and identify new multi-omic biomarkers of response prediction and tumour grading using cutting-edge artificial intelligence (AI) in OL and OC. DESCRIPTION deline the main AI methods used in OL and OC that we can try to standardize in the clinical radiological and medical practice to ameliorate the patients diagnosis and theraphy. CONCLUSION through new AI methods it's possible to combine research into a SwarmDeepSurv, generate new data flow channels, create medical imaging data channels of OL and OC using AI and identify new biomarkers of OL and OC. .
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Wu M, Que Z, Lai S, Li G, Long J, He Y, Wang S, Wu H, You N, Lan X, Wen L. Predicting the early therapeutic response to hepatic artery infusion chemotherapy in patients with unresectable HCC using a contrast-enhanced computed tomography-based habitat radiomics model: a multi-center retrospective study. Cell Oncol (Dordr) 2025; 48:709-723. [PMID: 39903419 PMCID: PMC12119385 DOI: 10.1007/s13402-025-01041-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/23/2025] [Indexed: 02/06/2025] Open
Abstract
OBJECTIVE Predicting the therapeutic response before initiation of hepatic artery infusion chemotherapy (HAIC) with fluorouracil, leucovorin, and oxaliplatin (FOLFOX) remains challenging for patients with unresectable hepatocellular carcinoma (HCC). Herein, we investigated the potential of a contrast-enhanced CT-based habitat radiomics model as a novel approach for predicting the early therapeutic response to HAIC-FOLFOX in patients with unresectable HCC. METHODS A total of 148 patients with unresectable HCC who received HAIC-FOLFOX combined with targeted therapy or immunotherapy at three tertiary care medical centers were enrolled retrospectively. Tumor habitat features were extracted from subregion radiomics based on CECT at different phases using k-means clustering. Logistic regression was used to construct the model. This CECT-based habitat radiomics model was verified by bootstrapping and compared with a model based on clinical variables. Model performance was evaluated using the area under the curve (AUC) and a calibration curve. RESULTS Three intratumoral habitats with high, moderate, and low enhancement were identified to construct a habitat radiomics model for therapeutic response prediction. Patients with a greater proportion of high-enhancement intratumoral habitat showed better therapeutic responses. The AUC of the habitat radiomics model was 0.857 (95% CI: 0.798-0.916), and the bootstrap-corrected concordance index was 0.842 (95% CI: 0.785-0.907), resulting in a better predictive value than the clinical variable-based model, which had an AUC of 0.757 (95% CI: 0.679-0.834). CONCLUSION The CECT-based habitat radiomics model is an effective, visualized, and noninvasive tool for predicting the early therapeutic response of patients with unresectable HCC to HAIC-FOLFOX treatment and could guide clinical management and decision-making.
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Affiliation(s)
- Mingsong Wu
- Department of Gastroenterology, Daping Hospital, Army Medical University (Third Military Medical University), No. 10, Changjiang Branch Road, Yuzhong District, Chongqing, 400042, P. R. China
| | - Zenglong Que
- Department of Infectious Diseases, The 960 Hospital of PLA, No. 25, Shifan Road, Tianqiao District, Jinan City, Shandong Province, 250031, P. R. China
| | - Shujie Lai
- Department of Gastroenterology, Daping Hospital, Army Medical University (Third Military Medical University), No. 10, Changjiang Branch Road, Yuzhong District, Chongqing, 400042, P. R. China
| | - Guanhui Li
- Department of Gastroenterology, Daping Hospital, Army Medical University (Third Military Medical University), No. 10, Changjiang Branch Road, Yuzhong District, Chongqing, 400042, P. R. China
| | - Jie Long
- Department of Gastroenterology, Daping Hospital, Army Medical University (Third Military Medical University), No. 10, Changjiang Branch Road, Yuzhong District, Chongqing, 400042, P. R. China
| | - Yuqin He
- Department of Gastroenterology, Daping Hospital, Army Medical University (Third Military Medical University), No. 10, Changjiang Branch Road, Yuzhong District, Chongqing, 400042, P. R. China
| | - Shunan Wang
- Department of Radiological, Daping Hospital, Army Medical University (Third Military Medical University), No. 10, Changjiang Branch Road, Yuzhong District, Chongqing, 400042, P. R. China
| | - Hao Wu
- Department of Radiological, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuanjiagang, Yuzhong District, Chongqing, 400042, P. R. China
| | - Nan You
- Department of Hepatobiliary, Xinqiao Hospital Affiliated to The Army Medical University, No. 1 Xinqiao Main Street, Shapingba District, Chongqing, 400037, P. R. China.
| | - Xiang Lan
- Department of Hepatobiliary, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuanjiagang, Yuzhong District, Chongqing, 400042, P. R. China.
| | - Liangzhi Wen
- Department of Gastroenterology, Daping Hospital, Army Medical University (Third Military Medical University), No. 10, Changjiang Branch Road, Yuzhong District, Chongqing, 400042, P. R. China.
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Liang Z, Li J, He S, Li S, Cai R, Chen C, Zhang Y, Deng B, Wu Y. Thymoma habitat segmentation and risk prediction model using CT imaging and K-means clustering. Med Phys 2025. [PMID: 40387507 DOI: 10.1002/mp.17892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2024] [Revised: 05/03/2025] [Accepted: 05/05/2025] [Indexed: 05/20/2025] Open
Abstract
BACKGROUND Thymomas, though rare, present a wide range of clinical behaviors, from indolent to aggressive forms, making accurate risk stratification crucial for treatment planning. Traditional methods such as histopathology and radiological assessments often lack the ability to capture tumor heterogeneity, which can impact prognosis. Radiomics, combined with machine learning, provides a method to extract and analyze quantitative imaging features, offering the potential to improve tumor classification and risk prediction. By segmenting tumors into distinct habitat zones, it becomes possible to assess intratumoral heterogeneity more effectively. This study employs radiomics and machine learning techniques to enhance thymoma risk prediction, aiming to improve diagnostic consistency and reduce variability in radiologists' assessments. OBJECTIVE This study aims to identify different habitat zones within thymomas through CT imaging feature analysis and to establish a predictive model to differentiate between high and low-risk thymomas. Additionally, the study explores how this model can assist radiologists. METHODS We obtained CT imaging data from 133 patients with thymoma who were treated at the Affiliated Hospital of Guangdong Medical University from 2015 to 2023. Images from the plain scan phase, venous phase, arterial phase, and their differential images (subtracted images) were used. Tumor regions were segmented into three habitat zones using K-Means clustering. Imaging features from each habitat zone were extracted using the PyRadiomics (van Griethuysen, 2017) library. The 28 most distinguishing features were selected through Mann-Whitney U tests (Mann, 1947) and Spearman's correlation analysis (Spearman, 1904). Five predictive models were built using the same machine learning algorithm (Support Vector Machine [SVM]): Habitat1, Habitat2, Habitat3 (trained on features from individual tumor habitat regions), Habitat All (trained on combined features from all regions), and Intra (trained on intratumoral features), and their performances were evaluated for comparison. The models' diagnostic outcomes were compared with the diagnoses of four radiologists (two junior and two experienced physicians). RESULTS The AUC (area under curve) for habitat zone 1 was 0.818, for habitat zone 2 was 0.732, and for habitat zone 3 was 0.763. The comprehensive model, which combined data from all habitat zones, achieved an AUC of 0.960, outperforming the model based on traditional radiomic features (AUC of 0.720). The model significantly improved the diagnostic accuracy of all four radiologists. The AUCs for junior radiologists 1 and 2 increased from 0.747 and 0.775 to 0.932 and 0.972, respectively, while for experienced radiologists 1 and 2, the AUCs increased from 0.932 and 0.859 to 0.977 and 0.972, respectively. CONCLUSION This study successfully identified distinct habitat zones within thymomas through CT imaging feature analysis and developed an efficient predictive model that significantly improved diagnostic accuracy. This model offers a novel tool for risk assessment of thymomas and can aid in guiding clinical decision-making.
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Affiliation(s)
- Zhu Liang
- Department of Thoracic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Jiamin Li
- The First Clinical Medical College, Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Shuyan He
- Clinical Medical College, Guangzhou Medical University, Guangzhou, Guangdong, China
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Siyuan Li
- The First Clinical Medical College, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Runzhi Cai
- Department of Radiology, The People Hospital of Longhua, Shenzhen, Guangdong, China
| | - Chunyuan Chen
- Department of Thoracic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Yan Zhang
- Clinical Medical College, Guangzhou Medical University, Guangzhou, Guangdong, China
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Biao Deng
- Department of Thoracic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Yanxia Wu
- Department of Thoracic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
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Huang X, Huang X, Wang K, Bai H, Ye B, Jin G. Habitat-based radiomics from contrast-enhanced CT and clinical data to predict lymph node metastasis in clinical N0 peripheral lung adenocarcinoma ≤ 3 cm. Sci Rep 2025; 15:17085. [PMID: 40379768 PMCID: PMC12084560 DOI: 10.1038/s41598-025-02181-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2024] [Accepted: 05/12/2025] [Indexed: 05/19/2025] Open
Abstract
This study aims to develop an integrated model combining habitat-based radiomics and clinical data to predict lymph node metastasis in patients with clinical N0 peripheral lung adenocarcinomas measuring ≤ 3 cm in diameter. We retrospectively analyzed 1132 patients with lung adenocarcinoma from two centers who underwent surgical resection with lymph node dissection and had preoperative computed tomography (CT) scans showing peripheral nodules ≤ 3 cm. Multivariable logistic regression was employed to identify independent risk factors for the clinical model. Radiomics and habitat models were constructed by extracting and analyzing radiomic features and habitat regions from contrast-enhanced CT images. Subsequently, a combined model was developed by integrating habitat-based radiomic features with clinical characteristics. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The habitat model exhibited promising predictive performance for lymph node metastasis, outperforming other standalone models with AUCs of 0.962, 0.865, and 0.853 in the training, validation, and external test cohorts, respectively. The combined model demonstrated superior discriminative ability, achieving the highest AUCs of 0.983, 0.950, and 0.877 for the training, validation, and external test cohorts, respectively. The integration of habitat-based radiomic features with clinical data offers a non-invasive approach to assess the risk of lymph node metastasis, potentially supporting clinicians in optimizing patient management decisions.
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Affiliation(s)
- Xiaoxin Huang
- Department of Radiology, Jiangbin Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530021, Guangxi, China
| | - Xiaoxiao Huang
- Department of Radiology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China
| | - Kui Wang
- Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, 530021, Guangxi, China
| | - Haosheng Bai
- Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, 530021, Guangxi, China
| | - Bin Ye
- Department of Radiology, Jiangbin Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530021, Guangxi, China
| | - Guanqiao Jin
- Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, 530021, Guangxi, China.
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Qin Y, Zhang LG, Zhou X, Song C, Wu Y, Tang M, Ling Z, Wang J, Cai H, Peng Z, Feng ST. Explainable Fusion Model for Predicting Postoperative Early Recurrence in Hepatocellular Carcinoma Using Gadoxetic Acid-Enhanced MRI Habitat Imaging. Acad Radiol 2025:S1076-6332(25)00317-4. [PMID: 40379586 DOI: 10.1016/j.acra.2025.04.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2025] [Revised: 03/17/2025] [Accepted: 04/07/2025] [Indexed: 05/19/2025]
Abstract
RATIONALE AND OBJECTIVES To develop an explainable fusion model that combines clinical, radiomic, and habitat features to predict postoperative early recurrence in hepatocellular carcinoma (HCC). METHODS The bicentric retrospective study included 370 patients with surgically confirmed early-stage HCC who underwent gadoxetic acid-enhanced MRI. The patients were stratified into a training cohort (n=296) and an external validation cohort (n=74). From the hepatobiliary phase images, habitat and radiomics features were extracted across the entire tumor and used to construct radiomics and habitat models. Additionally, a clinical model was established utilizing relevant clinical features. Subsequently, all previously mentioned features were merged to construct the fusion model (HabRad_FB). Diagnostic performance of these models was assessed and compared using the area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI). The fusion model was then interpreted using SHapley Additive exPlanations (SHAP) analysis. RESULTS Tumor recurrence was observed in 73 out of 370 patients (19.7%; 55.2±11.3 years; male=333). Among all study cohorts, the HabRad_FB model showed the highest AUC (0.820-0.959), outperforming the clinical (0.517-0.729), radiomics (0.707-0.815), and habitat (0.729-0.861) models. The HabRad_FB model also demonstrated significant improvement in IDI in the training cohort and NRI in the validation cohort. SHAP force plots provided valuable insights into the interpretation of HabRad_FB model's predictions for early recurrence. CONCLUSION The HabRad_FB, an explainable fusion model, aids clinicians in accurately and non-invasively predicting the early recurrence of HCC preoperatively. This model might provide great potential in prognostic prediction and clinical management.
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Affiliation(s)
- Yanjin Qin
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou 510080, China
| | - Lie-Guang Zhang
- Department of Radiology, Guangzhou Eighth People's Hospital, Guangzhou, Medical University, Guangzhou 510060, China
| | - Xiaoqi Zhou
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou 510080, China
| | - Chenyu Song
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou 510080, China
| | - Yuxin Wu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou 510080, China
| | - Mimi Tang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou 510080, China
| | - Zhoukun Ling
- Department of Radiology, Guangzhou Eighth People's Hospital, Guangzhou, Medical University, Guangzhou 510060, China
| | - Jifei Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou 510080, China
| | - Huasong Cai
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou 510080, China
| | - Zhenpeng Peng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou 510080, China
| | - Shi-Ting Feng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou 510080, China.
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Zhuo L, Chen W, Xing L, Li X, Song Z, Dong J, Zhang Y, Li H, Cui J, Han Y, Hao J, Wang J, Yin X, Li C. MRI-based quantification of intratumoral heterogeneity for intrahepatic mass-forming cholangiocarcinoma grading: a multicenter study. Insights Imaging 2025; 16:101. [PMID: 40369381 PMCID: PMC12078897 DOI: 10.1186/s13244-025-01985-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2024] [Accepted: 04/27/2025] [Indexed: 05/16/2025] Open
Abstract
OBJECTIVE This study aimed to develop a quantitative approach to measure intratumor heterogeneity (ITH) using MRI scans and predict the pathological grading of intrahepatic mass-forming cholangiocarcinoma (IMCC). METHODS Preoperative MRI scans from IMCC patients were retrospectively obtained from five academic medical centers, covering the period from March 2018 to April 2024. Radiomic features were extracted from the whole tumor and its subregions, which were segmented using K-means clustering. An ITH index was derived from a habitat model integrating output probabilities of the subregions-based models. Significant variables from clinical laboratory-imaging features, radiomics, and the habitat model were integrated into a predictive model, and its performance was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS The final training and internal validation datasets included 197 patients (median age, 59 years [IQR, 52-65 years]); the external validation dataset included 43 patients (median age, 58.5 years [IQR, 52.25-69.75 years]). The habitat model achieved AUCs of 0.847 (95% CI: 0.783, 0.911) in the training set and 0.753 (95% CI: 0.595, 0.911) in the internal validation set. Furthermore, the combined model, integrating imaging variables, the habitat model, and radiomics model, demonstrated improved predictive performance, with AUCs of 0.895 (95% CI: 0.845, 0.944) in the training dataset, 0.790 (95% CI: 0.65, 0.931) in the internal validation dataset, and 0.815 (95% CI: 0.68, 0.951) in the external validation dataset. CONCLUSION The combined model based on MRI-derived quantification of ITH, along with clinical, laboratory, radiological, and radiomic features, showed good performance in predicting IMCC grading. CRITICAL RELEVANCE STATEMENT This model, integrating MRI-derived intrahepatic mass-forming cholangiocarcinoma (IMCC) classification metrics with quantitative radiomic analysis of intratumor heterogeneity (ITH), demonstrates enhanced accuracy in tumor grade prediction, advancing risk stratification for clinical decision-making in IMCC management. KEY POINTS Grading of intrahepatic mass-forming cholangiocarcinoma (IMCC) is important for risk stratification, clinical decision-making, and personalized therapeutic optimization. Quantitative intratumor heterogeneity can accurately predict the pathological grading of IMCC. This combined model provides higher diagnostic accuracy.
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Affiliation(s)
- Liyong Zhuo
- Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, People's Republic of China
| | - Wenjing Chen
- Department of Research and Development, United Imaging Intelligence (Beijing) Co., Ltd., Beijing, People's Republic of China
| | - Lihong Xing
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, People's Republic of China
| | - Xiaomeng Li
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, People's Republic of China
| | - Zijun Song
- Department of Critical Care Medicine, Baoding First Central Hospital, Baoding, People's Republic of China
| | - Jinghui Dong
- Department of Radiology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, People's Republic of China
| | - Yanyan Zhang
- Department of Radiology, Beijing You'an Hospital, Beijing, People's Republic of China
| | - Hongjun Li
- Department of Radiology, Beijing You'an Hospital, Beijing, People's Republic of China
| | - Jingjing Cui
- Department of Research and Development, United Imaging Intelligence (Beijing) Co., Ltd., Beijing, People's Republic of China
| | - Yuxiao Han
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, People's Republic of China
| | - Jiawei Hao
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, People's Republic of China
| | - Jianing Wang
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, People's Republic of China
| | - Xiaoping Yin
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, People's Republic of China.
| | - Caiying Li
- Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China.
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Zhang C, Li S, Huang D, Wen B, Wei S, Song Y, Wu X. Development and Validation of an AI-Based Multimodal Model for Pathological Staging of Gastric Cancer Using CT and Endoscopic Images. Acad Radiol 2025; 32:2604-2617. [PMID: 39753481 DOI: 10.1016/j.acra.2024.12.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2024] [Revised: 12/10/2024] [Accepted: 12/13/2024] [Indexed: 04/23/2025]
Abstract
RATIONALE AND OBJECTIVES Accurate preoperative pathological staging of gastric cancer is crucial for optimal treatment selection and improved patient outcomes. Traditional imaging methods such as CT and endoscopy have limitations in staging accuracy. METHODS This retrospective study included 691 gastric cancer patients treated from March 2017 to March 2024. Enhanced venous-phase CT and endoscopic images, along with postoperative pathological results, were collected. We developed three modeling approaches: (1) nine deep learning models applied to CT images (DeepCT), (2) 11 machine learning algorithms using handcrafted radiomic features from CT images (HandcraftedCT), and (3) ResNet-50-extracted deep features from endoscopic images followed by 11 machine learning algorithms (DeepEndo). The two top-performing models from each approach were combined into the Integrated Multi-Modal Model using a stacking ensemble method. Performance was assessed using ROC-AUC, sensitivity, and specificity. RESULTS The Integrated Multi-Modal Model achieved an ROC-AUC of 0.933 (95% CI, 0.887-0.979) on the test set, outperforming individual models. Sensitivity and specificity were 0.869 and 0.840, respectively. Various evaluation metrics demonstrated that the final fusion model effectively integrated the strengths of each sub-model, resulting in a balanced and robust performance with reduced false-positive and false-negative rates. CONCLUSION The Integrated Multi-Modal Model effectively integrates radiomic and deep learning features from CT and endoscopic images, demonstrating superior performance in preoperative pathological staging of gastric cancer. This multimodal approach enhances predictive accuracy and provides a reliable tool for clinicians to develop individualized treatment plans, thereby improving patient outcomes. DATA AVAILABILITY The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical reasons. All code used in this study is based on third-party libraries and all custom code developed for this study is available upon reasonable request from the corresponding author.
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Affiliation(s)
- Chao Zhang
- Guangxi Medical University, Nanning, Guangxi 530021, China (C.Z., D.H., B.W., S.W., Y.S., X.W.); Guangxi Key Laboratory of Enhanced Recovery After Surgery for Gastrointestinal Cancer, Nanning, Guangxi 530021, China (C.Z., D.H., B.W., S.W., Y.S., X.W.)
| | - Siyuan Li
- Department of Obstetrics, Qingdao Municipal Hospital, Qingdao, Shandong 266071, China (S.L.)
| | - Daolai Huang
- Guangxi Medical University, Nanning, Guangxi 530021, China (C.Z., D.H., B.W., S.W., Y.S., X.W.); Guangxi Key Laboratory of Enhanced Recovery After Surgery for Gastrointestinal Cancer, Nanning, Guangxi 530021, China (C.Z., D.H., B.W., S.W., Y.S., X.W.); Department of Gastrointestinal Gland Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, China (D.H., X.W.)
| | - Bo Wen
- Guangxi Medical University, Nanning, Guangxi 530021, China (C.Z., D.H., B.W., S.W., Y.S., X.W.); Guangxi Key Laboratory of Enhanced Recovery After Surgery for Gastrointestinal Cancer, Nanning, Guangxi 530021, China (C.Z., D.H., B.W., S.W., Y.S., X.W.)
| | - Shizhuang Wei
- Guangxi Medical University, Nanning, Guangxi 530021, China (C.Z., D.H., B.W., S.W., Y.S., X.W.); Guangxi Key Laboratory of Enhanced Recovery After Surgery for Gastrointestinal Cancer, Nanning, Guangxi 530021, China (C.Z., D.H., B.W., S.W., Y.S., X.W.)
| | - Yaodong Song
- Guangxi Medical University, Nanning, Guangxi 530021, China (C.Z., D.H., B.W., S.W., Y.S., X.W.); Guangxi Key Laboratory of Enhanced Recovery After Surgery for Gastrointestinal Cancer, Nanning, Guangxi 530021, China (C.Z., D.H., B.W., S.W., Y.S., X.W.)
| | - Xianghua Wu
- Guangxi Medical University, Nanning, Guangxi 530021, China (C.Z., D.H., B.W., S.W., Y.S., X.W.); Guangxi Key Laboratory of Enhanced Recovery After Surgery for Gastrointestinal Cancer, Nanning, Guangxi 530021, China (C.Z., D.H., B.W., S.W., Y.S., X.W.); Department of Gastrointestinal Gland Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, China (D.H., X.W.).
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Liu X, Yuan Y, Chen XL, Fang Z, Liu SY, Pu H, Li H. Radiomics from dual-energy CT-derived iodine maps for predicting lymph node metastases in patients with resectable rectal cancer. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2025; 33:553-564. [PMID: 40343881 DOI: 10.1177/08953996241313322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2025]
Abstract
BackgroundLymph node metastasis (LNM) is a poor prognostic predictor and is highly correlated with local recurrence in rectal cancer patients.ObjectiveTo investigate the value of radiomics from dual-energy CT-derived iodine maps for the preoperative prediction of LNM in rectal cancer patients.MethodsA total of 176 patients were enrolled in this study (training group, n = 123; validation group, n = 53). A radiomic signature was constructed via support vector machine (SVM) modeling. Seven models, including a clinical feature model (Model 1), an arterial model (Model 2), a venous model (Model 3), an arterial-venous model (Model 4), an arterial-clinical model (Model 5), a venous-clinical model (Model 6) and an arterial-venous-clinical model (Model 7), were established via logistic regression modeling. Diagnostic performance was assessed via receiver operating characteristic (ROC) curves.ResultsTumor location and carcinoembryonic antigen levels were used to construct Model 1 (training group, AUC [area under the ROC curve] = 0.721, 95% CI [confidence intervals], 0.630-0.813; validation group, AUC = 0.729, 95% CI, 0.593-0.865). Model 6 and Model 7 further improved the discriminatory performance in the training (AUC = 0.850 and 0.869, 95% CI, 0.782-0.919 and 0.807-0.932, respectively; p = 0.250) and validation groups (AUC = 0.780 and 0.716, 95% CI, 0.653-0.906 and 0.576-0.856, respectively; p = 0.115). Moreover, decision curve analysis revealed a greater net benefit with Model 6.ConclusionsThe combination of radiomic features based on dual-energy CT-derived iodine maps and clinical features provides better diagnostic performance for predicting LNM in rectal cancer patients.
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Affiliation(s)
- Xia Liu
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, Sichuan, China
| | - Yi Yuan
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, Sichuan, China
| | - Xiao-Li Chen
- Department of Radiology, Affiliated Cancer Hospital of Medical School, University of Electronic Science and Technology of China, Sichuan Cancer Hospital, Chengdu, China
| | - Zhu Fang
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, Sichuan, China
| | | | - Hong Pu
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, Sichuan, China
| | - Hang Li
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, Sichuan, China
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10
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Zhang W, Zhuang D, Wei W, Yang Y, Ma L, Du H, Jin A, He J, Li X. The 100 most-cited radiomics articles in cancer research: A bibliometric analysis. Clin Imaging 2025; 121:110442. [PMID: 40086035 DOI: 10.1016/j.clinimag.2025.110442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2024] [Revised: 02/15/2025] [Accepted: 03/06/2025] [Indexed: 03/16/2025]
Abstract
Radiomics, an advanced medical imaging analysis technique introduced by Professor Lambin in 2012, has quickly become a key area of medical research. To explore emerging trends in cancer-related radiomics, we conducted a bibliometric analysis of the 100 most-cited articles (T100) in this field. Data were collected from the Web of Science Core Collection on October 7, 2023, and the articles were ranked by citation count. We extracted data such as authors, journals, citation counts, and publication years and analyzed it using Microsoft Excel 2019 and R 4.4.2. CiteSpace was used to create co-occurrence and citation burst maps to show the relationships between authors, countries, institutions, and keywords. The analysis revealed that the T100 came from 81 countries, with the U.S. contributing the most (56 articles). Harvard University was the leading institution, and the journal Radiology had the highest citation count. Aerts Hugo JWL was the most influential author. The study highlights that "lung cancer" and "artificial intelligence" are emerging as major research hotspots, shaping the future of cancer radiomics.
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Affiliation(s)
- Wenhao Zhang
- Department of Psychiatry, Chaohu Hospital of Anhui Medical University, Hefei, Anhui, China; Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China; Department of Clinical Medical, First Clinical Medical College, Anhui Medical University, Hefei, China
| | - Dongmei Zhuang
- Suzhou Hospital of Anhui Medical University, Suzhou, Anhui, China
| | - Wenzhuo Wei
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
| | - Yuchen Yang
- Department of Clinical Medical, First Clinical Medical College, Anhui Medical University, Hefei, China
| | - Lijun Ma
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
| | - He Du
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
| | - Anran Jin
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
| | - Jingyi He
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
| | - Xiaoming Li
- Department of Psychiatry, Chaohu Hospital of Anhui Medical University, Hefei, Anhui, China; Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China.
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11
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Zhang L, Diao B, Fan Z, Zhan H. Radiomics for Differentiating Pancreatic Mucinous Cystic Neoplasm from Serous Cystic Neoplasm: Systematic Review and Meta-Analysis. Acad Radiol 2025; 32:2679-2688. [PMID: 39648097 DOI: 10.1016/j.acra.2024.11.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2024] [Revised: 11/17/2024] [Accepted: 11/17/2024] [Indexed: 12/10/2024]
Abstract
BACKGROUND As pancreatic cystic neoplasms (PCN) differ in current standard of care, and these treatments can affect quality of life to varying degrees, a definitive preoperative diagnosis must be reliable. Current diagnostic approaches, specifically traditional cross-sectional imaging techniques, face certain limitations. But radiomics has been shown to have high diagnostic accuracy across a range of diseases. Objective to conduct a comprehensive review of the literature on the use of radiomics to differentiate Mucinous Cystic Neoplasm (MCN) from Serous Cystic Neoplasm (SCN). METHODS This study was comprehensively searched in Pubmed, Scopus and Web of Science databases for meta-analysis of studies that used radiomics to distinguish MCN from SCN. Risk of bias was assessed using the diagnostic accuracy study quality assessment method and combined with sensitivity, specificity, diagnostic odds ratio, and summary receiver operating characteristic (SROC)curve analysis. RESULTS A total of 884 patients from 8 studies were included in this analysis, including 365 MCN and 519 SCN. The Meta-analysis found that radiomics identified MCN and SCN with high sensitivity and specificity, with combined sensitivity and specificity of 0.84(0.82-0.87) and 0.82(0.79-0.84). The positive likelihood ratio (PLR) and the negative likelihood ratio (NLR) are 5.61(3.72, 8.47) and 0.14(0.09-0.26). In addition, the area under the SROC curve (AUC) was drawn at 0.93. No significant risk of publication bias was detected through the funnel plot analysis. The performances of feature extraction from the volume of interest (VOI) or Using AI classifier in the radiomics models were superior to those of protocols employing region of interest (ROI) or absence of AI classifier. CONCLUSION This meta-analysis demonstrates that radiomics exhibits high sensitivity and specificity in distinguishing between MCN and SCN, and has the potential to become a reliable diagnostic tool for their identification.
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Affiliation(s)
- Longjia Zhang
- Division of Pancreatic Surgery, Department of General Surgery, Qilu Hospital, Shandong University, Jinan, Shandong Province, China (L.Z., B.D., Z.F., H.Z.)
| | - Boyu Diao
- Division of Pancreatic Surgery, Department of General Surgery, Qilu Hospital, Shandong University, Jinan, Shandong Province, China (L.Z., B.D., Z.F., H.Z.)
| | - Zhiyao Fan
- Division of Pancreatic Surgery, Department of General Surgery, Qilu Hospital, Shandong University, Jinan, Shandong Province, China (L.Z., B.D., Z.F., H.Z.)
| | - Hanxiang Zhan
- Division of Pancreatic Surgery, Department of General Surgery, Qilu Hospital, Shandong University, Jinan, Shandong Province, China (L.Z., B.D., Z.F., H.Z.).
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Lian T, Deng C, Feng Q. Patch-Based Texture Feature Extraction Towards Improved Clinical Task Performance. Bioengineering (Basel) 2025; 12:404. [PMID: 40281764 PMCID: PMC12024865 DOI: 10.3390/bioengineering12040404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2025] [Revised: 04/01/2025] [Accepted: 04/08/2025] [Indexed: 04/29/2025] Open
Abstract
Texture features can capture microstructural patterns and tissue heterogeneity, playing a pivotal role in medical image analysis. Compared to deep learning-based features, texture features offer superior interpretability in clinical applications. However, as conventional texture features focus strictly on voxel-level statistical information, they fail to account for critical spatial heterogeneity between small tissue volumes, which may hold significant importance. To overcome this limitation, we propose novel 3D patch-based texture features and develop a radiomics analysis framework to validate the efficacy of our proposed features. Specifically, multi-scale 3D patches were created to construct patch patterns via k-means clustering. The multi-resolution images were discretized based on labels of the patterns, and then texture features were extracted to quantify the spatial heterogeneity between patches. Twenty-five cross-combination models of five feature selection methods and five classifiers were constructed. Our methodology was evaluated using two independent MRI datasets. Specifically, 145 breast cancer patients were included for axillary lymph node metastasis prediction, and 63 cervical cancer patients were enrolled for histological subtype prediction. Experimental results demonstrated that the proposed 3D patch-based texture features achieved an AUC of 0.76 in the breast cancer lymph node metastasis prediction task and an AUC of 0.94 in cervical cancer histological subtype prediction, outperforming conventional texture features (0.74 and 0.83, respectively). Our proposed features have successfully captured multi-scale patch-level texture representations, which could enhance the application of imaging biomarkers in the precise prediction of cancers and personalized therapeutic interventions.
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Affiliation(s)
- Tao Lian
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China;
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China
| | - Chunyan Deng
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China;
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China;
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China
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Wu Y, Zhang W, Liang X, Zhang P, Zhang M, Jiang Y, Cui Y, Chen Y, Zhou W, Liang Q, Dai J, Zhang C, Xu J, Li J, Yu T, Zhang Z, Guo R. Habitat radiomics analysis for progression free survival and immune-related adverse reaction prediction in non-small cell lung cancer treated by immunotherapy. J Transl Med 2025; 23:393. [PMID: 40181378 PMCID: PMC11970015 DOI: 10.1186/s12967-024-06057-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2024] [Accepted: 12/25/2024] [Indexed: 04/05/2025] Open
Abstract
BACKGROUND Non-small cell lung cancer (NSCLC) is highly heterogeneous, leading to varied treatment responses and immune-related adverse reactions (irAEs) among patients. Habitat radiomics allows non-invasive quantitative assessment of intratumor heterogeneity (ITH). Therefore, our objective is to employ habitat radiomics techniques to develop a robust approach for predicting the efficacy of Immune checkpoint inhibitors (ICIs) and the likelihood of irAEs in advanced NSCLC patients. METHODS In this retrospective two center study, two independent cohorts of patients with NSCLC were used to develop (n = 248) and validate signatures (n = 95). After applying four kinds of machine learning algorithms to select the key preoperative CT radiomic features, we used clinical, radiomics and habitat radiomic features to develop the clinical signature, radiomics signature and habitat radiomic signature for ICIs prognostics and irAEs prediction. By combining habitat radiomic features with corresponding clinicopathologic information, the nomogram signature was constructed in the training cohort. Next, the internal validation cohort (n = 75) of patients, and the external validation cohort (n = 20) of patients treated with ICIs were included to evaluate the predictive value of the four signatures, and their predictive performance was assessed by the area under operating characteristic curve (AUC). RESULTS Our study introduces a radiomic nomogram model that integrates clinical and habitat radiomic features to identify patients who may benefit from ICIs or experience irAEs. The Radiomics Nomogram model exhibited superior predictive performance in the training, validation, and external validation sets, with AUCs of 0.923, 0.817, and 0.899, respectively. This model outperformed both the Whole-tumor Radiomics Signature model (AUCs of 0.870, 0.736, and 0.626) and the Habitat Signature model (AUCs of 0.900, 0.804, and 0.808). The radiomics model focusing on tumor sub-regional habitat showed better predictive performance than the model derived from the entire tumor. Decision Curve Analysis (DCA) and calibration curves confirmed the nomogram's effectiveness. CONCLUSION By leveraging machine learning to predict the outcomes of ICIs, we can move closer to achieving tailored ICIs for lung cancer. This advancement will assist physicians in selecting and managing subsequent treatment strategies, thereby facilitating clinical decision-making.
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Affiliation(s)
- Yuemin Wu
- Department of Oncology, First Affiliated Hospital, Nanjing Medical University, Nanjing, China
| | - Wei Zhang
- Department of Radiology, First Affiliated Hospital, Nanjing Medical University, Nanjing, China
| | - Xiao Liang
- Department of Oncology, First Affiliated Hospital, Nanjing Medical University, Nanjing, China
| | - Pengpeng Zhang
- Department of Lung Cancer Surgery, Tianjin Lung Cancer Institute, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Mengzhe Zhang
- Department of Lung Cancer Surgery, Tianjin Lung Cancer Institute, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Yuqin Jiang
- Department of Oncology, First Affiliated Hospital, Nanjing Medical University, Nanjing, China
| | - Yanan Cui
- Department of Radiology, First Affiliated Hospital, Nanjing Medical University, Nanjing, China
| | - Yi Chen
- Department of Oncology, Pukou Branch of Jiangsu People's Hospital, Nanjing Pukou District Central Hospital, Nanjing, China
| | - Wenxin Zhou
- Department of Oncology, First Affiliated Hospital, Nanjing Medical University, Nanjing, China
| | - Qi Liang
- Department of Oncology, First Affiliated Hospital, Nanjing Medical University, Nanjing, China
| | - Jiali Dai
- Department of Oncology, First Affiliated Hospital, Nanjing Medical University, Nanjing, China
| | - Chen Zhang
- Department of Oncology, First Affiliated Hospital, Nanjing Medical University, Nanjing, China
| | - Jiali Xu
- Department of Oncology, First Affiliated Hospital, Nanjing Medical University, Nanjing, China
| | - Jun Li
- Department of Oncology, First Affiliated Hospital, Nanjing Medical University, Nanjing, China
| | - Tongfu Yu
- Department of Radiology, First Affiliated Hospital, Nanjing Medical University, Nanjing, China
| | - Zhihong Zhang
- Department of Pathology, First Affiliated Hospital, Nanjing Medical University, Nanjing, China
| | - Renhua Guo
- Department of Radiology, First Affiliated Hospital, Nanjing Medical University, Nanjing, China.
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14
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Wang Y, Han Q, Wen B, Yang B, Zhang C, Song Y, Zhang L, Xian J. Development and validation of a prediction model for malignant sinonasal tumors based on MR radiomics and machine learning. Eur Radiol 2025; 35:2074-2083. [PMID: 39210161 DOI: 10.1007/s00330-024-11033-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 06/23/2024] [Accepted: 08/06/2024] [Indexed: 09/04/2024]
Abstract
OBJECTIVES This study aimed to utilize MR radiomics-based machine learning classifiers on a large-sample, multicenter dataset to develop an optimal model for predicting malignant sinonasal tumors and tumor-like lesions. METHODS This study included 1711 adult patients (875 benign and 836 malignant) with sinonasal tumors or tumor-like lesions from three institutions. Patients from institution 1 (n = 1367) constituted both the training and validation cohorts, while those from institution 2 and 3 (n = 158/186) made up the test cohorts. Manual segmentation of the region of interest of the tumor was performed on T1WI, T2WI, and contrast-enhanced T1WI (CE-T1WI). Data normalization, dimensional reductions, feature selection, and classifications were performed using ten machine-learning classifiers. Four fusion models, namely T1WI + T2WI, T1WI + CE-T1WI, T2WI + CE-T1WI, and T1WI + T2WI + CE-T1WI, were constructed using the top ten features with the highest contribution in feature selection in the optimal models of T1WI, T2WI, and CE-T1WI. The Delong test compared areas under the curve (AUC) between models. RESULTS The AUCs of training/validation/test1/test2 datasets for T1WI, T2WI, and CE-T1WI were 0.900/0.842/0.872/0.839, 0.876/0.789/0.842/0.863, and 0.899/0.824/0.831/0.707, respectively. The fusion model from T1WI + T2WI + CE-T1WI had the highest AUC. The AUCs of training/validation/test1/test2 datasets were 0.947/0.849/0.871/0.887. The T1WI + T2WI + CE-T1WI model demonstrated a significantly higher AUC than the T2WI + CE-T1WI model in both cohorts (p < 0.05) and outperformed the T2WI model in test 1 (p = 0.008) and the T1WI model in test 2 (p = 0.006). CONCLUSIONS This fusion model based on radiomics from T1WI + T2WI + CE-T1WI images and machine learning can improve the power in predicting malignant sinonasal tumors with high accuracy, resilience, and robustness. CLINICAL RELEVANCE STATEMENT Our study proposes a radiomics-based machine learning fusion model from T1- and T2-weighted images and contrast-enhanced T1-weighted images, which can non-invasively identify the nature of sinonasal tumors and improve the performance in predicting malignant sinonasal tumors. KEY POINTS Differentiating benign and malignant sinonasal tumors is difficult due to similar clinical presentations. A radiomics model from T1 + T2 + contrast-enhanced T1 images can identify the nature of sinonasal tumors. This model can help distinguish benign and malignant sinonasal tumors.
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Affiliation(s)
- Yuchen Wang
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Qinghe Han
- Department of Radiology, The Second Hospital of Jilin University, Changchun, China
| | - Baohong Wen
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bingbing Yang
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Chen Zhang
- MR Research Collaboration Team, Siemens Healthcare, Beijing, China
| | - Yang Song
- MR Research Collaboration Team, Siemens Healthcare, Beijing, China
| | - Luo Zhang
- Department of Otolaryngology-Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
- Beijing Laboratory of Allergic Diseases and Beijing Key Laboratory of Nasal Diseases, Beijing Institute of Otorhinolaryngology, Beijing, China.
- Research Unit of Diagnosis and Treatment of Chronic Nasal Diseases, Chinese Academy of Medical Sciences, Beijing, China.
- Department of Allergy, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
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15
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Huang Z, Huang W, Jiang L, Zheng Y, Pan Y, Yan C, Ye R, Weng S, Li Y. Decision Fusion Model for Predicting Microvascular Invasion in Hepatocellular Carcinoma Based on Multi-MR Habitat Imaging and Machine-Learning Classifiers. Acad Radiol 2025; 32:1971-1980. [PMID: 39472207 DOI: 10.1016/j.acra.2024.10.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2024] [Revised: 09/30/2024] [Accepted: 10/10/2024] [Indexed: 11/20/2024]
Abstract
RATIONALE AND OBJECTIVES Accurate prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is crucial for guiding treatment. This study evaluates and compares the performance of clinicoradiologic, traditional radiomics, deep-learning radiomics, feature fusion, and decision fusion models based on multi-region MR habitat imaging using six machine-learning classifiers. MATERIALS AND METHODS We retrospectively included 300 HCC patients. The intratumoral and peritumoral regions were segmented into distinct habitats, from which radiomics and deep-learning features were extracted using arterial phase MR images. To reduce feature dimensionality, we applied intra-class correlation coefficient (ICC) analysis, Pearson correlation coefficient (PCC) filtering, and recursive feature elimination (RFE). Based on the selected optimal features, prediction models were constructed using decision tree (DT), K-nearest neighbors (KNN), logistic regression (LR), random forest (RF), support vector machine (SVM), and XGBoost (XGB) classifiers. Additionally, fusion models were developed utilizing both feature fusion and decision fusion strategies. The performance of these models was validated using the area under the receiver operating characteristic curve (ROC AUC), calibration curves, and decision curve analysis. RESULTS The decision fusion model (VOI-Peri10-1) using LR and integrating clinicoradiologic, radiomics, and deep-learning features achieved the highest AUC of 0.808 (95% confidence interval [CI]: 0.807-0.912) in the test cohort, with good calibration (Hosmer-Lemeshow test, P > 0.050) and clinical net benefit. CONCLUSION The LR-based decision fusion model integrating clinicoradiologic, radiomics, and deep-learning features shows promise for preoperative prediction of MVI in HCC, aiding in patient outcome predictions and personalized treatment planning.
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Affiliation(s)
- Zhenhuan Huang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., W.H., L.J., Y.Z., Y.P., C.Y., R.Y., Y.L.); Department of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian 364000, China (Z.H.)
| | - Wanrong Huang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., W.H., L.J., Y.Z., Y.P., C.Y., R.Y., Y.L.)
| | - Lu Jiang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., W.H., L.J., Y.Z., Y.P., C.Y., R.Y., Y.L.)
| | - Yao Zheng
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., W.H., L.J., Y.Z., Y.P., C.Y., R.Y., Y.L.)
| | - Yifan Pan
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., W.H., L.J., Y.Z., Y.P., C.Y., R.Y., Y.L.)
| | - Chuan Yan
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., W.H., L.J., Y.Z., Y.P., C.Y., R.Y., Y.L.)
| | - Rongping Ye
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., W.H., L.J., Y.Z., Y.P., C.Y., R.Y., Y.L.)
| | - Shuping Weng
- Department of Radiology, Fujian Maternity and Child Health Hospital, Fujian Medical University, Fuzhou, Fujian 350001, China (S.W.)
| | - Yueming Li
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., W.H., L.J., Y.Z., Y.P., C.Y., R.Y., Y.L.); Department of Radiology, National Regional Medical Center, Binhai Campus of The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian 350212, China (Y.L.); Key Laboratory of Radiation Biology of Fujian higher education institutions, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian 350005, China (Y.L.).
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Wu LX, Ding N, Ji YD, Zhang YC, Li MJ, Shen JC, Hu HT, Jin L, Yin SN. Habitat Analysis in Tumor Imaging: Advancing Precision Medicine Through Radiomic Subregion Segmentation. Cancer Manag Res 2025; 17:731-741. [PMID: 40190416 PMCID: PMC11971994 DOI: 10.2147/cmar.s511796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2024] [Accepted: 03/19/2025] [Indexed: 04/09/2025] Open
Abstract
Radiomics received a lot of attention because of its potential to provide personalized medicine in a non-invasive manner, usually focusing on the analysis of the entire lesion. A new method called habitat can identify subregional phenotypic changes within the lesion, thereby improving the ability to distinguish heterogeneity. The clustering method can be applied to multiple measurement parameters to separate different tumor habitats by segmentation. A data-driven repeatable voxel clustering method to identify subregions reflecting live tumors will be valuable for clinical diagnosis and further treatment. In this review, we aim to briefly summarize the widely used cluster analysis algorithms in subregion segmentation and the application of habitat analysis in tumor imaging. By analyzing many literatures, the commonly used K-means algorithm and other algorithms such as hierarchical clustering and consensus clustering are summarized. By identifying intratumoral heterogeneity, the key findings of habitat analysis in oncology are described, such as tumor differentiation, grading, and gene expression status. The latest progress and innovations in predicting tumor therapeutic effects and prognosis using habitat analysis are reviewed, including multimodal imaging data fusion, integration with artificial intelligence technologies, and non-invasive diagnostic methods. The limitations and challenges of habitat analysis in tumor imaging are also discussed, such as dependence on image quality and imaging techniques, insufficient automation and standardization, difficulties in biological interpretation, and lack of clinical validation. Finally, future directions for increasing the level of automation and standardization of habitat analysis to improve its accuracy and efficiency and reduce reliance on expert intervention are proposed. Habitat analysis represents a significant advancement in radiomics, offering a nuanced understanding of tumor heterogeneity. By leveraging sophisticated clustering algorithms and integrating multimodal imaging data, habitat analysis has the potential to transform clinical decision-making, enabling more precise diagnostics and personalized treatment strategies, ultimately advancing the field of precision medicine.
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Affiliation(s)
- Ling Xiao Wu
- Department of Medical Imaging, Suzhou Ninth People’s Hospital, Wujiang, Suzhou, Jiangsu, People’s Republic of China
| | - Ning Ding
- Department of Medical Imaging, Suzhou Ninth People’s Hospital, Wujiang, Suzhou, Jiangsu, People’s Republic of China
| | - Yi Ding Ji
- Department of Medical Imaging, Suzhou Ninth People’s Hospital, Wujiang, Suzhou, Jiangsu, People’s Republic of China
| | - Yi Chi Zhang
- Department of Medical Imaging, Suzhou Ninth People’s Hospital, Wujiang, Suzhou, Jiangsu, People’s Republic of China
| | - Meng Juan Li
- Department of Medical Imaging, Suzhou Ninth People’s Hospital, Wujiang, Suzhou, Jiangsu, People’s Republic of China
| | - Jia Cheng Shen
- Department of Medical Imaging, Suzhou Ninth People’s Hospital, Wujiang, Suzhou, Jiangsu, People’s Republic of China
| | - Hai Tao Hu
- Department of Medical Imaging, Suzhou Ninth People’s Hospital, Wujiang, Suzhou, Jiangsu, People’s Republic of China
| | - Long Jin
- Department of Medical Imaging, Suzhou Ninth People’s Hospital, Wujiang, Suzhou, Jiangsu, People’s Republic of China
| | - Sheng Nan Yin
- Department of Medical Imaging, Suzhou Ninth People’s Hospital, Wujiang, Suzhou, Jiangsu, People’s Republic of China
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Chen Y, Fan Z, Luo Z, Kang X, Wan R, Li F, Lin W, Han Z, Qi B, Lin J, Sun Y, Huang J, Xu Y, Chen S. Impacts of Nutlin-3a and exercise on murine double minute 2-enriched glioma treatment. Neural Regen Res 2025; 20:1135-1152. [PMID: 38989952 PMCID: PMC11438351 DOI: 10.4103/nrr.nrr-d-23-00875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 12/21/2023] [Indexed: 07/12/2024] Open
Abstract
JOURNAL/nrgr/04.03/01300535-202504000-00029/figure1/v/2024-07-06T104127Z/r/image-tiff Recent research has demonstrated the impact of physical activity on the prognosis of glioma patients, with evidence suggesting exercise may reduce mortality risks and aid neural regeneration. The role of the small ubiquitin-like modifier (SUMO) protein, especially post-exercise, in cancer progression, is gaining attention, as are the potential anti-cancer effects of SUMOylation. We used machine learning to create the exercise and SUMO-related gene signature (ESLRS). This signature shows how physical activity might help improve the outlook for low-grade glioma and other cancers. We demonstrated the prognostic and immunotherapeutic significance of ESLRS markers, specifically highlighting how murine double minute 2 (MDM2), a component of the ESLRS, can be targeted by nutlin-3. This underscores the intricate relationship between natural compounds such as nutlin-3 and immune regulation. Using comprehensive CRISPR screening, we validated the effects of specific ESLRS genes on low-grade glioma progression. We also revealed insights into the effectiveness of Nutlin-3a as a potent MDM2 inhibitor through molecular docking and dynamic simulation. Nutlin-3a inhibited glioma cell proliferation and activated the p53 pathway. Its efficacy decreased with MDM2 overexpression, and this was reversed by Nutlin-3a or exercise. Experiments using a low-grade glioma mouse model highlighted the effect of physical activity on oxidative stress and molecular pathway regulation. Notably, both physical exercise and Nutlin-3a administration improved physical function in mice bearing tumors derived from MDM2-overexpressing cells. These results suggest the potential for Nutlin-3a, an MDM2 inhibitor, with physical exercise as a therapeutic approach for glioma management. Our research also supports the use of natural products for therapy and sheds light on the interaction of exercise, natural products, and immune regulation in cancer treatment.
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Affiliation(s)
- Yisheng Chen
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhongcheng Fan
- Department of Orthopedic Surgery, Hainan Province Clinical Medical Center, Haikou Affiliated Hospital of Central South University Xiangya School of Medicine, Haikou, Hainan Province, China
| | - Zhiwen Luo
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Xueran Kang
- Department of Basic Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Renwen Wan
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Fangqi Li
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Weiwei Lin
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Zhihua Han
- Department of Orthopedics, Shanghai General Hospital, School of Medicine Shanghai Jiao Tong University, Shanghai, China
| | - Beijie Qi
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Jinrong Lin
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Yaying Sun
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Jiebin Huang
- Department of Infectious Diseases, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China
| | - Yuzhen Xu
- Department of Rehabilitation, The Second Affiliated Hospital of Shandong First Medical University, Taian, Shandong Province, China
| | - Shiyi Chen
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
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18
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Yean AW, Leong E, King OS, Mohamad Z. Breast cancer treatment modalities, treatment delays, and survival in Brunei Darussalam. BMC Cancer 2025; 25:510. [PMID: 40114099 PMCID: PMC11924827 DOI: 10.1186/s12885-025-13861-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2024] [Accepted: 03/04/2025] [Indexed: 03/22/2025] Open
Abstract
INTRODUCTION Breast cancer remains a leading cause of cancer-related mortality globally. This study aims to examine the demographic variables and effects of different treatment modalities and treatment delays on overall and relative survival rates of breast cancer patients in Brunei Darussalam. METHODS This retrospective study analysed data from the Brunei Darussalam Cancer Registry on breast cancer cases diagnosed and treated between 2013 and 2022. Statistical analyses included descriptive statistics to characterise the study population, Kaplan-Meier estimates to compare survival curves of different groups, Log rank tests to determine significant differences in survival rates among groups, and Cox Proportional Hazard (PH) models to estimate hazard ratios (HRs) and identify predictors of survival outcomes. Overall survival (OS) and relative survival (RS) rates were calculated. RESULTS Out of the 431 women treated for breast cancer, the majority were diagnosed at the regional stage (45.7%), with 39.0% at the localised stage. Over half (55.4%) of the diagnoses occurred in women aged 40 to 59, while about a quarter (25.5%) were in the 60-69 age group. Surgery was the most common first-line treatment modality (55.9%), with a median time to treatment of 37 days, followed by chemotherapy (30.6%). More than half of the patients (62.9%) were treated within 60 days of diagnosis. Treatment varied by age and cancer stage, with younger patients more likely to undergo surgery and older patients more likely to receive chemotherapy or hormonal therapy. Survival rates were high for patients receiving only surgery (5-year RS: 98.7%, OS: 92.3%), and significant survival differences were found for cancer stage and treatment delay, with a HR of 2.5 for delays over 60 days. Multivariate analysis showed that patients with distant stage cancer had a significantly higher risk of death (HR = 15.3) compared to localised stage. CONCLUSION This study highlights the impact of treatment modalities and delays on breast cancer survival in Brunei Darussalam, emphasising the need for timely treatment to improve survival rates. Our findings suggest that ensuring breast cancer treatment initiation within two months post-diagnosis may enhance patient outcomes, supporting potential policy targets for timely access to care.
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Affiliation(s)
- Ang Woan Yean
- Faculty of Science, Universiti Brunei Darussalam, Jln Tungku Link, Bandar Seri Begawan, Brunei Darussalam
| | - Elvynna Leong
- Faculty of Science, Universiti Brunei Darussalam, Jln Tungku Link, Bandar Seri Begawan, Brunei Darussalam.
| | - Ong Sok King
- PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam, Jln Tungku Link, Bandar Seri Begawan, Brunei Darussalam
- Department of Policy and Planning, Ministry of Health, Bandar Seri Begawan, Brunei Darussalam
| | - Zulkhairi Mohamad
- The Brunei Cancer Centre, Jerudong Park Medical Centre, Jerudong, Bandar Seri Begawan, Brunei Darussalam
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Wang SR, Shen YT, Huang B, Xu HX. Ultrasound-based radiogenomics: status, applications, and future direction. Ultrasonography 2025; 44:95-111. [PMID: 39935290 PMCID: PMC11938802 DOI: 10.14366/usg.24152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2024] [Accepted: 12/12/2024] [Indexed: 02/13/2025] Open
Abstract
Radiogenomics, an extension of radiomics, explores the relationship between imaging features and underlying gene expression patterns. This field is instrumental in providing reliable imaging surrogates, thus potentially representing an alternative to genetic testing. The rapidly growing area of radiogenomics that utilizes ultrasound (US) imaging seeks to elucidate the connections between US image characteristics and genomic data. In this review, the authors outline the radiogenomics workflow and summarize the applications of US-based radiogenomics. These include the prediction of gene variations, molecular subtypes, and other biological characteristics, as well as the exploration of the relationships between US phenotypes and cancer gene profiles. Although the field faces various challenges, US-based radiogenomics offers promising prospects and avenues for future research.
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Affiliation(s)
- Si-Rui Wang
- Department of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yu-Ting Shen
- Department of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Bin Huang
- Department of Ultrasound, Zhejiang Hospital, Hangzhou, China
| | - Hui-Xiong Xu
- Department of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai, China
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20
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Yadav A, Welland S, Hoffman JM, Hyun J Kim G, Brown MS, Prosper AE, Aberle DR, McNitt-Gray MF, Hsu W. A comparative analysis of image harmonization techniques in mitigating differences in CT acquisition and reconstruction. Phys Med Biol 2025; 70:055015. [PMID: 39823753 PMCID: PMC11866762 DOI: 10.1088/1361-6560/adabad] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2024] [Revised: 11/28/2024] [Accepted: 01/17/2025] [Indexed: 01/20/2025]
Abstract
Objective. The study aims to systematically characterize the effect of CT parameter variations on images and lung radiomic and deep features, and to evaluate the ability of different image harmonization methods to mitigate the observed variations.Approach. A retrospective in-house sinogram dataset of 100 low-dose chest CT scans was reconstructed by varying radiation dose (100%, 25%, 10%) and reconstruction kernels (smooth, medium, sharp). A set of image processing, convolutional neural network (CNNs), and generative adversarial network-based (GANs) methods were trained to harmonize all image conditions to a reference condition (100% dose, medium kernel). Harmonized scans were evaluated for image similarity using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS), and for the reproducibility of radiomic and deep features using concordance correlation coefficient (CCC).Main Results. CNNs consistently yielded higher image similarity metrics amongst others; for Sharp/10%, which exhibited the poorest visual similarity, PSNR increased from a mean ± CI of 17.763 ± 0.492 to 31.925 ± 0.571, SSIM from 0.219 ± 0.009 to 0.754 ± 0.017, and LPIPS decreased from 0.490 ± 0.005 to 0.275 ± 0.016. Texture-based radiomic features exhibited a greater degree of variability across conditions, i.e. a CCC of 0.500 ± 0.332, compared to intensity-based features (0.972 ± 0.045). GANs achieved the highest CCC (0.969 ± 0.009 for radiomic and 0.841 ± 0.070 for deep features) amongst others. CNNs are suitable if downstream applications necessitate visual interpretation of images, whereas GANs are better alternatives for generating reproducible quantitative image features needed for machine learning applications.Significance. Understanding the efficacy of harmonization in addressing multi-parameter variability is crucial for optimizing diagnostic accuracy and a critical step toward building generalizable models suitable for clinical use.
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Affiliation(s)
- Anil Yadav
- Department of Bioengineering, Samueli School of Engineering, University of California, Los Angeles, CA 90095, United States of America
- Medical & Imaging Informatics Group, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, United States of America
| | - Spencer Welland
- Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, United States of America
| | - John M Hoffman
- Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, United States of America
| | - Grace Hyun J Kim
- Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, United States of America
| | - Matthew S Brown
- Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, United States of America
| | - Ashley E Prosper
- Medical & Imaging Informatics Group, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, United States of America
| | - Denise R Aberle
- Department of Bioengineering, Samueli School of Engineering, University of California, Los Angeles, CA 90095, United States of America
- Medical & Imaging Informatics Group, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, United States of America
| | - Michael F McNitt-Gray
- Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, United States of America
| | - William Hsu
- Department of Bioengineering, Samueli School of Engineering, University of California, Los Angeles, CA 90095, United States of America
- Medical & Imaging Informatics Group, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, United States of America
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Huang Z, Pan Y, Huang W, Pan F, Wang H, Yan C, Ye R, Weng S, Cai J, Li Y. Predicting Microvascular Invasion and Early Recurrence in Hepatocellular Carcinoma Using DeepLab V3+ Segmentation of Multiregional MR Habitat Images. Acad Radiol 2025:S1076-6332(25)00109-6. [PMID: 40011096 DOI: 10.1016/j.acra.2025.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2025] [Revised: 02/05/2025] [Accepted: 02/05/2025] [Indexed: 02/28/2025]
Abstract
RATIONALE AND OBJECTIVES Accurate identification of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is crucial for treatment and prognosis. Single-modality and feature fusion models using manual segmentation fail to provide insights into MVI. This study aims to develop a DeepLab V3+ model for automated segmentation of HCC magnetic resonance (MR) images and a decision fusion model to predict MVI and early recurrence (ER). MATERIALS AND METHODS This retrospective study included 209 HCC patients (146 in the training and 63 in the test cohorts). The performance of DeepLab V3+ for HCC MR image segmentation was evaluated using Dice Loss and F1 score. Intraclass correlation coefficients (ICCs) assessed feature extraction reliability. Spearman's correlation analyzed the relationship between tumor volumes from automated and manual segmentation, with agreement evaluated using Bland-Altman plots. Model performance was assessed using the area under the receiver operating characteristic curve (ROC AUC), calibration curves, and decision curve analysis. A nomogram predicted ER of HCC after surgery, with Kaplan-Meier analysis for 2-year recurrence-free survival (RFS). RESULTS The DeepLab V3+ model demonstrated high segmentation accuracy, with strong agreement in feature extraction (ICC: 0.802-0.999). The decision fusion model achieved AUCs of 0.968 and 0.878 for MVI prediction, and the nomogram for predicting ER yielded AUCs of 0.782 and 0.690 in the training and test cohorts, respectively, with significant RFS differences between the risk groups. CONCLUSION The DeepLab V3+ model accurately segmented HCC. The decision fusion model significantly improved MVI prediction, and the nomogram offered valuable insights into recurrence risk for clinical decision-making. AVAILABILITY OF DATA AND MATERIALS The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
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Affiliation(s)
- Zhenhuan Huang
- Department of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian 364000, China (Z.H.); Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., Y.P., W.H., F.P., H.W., C.Y., R.Y., Y.L.)
| | - Yifan Pan
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., Y.P., W.H., F.P., H.W., C.Y., R.Y., Y.L.)
| | - Wanrong Huang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., Y.P., W.H., F.P., H.W., C.Y., R.Y., Y.L.)
| | - Feng Pan
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., Y.P., W.H., F.P., H.W., C.Y., R.Y., Y.L.)
| | - Huifang Wang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., Y.P., W.H., F.P., H.W., C.Y., R.Y., Y.L.)
| | - Chuan Yan
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., Y.P., W.H., F.P., H.W., C.Y., R.Y., Y.L.)
| | - Rongping Ye
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., Y.P., W.H., F.P., H.W., C.Y., R.Y., Y.L.)
| | - Shuping Weng
- Department of Radiology, Fujian Maternity and Child Health Hospital, Fujian Medical University, Fuzhou, Fujian 350001, China (S.W.)
| | - Jingyi Cai
- School of Medical Imaging, Fujian Medical University, Fuzhou, Fujian 350001, China (J.C.)
| | - Yueming Li
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., Y.P., W.H., F.P., H.W., C.Y., R.Y., Y.L.); Department of Radiology, National Regional Medical Center, Binhai Campus of The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian 350212, China (Y.L.); Key Laboratory of Radiation Biology of Fujian higher education institutions, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian 350005, China (Y.L.).
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22
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Gennaro N, Soliman M, Borhani AA, Kelahan L, Savas H, Avery R, Subedi K, Trabzonlu TA, Krumpelman C, Yaghmai V, Chae Y, Lorch J, Mahalingam D, Mulcahy M, Benson A, Bagci U, Velichko YS. Delta Radiomics and Tumor Size: A New Predictive Radiomics Model for Chemotherapy Response in Liver Metastases from Breast and Colorectal Cancer. Tomography 2025; 11:20. [PMID: 40137560 PMCID: PMC11945686 DOI: 10.3390/tomography11030020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2024] [Revised: 02/14/2025] [Accepted: 02/18/2025] [Indexed: 03/29/2025] Open
Abstract
Background/Objectives: Radiomic features exhibit a correlation with tumor size on pretreatment images. However, on post-treatment images, this association is influenced by treatment efficacy and varies between responders and non-responders. This study introduces a novel model, called baseline-referenced Delta radiomics, which integrates the association between radiomic features and tumor size into Delta radiomics to predict chemotherapy response in liver metastases from breast cancer (BC) and colorectal cancer (CRC). Materials and Methods: A retrospective study analyzed contrast-enhanced computed tomography (CT) scans of 83 BC patients and 84 CRC patients. Among these, 57 BC patients with 106 liver lesions and 37 CRC patients with 109 lesions underwent post-treatment imaging after systemic chemotherapy. Radiomic features were extracted from up to three lesions per patient following manual segmentation. Tumor response was assessed by measuring the longest diameter and classified according to RECIST 1.1 criteria as progressive disease (PD), partial response (PR), or stable disease (SD). Classification models were developed to predict chemotherapy response using pretreatment data only, Delta radiomics, and baseline-referenced Delta radiomics. Model performance was evaluated using confusion matrix metrics. Results: Baseline-referenced Delta radiomics performed comparably or better than established radiomics models in predicting tumor response in chemotherapy-treated patients with liver metastases. The sensitivity, specificity, and balanced accuracy in predicting response ranged from 0.66 to 0.97, 0.81 to 0.97, and 80% to 90%, respectively. Conclusions: By integrating the relationship between radiomic features and tumor size into Delta radiomics, baseline-referenced Delta radiomics offers a promising approach for predicting chemotherapy response in liver metastases from breast and colorectal cancer.
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Affiliation(s)
- Nicolò Gennaro
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (N.G.); (M.S.); (A.A.B.); (L.K.); (H.S.); (R.A.); (K.S.); (T.A.T.); (C.K.); (U.B.)
| | - Moataz Soliman
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (N.G.); (M.S.); (A.A.B.); (L.K.); (H.S.); (R.A.); (K.S.); (T.A.T.); (C.K.); (U.B.)
| | - Amir A. Borhani
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (N.G.); (M.S.); (A.A.B.); (L.K.); (H.S.); (R.A.); (K.S.); (T.A.T.); (C.K.); (U.B.)
| | - Linda Kelahan
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (N.G.); (M.S.); (A.A.B.); (L.K.); (H.S.); (R.A.); (K.S.); (T.A.T.); (C.K.); (U.B.)
| | - Hatice Savas
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (N.G.); (M.S.); (A.A.B.); (L.K.); (H.S.); (R.A.); (K.S.); (T.A.T.); (C.K.); (U.B.)
| | - Ryan Avery
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (N.G.); (M.S.); (A.A.B.); (L.K.); (H.S.); (R.A.); (K.S.); (T.A.T.); (C.K.); (U.B.)
| | - Kamal Subedi
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (N.G.); (M.S.); (A.A.B.); (L.K.); (H.S.); (R.A.); (K.S.); (T.A.T.); (C.K.); (U.B.)
| | - Tugce A. Trabzonlu
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (N.G.); (M.S.); (A.A.B.); (L.K.); (H.S.); (R.A.); (K.S.); (T.A.T.); (C.K.); (U.B.)
| | - Chase Krumpelman
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (N.G.); (M.S.); (A.A.B.); (L.K.); (H.S.); (R.A.); (K.S.); (T.A.T.); (C.K.); (U.B.)
| | - Vahid Yaghmai
- Department of Radiological Sciences, University of California Irvine, Irvine, CA 92868, USA;
| | - Young Chae
- Department of Medicine, Division of Hematology/Oncology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (Y.C.); (J.L.); (D.M.); (M.M.); (A.B.)
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA
| | - Jochen Lorch
- Department of Medicine, Division of Hematology/Oncology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (Y.C.); (J.L.); (D.M.); (M.M.); (A.B.)
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA
| | - Devalingam Mahalingam
- Department of Medicine, Division of Hematology/Oncology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (Y.C.); (J.L.); (D.M.); (M.M.); (A.B.)
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA
| | - Mary Mulcahy
- Department of Medicine, Division of Hematology/Oncology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (Y.C.); (J.L.); (D.M.); (M.M.); (A.B.)
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA
| | - Al Benson
- Department of Medicine, Division of Hematology/Oncology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (Y.C.); (J.L.); (D.M.); (M.M.); (A.B.)
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA
| | - Ulas Bagci
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (N.G.); (M.S.); (A.A.B.); (L.K.); (H.S.); (R.A.); (K.S.); (T.A.T.); (C.K.); (U.B.)
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA
| | - Yuri S. Velichko
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (N.G.); (M.S.); (A.A.B.); (L.K.); (H.S.); (R.A.); (K.S.); (T.A.T.); (C.K.); (U.B.)
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA
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Sui C, Chen K, Ding E, Tan R, Li Y, Shen J, Xu W, Li X. 18F-FDG PET/CT-based intratumoral and peritumoral radiomics combining ensemble learning for prognosis prediction in hepatocellular carcinoma: a multi-center study. BMC Cancer 2025; 25:300. [PMID: 39972270 PMCID: PMC11841186 DOI: 10.1186/s12885-025-13649-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Accepted: 02/05/2025] [Indexed: 02/21/2025] Open
Abstract
BACKGROUND Radiomic models combining intratumoral with peritumoral features are potentially beneficial to enhance the predictive performance. This study aimed to identify the optimal 18F-FDG PET/CT-derived radiomic models for prediction of prognosis in hepatocellular carcinoma (HCC). METHODS A total of 135 HCC patients from two institutions were retrospectively included. Four peritumoral regions were defined by dilating tumor region with thicknesses of 2 mm, 4 mm, 6 mm, and 8 mm, respectively. Based on segmentation of intratumoral, peritumoral and integrated volume of interest (VOI), corresponding radiomic features were extracted respectively. After feature selection, a total of 15 intratumoral radiomic models were constructed based on five ensemble learning algorithms and radiomic features from three image modalities. Then, the optimal combination of ensemble learning algorithms and image modality in the intratumoral models was selected to develop subsequent peritumoral radiomic models and integrated radiomic models. Finally, a nomogram was developed incorporating the optimal radiomic model with clinical independent predictors to achieve an intuitive representation of the prediction model. RESULTS Among the intratumoral radiomic models, the one which combined PET/CT-based radiomic features with SVM classifier outperformed other models. With the addition of peritumoral information, the integrated model based on an integration of intratumoral and 2 mm-peritumoral VOI, was finally approved as the optimal radiomic model with a mean AUC of 0.831 in the internal validation, and a highest AUC of 0.839 (95%CI:0.718-0.960) in the external test. Furthermore, a nomogram incorporating the optimal radiomic model with HBV infection and TNM status, was able to predict the prognosis for HCC with an AUC of 0.889 (95%CI: 0.799-0.979). CONCLUSIONS The integrated intratumoral and peritumoral radiomic model, especially for a 2 mm peritumoral region, was verified as the optimal radiomic model to predict the overall survival of HCC. Furthermore, combination of integrated radiomic model with significant clinical parameter contributed to further enhance the prediction efficacy. TRIAL REGISTRATION This study was a retrospective study, so it was free from registration.
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Affiliation(s)
- Chunxiao Sui
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China
- Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Kun Chen
- Hangzhou Institute of Medicine (HIM), Zhejiang Cancer Hospital, Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
| | - Enci Ding
- Department of Nuclear Medicine, Tianjin First Central Hospital, Tianjin, 300192, China
| | - Rui Tan
- Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
| | - Yue Li
- Department of Nuclear Medicine, Tianjin First Central Hospital, Tianjin, 300192, China
| | - Jie Shen
- Department of Nuclear Medicine, Tianjin First Central Hospital, Tianjin, 300192, China.
| | - Wengui Xu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China.
- Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China.
| | - Xiaofeng Li
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China.
- Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China.
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Jing F, Zhang X, Liu Y, Chen X, Zhao J, Zhao X, Chen X, Yuan H, Dai M, Wang N, Zhang Z, Zhang J. Baseline 18F-FDG PET/CT radiomics for prognosis prediction in diffuse large B cell lymphoma with extranodal involvement. Clin Transl Oncol 2025; 27:727-735. [PMID: 39083140 DOI: 10.1007/s12094-024-03633-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Accepted: 07/19/2024] [Indexed: 02/01/2025]
Abstract
PURPOSE The objective of this investigation is to explore the capability of baseline 18F-FDG PET/CT radiomics to predict the prognosis of diffuse large B-cell lymphoma (DLBCL) with extranodal involvement (ENI). METHODS 126 patients diagnosed with DLBCL with ENI were included in the cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression was utilized to refine the optimum subset from the 1328 features. Cox regression analyses were employed to discern significant clinical variables and conventional PET parameters, which were then employed with radiomics score to develop combined model for predicting both progression-free survival (PFS) and overall survival (OS). The fitness and the predictive capability of the models were assessed via the Akaike information criterion (AIC) and concordance index (C-index). RESULTS 62 patients experienced disease recurrence or progression and 28 patients ultimately died. The combined model exhibited a lower AIC value compared to the radiomics model and SDmax/clinical variables for both PFS (507.101 vs. 510.658 vs. 525.506) and OS (215.667 vs. 230.556 vs. 219.313), respectively. The C-indices of the combined model, radiomics model, and SDmax/clinical variables were 0.724, 0.704, and 0.615 for PFS, and 0.842, 0.744, and 0.792 for OS, respectively. Kaplan--Meier curves showed significantly higher rates of relapse and mortality among patients classified as high-risk compared to those classified as low-risk (all P < 0.05). CONCLUSIONS The combined model of clinical variables, conventional PET parameters, and baseline PET/CT radiomics features demonstrates a higher accuracy in predicting the prognosis of DLBCL with ENI.
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Affiliation(s)
- Fenglian Jing
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
| | - Xinchao Zhang
- Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, 050000, Hebei, China
| | - Yunuan Liu
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
| | - Xiaolin Chen
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
| | - Jianqiang Zhao
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
| | - Xinming Zhao
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China.
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, 050011, Hebei, China.
| | - Xiaoshan Chen
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
| | - Huiqing Yuan
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
| | - Meng Dai
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
| | - Na Wang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
| | - Zhaoqi Zhang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
| | - Jingmian Zhang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
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Li C, Tan J, Li H, Lei Y, Yang G, Zhang C, Song Y, Wu Y, Bi G, Bi Q. The value of multiparametric MRI-based habitat imaging for differentiating uterine sarcomas from atypical leiomyomas: a multicentre study. Abdom Radiol (NY) 2025; 50:995-1008. [PMID: 39183205 DOI: 10.1007/s00261-024-04539-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 08/14/2024] [Accepted: 08/15/2024] [Indexed: 08/27/2024]
Abstract
PURPOSE To explore the feasibility of multiparametric MRI-based habitat imaging for distinguishing uterine sarcoma (US) from atypical leiomyoma (ALM). METHODS This retrospective study included the clinical and preoperative MRI data of 69 patients with US and 225 patients with ALM from three hospitals. At both the individual and cohort levels, the K-means and Gaussian mixture model (GMM) algorithms were utilized to perform habitat imaging on MR images, respectively. Specifically, T2-weighted images (T2WI) and contrast-enhanced T1-weighted images (CE-T1WI) were clustered to generate structural habitats, while apparent diffusion coefficient (ADC) maps and CE-T1WI were clustered to create functional habitats. Parameters of each habitat subregion were extracted to construct distinct habitat models. The integrated models were constructed by combining habitat and clinical independent predictors. Model performance was assessed using the area under the curve (AUC). RESULTS Abnormal vaginal bleeding, lactate dehydrogenase (LDH), and white blood cell (WBC) counts can serve as clinical independent predictors of US. The GMM-based functional habitat model at the cohort level had the highest mean AUC (0.766) in both the training and validation cohorts, followed by the GMM-based structural habitat model at the cohort level (AUC = 0.760). Within the integrated models, the K-means functional habitat model based on the cohort level achieved the highest mean AUC (0.905) in both the training and validation cohorts. CONCLUSION Habitat imaging based on multiparametric MRI has the potential to distinguish US from ALM. The combination of clinical independent predictors with the habitat models can effectively improve the performance.
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Affiliation(s)
- Chenrong Li
- Medical school, Kunming University of Science and Technology, The First People's Hospital of Yunnan Province, Kunming, 650500, Yunnan, China
| | - Jing Tan
- Department of Radiology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University,Peking University Cancer Hospital Yunnan, Kunming, 650118, Yunnan, China
| | - Haiyan Li
- Department of MRI, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, No. 157 Jinbi Road, Kunming, 650032, Yunnan, China
| | - Ying Lei
- Department of Radiology, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009, Jiangsu, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200241, China
| | - Chengxiu Zhang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200241, China
| | - Yang Song
- MR Research Collaboration, Siemens Healthineers, Shanghai, 201318, China
| | - Yunzhu Wu
- School of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Guoli Bi
- Department of MRI, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, No. 157 Jinbi Road, Kunming, 650032, Yunnan, China
| | - Qiu Bi
- Department of MRI, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, No. 157 Jinbi Road, Kunming, 650032, Yunnan, China.
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Zhou Y, Zhou XY, Xu YC, Ma XL, Tian R. Radiomics based on 18F-FDG PET for predicting treatment response and prognosis in newly diagnosed diffuse large B-cell lymphoma patients: do lesion selection and segmentation methods matter? Quant Imaging Med Surg 2025; 15:103-120. [PMID: 39839002 PMCID: PMC11744140 DOI: 10.21037/qims-24-585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 11/05/2024] [Indexed: 01/23/2025]
Abstract
Background Radiomics features extracted from baseline 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) scans have shown promising results in predicting the treatment response and outcome of diffuse large B-cell lymphoma (DLBCL) patients. This study aimed to assess the influence of lesion selection approaches and segmentation methods on the radiomics of DLBCL in terms of treatment response and prognosis prediction. Methods A total of 522 and 382 patients pathologically diagnosed with DLBCL were enrolled for complete regression and 2-year event-free survival prediction, respectively. Three lesion selection methods (largest or hottest lesion, patient level) and five segmentation methods (manual and four semiautomatic segmentations) were applied. A total of 112 radiomics features were extracted from the lesions and at the patient level. The feature selection was performed via random forest, and models were constructed via eXtreme Gradient Boosting. The performance of all the models was evaluated via the area under the curve (AUC), which was compared via the Delong test. Results The AUC values varied from 0.583 to 0.768 for the treatment response and prognosis prediction models on the basis of different lesion selection and segmentation methods. However, the prediction performance gap was not significant for each model (all P>0.05). The combined models (AUC =0.908 and 0.837 for treatment response and prognosis prediction, respectively) that incorporated radiomics and clinical features exhibited significant predictive superiority over the clinical models (AUC =0.622 and 0.636, respectively) and the international prognostic index model (AUC =0.623 for prognosis prediction) (all P<0.05). Conclusions Although there are differences in the selected radiomics features among lesion selection and segmentation methods, there is no substantial difference in the predictive power of each radiomics model. In addition, radiomics features have potential added value to clinical features.
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Affiliation(s)
- Yi Zhou
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Xue-Yan Zhou
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
| | - Yu-Chao Xu
- School of Nuclear Science and Technology, University of South China, Hengyang, China
| | - Xue-Lei Ma
- Department of Biotherapy, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Rong Tian
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu, China
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27
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Brancato V, Garbino N, Aiello M, Salvatore M, Cavaliere C. Exploratory Analysis of Radiomics and Pathomics in Uterine Corpus Endometrial Carcinoma. Sci Rep 2024; 14:30727. [PMID: 39730425 DOI: 10.1038/s41598-024-78987-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2024] [Accepted: 11/05/2024] [Indexed: 12/29/2024] Open
Abstract
Uterine corpus endometrial carcinoma (EC) is one of the most common malignancies in the female reproductive system, characterized by tumor heterogeneity at both radiological and pathological scales. Both radiomics and pathomics have the potential to assess this heterogeneity and support EC diagnosis. This study examines the correlation between radiomics features from Apparent Diffusion Coefficient (ADC) maps and post-contrast T1 (T1C) images with pathomic features from pathology images in 32 patients from the CPTAC-UCEC database. 91 radiomics features were extracted from ADC maps and T1C images, and 566 pathomic features from cell detections and cell density maps at four different resolutions. Spearman's correlation and Bayes Factor analysis were used to evaluate radio-pathomic correlations. Significant cross-scale correlations were found, with strengths ranging from 0.57 to 0.89 in absolute value (9.47 × 104 < BF < 4.77 × 1014) for the ADC task, and from 0.64 and 0.70 (1.80 × 104 < BF < 5.69 × 105) for the T1C task. Most significant and high cross-scale associations were observed between ADC textural features and features from cell density maps. Correlations involving morphometric features and ADC and T1C first-order features were also observed, reflecting variations in tumor aggressiveness and tissue composition. These findings suggest that correlating radiomic features from ADC and T1C features with histopathological features can enhance understanding of EC intratumoral heterogeneity.
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Affiliation(s)
| | - Nunzia Garbino
- IRCCS SYNLAB SDN, Via E. Gianturco 113, 80143, Naples, Italy
| | - Marco Aiello
- IRCCS SYNLAB SDN, Via E. Gianturco 113, 80143, Naples, Italy
| | - Marco Salvatore
- IRCCS SYNLAB SDN, Via E. Gianturco 113, 80143, Naples, Italy
| | - Carlo Cavaliere
- IRCCS SYNLAB SDN, Via E. Gianturco 113, 80143, Naples, Italy
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28
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Bini F, Missori E, Pucci G, Pasini G, Marinozzi F, Forte GI, Russo G, Stefano A. Preclinical Implementation of matRadiomics: A Case Study for Early Malformation Prediction in Zebrafish Model. J Imaging 2024; 10:290. [PMID: 39590754 PMCID: PMC11595506 DOI: 10.3390/jimaging10110290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2024] [Revised: 11/08/2024] [Accepted: 11/12/2024] [Indexed: 11/28/2024] Open
Abstract
Radiomics provides a structured approach to support clinical decision-making through key steps; however, users often face difficulties when switching between various software platforms to complete the workflow. To streamline this process, matRadiomics integrates the entire radiomics workflow within a single platform. This study extends matRadiomics to preclinical settings and validates it through a case study focused on early malformation differentiation in a zebrafish model. The proposed plugin incorporates Pyradiomics and streamlines feature extraction, selection, and classification using machine learning models (linear discriminant analysis-LDA; k-nearest neighbors-KNNs; and support vector machines-SVMs) with k-fold cross-validation for model validation. Classifier performances are evaluated using area under the ROC curve (AUC) and accuracy. The case study indicated the criticality of the long time required to extract features from preclinical images, generally of higher resolution than clinical images. To address this, a feature analysis was conducted to optimize settings, reducing extraction time while maintaining similarity to the original features. As a result, SVM exhibited the best performance for early malformation differentiation in zebrafish (AUC = 0.723; accuracy of 0.72). This case study underscores the plugin's versatility and effectiveness in early biological outcome prediction, emphasizing its applicability across biomedical research fields.
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Affiliation(s)
- Fabiano Bini
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy; (F.B.); (E.M.); (F.M.)
| | - Elisa Missori
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy; (F.B.); (E.M.); (F.M.)
| | - Gaia Pucci
- Institute of Bioimaging and Complex Biological Systems—National Research Council (IBSBC—CNR), Contrada Pietrapollastra-Pisciotto, 90015 Cefalù, Italy; (G.P.); (G.I.F.); (G.R.); (A.S.)
| | - Giovanni Pasini
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy; (F.B.); (E.M.); (F.M.)
- Institute of Bioimaging and Complex Biological Systems—National Research Council (IBSBC—CNR), Contrada Pietrapollastra-Pisciotto, 90015 Cefalù, Italy; (G.P.); (G.I.F.); (G.R.); (A.S.)
| | - Franco Marinozzi
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy; (F.B.); (E.M.); (F.M.)
| | - Giusi Irma Forte
- Institute of Bioimaging and Complex Biological Systems—National Research Council (IBSBC—CNR), Contrada Pietrapollastra-Pisciotto, 90015 Cefalù, Italy; (G.P.); (G.I.F.); (G.R.); (A.S.)
| | - Giorgio Russo
- Institute of Bioimaging and Complex Biological Systems—National Research Council (IBSBC—CNR), Contrada Pietrapollastra-Pisciotto, 90015 Cefalù, Italy; (G.P.); (G.I.F.); (G.R.); (A.S.)
| | - Alessandro Stefano
- Institute of Bioimaging and Complex Biological Systems—National Research Council (IBSBC—CNR), Contrada Pietrapollastra-Pisciotto, 90015 Cefalù, Italy; (G.P.); (G.I.F.); (G.R.); (A.S.)
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Behmanesh B, Abdi-Saray A, Deevband MR, Amoui M, Haghighatkhah HR, Shalbaf A. Predicting the Response of Patients Treated with 177Lu-DOTATATE Using Single-photon Emission Computed Tomography-Computed Tomography Image-based Radiomics and Clinical Features. JOURNAL OF MEDICAL SIGNALS & SENSORS 2024; 14:28. [PMID: 39600984 PMCID: PMC11592923 DOI: 10.4103/jmss.jmss_54_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 05/01/2024] [Accepted: 05/27/2024] [Indexed: 11/29/2024]
Abstract
Background In this study, we want to evaluate the response to Lutetium-177 (177Lu)-DOTATATE treatment in patients with neuroendocrine tumors (NETs) using single-photon emission computed tomography (SPECT) and computed tomography (CT), based on image-based radiomics and clinical features. Methods The total volume of tumor areas was segmented into 61 SPECT and 41 SPECT-CT images from 22 patients with NETs. A total of 871 radiomics and clinical features were extracted from the SPECT and SPECT-CT images. Subsequently, a feature reduction method called maximum relevance minimum redundancy (mRMR) was used to select the best combination of features. These selected features were modeled using a decision tree (DT), random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM) classifiers to predict the treatment response in patients. For the SPECT and SPECT-CT images, ten and eight features, respectively, were selected using the mRMR algorithm. Results The results revealed that the RF classifier with feature selection algorithms through mRMR had the highest classification accuracies of 64% and 83% for the SPECT and SPECT-CT images, respectively. The accuracy of the classifications of DT, KNN, and SVM for SPECT-CT images is 79%, 74%, and 67%, respectively. The poor accuracy obtained from different classifications in SPECT images (≈64%) showed that these images are not suitable for predicting treatment response. Conclusions Modeling the selected features of SPECT-CT images based on their anatomy and the presence of extensive gray levels makes it possible to predict responses to the treatment of 177Lu-DOTATATE for patients with NETs.
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Affiliation(s)
| | | | - Mohammad Reza Deevband
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mahasti Amoui
- Department of Nuclear Medicine, School of Medicine, Shohada-e Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid R. Haghighatkhah
- Department of Radiology and Medical Imaging Center, School of Medicine, Shohada-e Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Jabal MS, Mohammed MA, Nesvick CL, Kobeissi H, Graffeo CS, Pollock BE, Brinjikji W. DSA Quantitative Analysis and Predictive Modeling of Obliteration in Cerebral AVM following Stereotactic Radiosurgery. AJNR Am J Neuroradiol 2024; 45:1521-1527. [PMID: 39266257 PMCID: PMC11448972 DOI: 10.3174/ajnr.a8351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 05/09/2024] [Indexed: 09/14/2024]
Abstract
BACKGROUND AND PURPOSE Stereotactic radiosurgery is a key treatment modality for cerebral AVMs, particularly for small lesions and those located in eloquent brain regions. Predicting obliteration remains challenging due to evolving treatment paradigms and complex AVM presentations. With digital subtraction angiography (DSA) being the gold standard for outcome evaluation, radiomic approaches offer potential for more objective and detailed analysis. We aimed to develop machine learning modeling using DSA quantitative features for post-SRS obliteration prediction. MATERIALS AND METHODS A prospective registry of patients with cerebral AVMs was screened to include patients with digital prestereotactic radiosurgery DSA. Anterior-posterior and lateral views were retrieved and manually segmented. Quantitative features were computed from the lesion ROI. Following feature selection, machine learning models were developed to predict unsuccessful 2-year total obliteration using processed radiomics features in comparison with clinical and radiosurgical features. When we evaluated through area under the receiver operating characteristic curve (AUROC), accuracy, area under the precision-recall curve F1, recall, and precision, the best performing model predictions on the test set were interpreted using the Shapley additive explanations approach. RESULTS DSA images of 100 included patients were retrieved and analyzed. The best-performing clinical radiosurgical model was a gradient boosting classifier with an AUROC of 68% and a recall of 67%. When we used radiomics variables as input, the AdaBoost classifier had the best evaluation metrics with an AUROC of 79% and a recall of 75%. The most important clinico-radiosurgical features, ranked by model contribution, were lesion volume, patient age, treatment dose rate, the presence of seizure at presentation, and prior resection. The most important ranked radiomics features were the following: gray-level size zone matrix, gray-level nonuniformity, kurtosis, sphericity, skewness, and gray-level dependence matrix dependence nonuniformity. CONCLUSIONS The combination of radiomics with machine learning is a promising approach for predicting cerebral AVM obliteration status following stereotactic radiosurgery. DSA could enhance prognostication of stereotactic radiosurgery-treated AVMs due to its high spatial resolution. Model interpretation is essential for building transparent models and establishing clinically valid radiomic signatures.
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Affiliation(s)
- Mohamed Sobhi Jabal
- From the Department of Radiology (M.S.J., M.A.M., H.K., W.B.), Mayo Clinic, Rochester, Minnesota
- Department of Computer and Information Science (M.S.J.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - Marwa A Mohammed
- From the Department of Radiology (M.S.J., M.A.M., H.K., W.B.), Mayo Clinic, Rochester, Minnesota
| | - Cody L Nesvick
- Department of Neurological Surgery (C.L.N., C.S.G., B.E.P., W.B.), Mayo Clinic, Rochester, Minnesota
| | - Hassan Kobeissi
- From the Department of Radiology (M.S.J., M.A.M., H.K., W.B.), Mayo Clinic, Rochester, Minnesota
| | - Christopher S Graffeo
- Department of Neurological Surgery (C.L.N., C.S.G., B.E.P., W.B.), Mayo Clinic, Rochester, Minnesota
| | - Bruce E Pollock
- Department of Neurological Surgery (C.L.N., C.S.G., B.E.P., W.B.), Mayo Clinic, Rochester, Minnesota
| | - Waleed Brinjikji
- From the Department of Radiology (M.S.J., M.A.M., H.K., W.B.), Mayo Clinic, Rochester, Minnesota
- Department of Neurological Surgery (C.L.N., C.S.G., B.E.P., W.B.), Mayo Clinic, Rochester, Minnesota
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31
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Behmanesh B, Abdi-Saray A, Deevband MR, Amoui M, Haghighatkhah HR. Radiomics Analysis for Clinical Decision Support in 177Lu-DOTATATE Therapy of Metastatic Neuroendocrine Tumors using CT Images. J Biomed Phys Eng 2024; 14:423-434. [PMID: 39391275 PMCID: PMC11462270 DOI: 10.31661/jbpe.v0i0.2112-1444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 02/10/2022] [Indexed: 10/12/2024]
Abstract
Background Radiomics is the computation of quantitative image features extracted from medical imaging modalities to help clinical decision support systems, which could ultimately meliorate personalized management based on individual characteristics. Objective This study aimed to create a predictive model of response to peptide receptor radionuclide therapy (PRRT) using radiomics computed tomography (CT) images to decrease the dose for patients if they are not a candidate for treatment. Material and Methods In the current retrospective study, 34 patients with neuroendocrine tumors whose disease is clinically confirmed participated. Effective factors in the treatment were selected by eXtreme gradient boosting (XGBoost) and minimum redundancy maximum relevance (mRMR). Classifiers of decision trees (DT), random forest (RF), and K-nearest neighbors (KNN) with selected quantitative and clinical features were used for modeling. A confusion matrix was used to evaluate the performance of the model. Results Out of 866 quantitative and clinical features, nine features with the XGBoost method and ten features with the mRMR pattern were selected that had the most relevance in predicting response to treatment. Selected features of the XGBoost method in integration with the RF classifier provided the highest accuracy (accuracy: 89%), and features selected by the mRMR method in combination with the RF classifier showed satisfactory performance (accuracy: 74%). Conclusion This exploratory analysis shows that radiomic features with high accuracy can effectively predict response to personalize treatment.
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Affiliation(s)
- Baharak Behmanesh
- Department of Nuclear Physics Faculty of Science, Urmia University, Oroumieh, Iran
| | - Akbar Abdi-Saray
- Department of Nuclear Physics Faculty of Science, Urmia University, Oroumieh, Iran
| | - Mohammad Reza Deevband
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mahasti Amoui
- Department of Nuclear Medicine, Shohada-e Tajrish Hospital, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Reza Haghighatkhah
- Department of Radiology and Medical Imaging Center, Shohada-e Tajrish Hospital, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Huang F, Huang Q, Liao X, Gao Y. Prediction of high-risk prostate cancer based on the habitat features of biparametric magnetic resonance and the omics features of contrast-enhanced ultrasound. Heliyon 2024; 10:e37955. [PMID: 39323806 PMCID: PMC11423289 DOI: 10.1016/j.heliyon.2024.e37955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 08/22/2024] [Accepted: 09/13/2024] [Indexed: 09/27/2024] Open
Abstract
Rationale and objectives To predict high-risk prostate cancer (PCa) by combining the habitat features of biparametric magnetic resonance imaging (bp-MRI) with the omics features of contrast-enhanced ultrasound (CEUS). Materials and methods This study retrospectively collected patients with PCa confirmed by histopathology from January 2020 to June 2023. All patients underwent bp-MRI and CEUS of the prostate, followed by a targeted and transrectal systematic prostate biopsy. The cases were divided into the intermediate-low-risk group (Gleason score ≤7, n = 59) and high-risk group (Gleason score ≥8, n = 33). Radiomics prediction models, namely, MRI_habitat, CEUS_intra, and MRI-CEUS models, were developed based on the habitat features of bp-MRI, the omics features of CEUS, and a merge of features of the two, respectively. Predicted probabilities, called radscores, were then obtained. Clinical-radiological indicators were screened to construct clinic models, which generated clinic scores. The omics-clinic model was constructed by combining the radscore of MRI-CEUS and the clinic score. The predictive performance of all the models was evaluated using the receiver operating characteristic curve. Results The area under the curve (AUC) values of the MRI-CEUS model were 0.875 and 0.842 in the training set and test set, respectively, which were higher than those of the MR_habitat (training set: 0.846, test set: 0.813), CEUS_intra (training set: 0.801, test set: 0.743), and clinic models (training set: 0.722, test set: 0.611). The omics-clinic model achieved a higher AUC (train set: 0.986, test set: 0.898). Conclusions The combination of the habitat features of bp-MRI and the omics features of CEUS can help predict high-risk PCa.
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Affiliation(s)
- Fangyi Huang
- Department of Ultrasound, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Rd, Nanning, 530021, Guangxi, China
| | - Qun Huang
- Department of Ultrasound, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Rd, Nanning, 530021, Guangxi, China
| | - Xinhong Liao
- Department of Ultrasound, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Rd, Nanning, 530021, Guangxi, China
| | - Yong Gao
- Department of Ultrasound, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Rd, Nanning, 530021, Guangxi, China
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Hong R, Ping X, Liu Y, Feng F, Hu S, Hu C. Combined CT-Based Radiomics and Clinic-Radiological Characteristics for Preoperative Differentiation of Solitary-Type Invasive Mucinous and Non-Mucinous Lung Adenocarcinoma. Int J Gen Med 2024; 17:4267-4279. [PMID: 39324145 PMCID: PMC11423830 DOI: 10.2147/ijgm.s479978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2024] [Accepted: 09/11/2024] [Indexed: 09/27/2024] Open
Abstract
Purpose The clinical, pathological, gene expression, and prognosis of invasive mucinous adenocarcinoma (IMA) differ from those of invasive non-mucinous adenocarcinoma (INMA), but it is not easy to distinguish these two. This study aims to explore the value of combining CT-based radiomics features with clinic-radiological characteristics for preoperative diagnosis of solitary-type IMA and to establish an optimal diagnostic model. Methods In this retrospective study, a total of 220 patients were enrolled and randomly assigned to a training cohort (n = 154; 73 IMA and 81 INMA) and a testing cohort (n = 66; 31 IMA and 35 INMA). Radiomics features and clinic-radiological characteristics were extracted from plain CT images. The radiomics models for predicting solitary-type IMA were developed by three classifiers: linear discriminant analysis (LDA), logistic regression-least absolute shrinkage and selection operator (LR-LASSO), and support vector machine (SVM). The combined model was constructed by integrating radiomics and clinic-radiological features with the best performing classifier. Receiver operating characteristic (ROC) curves were used to evaluate models' performance, and the area under the curve (AUC) were compared by the DeLong test. Decision curve analysis (DCA) was conducted to assess the clinical utility. Results Regarding CT characteristics, tumor lung interface, and pleural retraction were the independent risk factors of solitary-type IMA. The radiomics model using the SVM classifier outperformed the other two classifiers in the testing cohort, with an AUC of 0.776 (95% CI: 0.664-0.888). The combined model incorporating radiomics features and clinic-radiological factors was the optimal model, with AUCs of 0.843 (95% CI: 0.781-0.906) and 0.836 (95% CI: 0.732-0.940) in the training and testing cohorts, respectively. Conclusion The combined model showed good ability in predicting solitary-type IMA and can provide a non-invasive and efficient approach to clinical decision-making.
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Affiliation(s)
- Rong Hong
- Department of Radiology, Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, Jiangsu, 215100, People's Republic of China
- Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215006, People's Republic of China
| | - Xiaoxia Ping
- Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215006, People's Republic of China
- Institute of Medical Imaging, Soochow University, Suzhou, Jiangsu, 215006, People's Republic of China
| | - Yuanying Liu
- Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215006, People's Republic of China
| | - Feiwen Feng
- Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215006, People's Republic of China
| | - Su Hu
- Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215006, People's Republic of China
- Institute of Medical Imaging, Soochow University, Suzhou, Jiangsu, 215006, People's Republic of China
| | - Chunhong Hu
- Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215006, People's Republic of China
- Institute of Medical Imaging, Soochow University, Suzhou, Jiangsu, 215006, People's Republic of China
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McGale JP, Chen DL, Trebeschi S, Farwell MD, Wu AM, Cutler CS, Schwartz LH, Dercle L. Artificial intelligence in immunotherapy PET/SPECT imaging. Eur Radiol 2024; 34:5829-5841. [PMID: 38355986 DOI: 10.1007/s00330-024-10637-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/12/2023] [Accepted: 01/15/2024] [Indexed: 02/16/2024]
Abstract
OBJECTIVE Immunotherapy has dramatically altered the therapeutic landscape for oncology, but more research is needed to identify patients who are likely to achieve durable clinical benefit and those who may develop unacceptable side effects. We investigated the role of artificial intelligence in PET/SPECT-guided approaches for immunotherapy-treated patients. METHODS We performed a scoping review of MEDLINE, CENTRAL, and Embase databases using key terms related to immunotherapy, PET/SPECT imaging, and AI/radiomics through October 12, 2022. RESULTS Of the 217 studies identified in our literature search, 24 relevant articles were selected. The median (interquartile range) sample size of included patient cohorts was 63 (157). Primary tumors of interest were lung (n = 14/24, 58.3%), lymphoma (n = 4/24, 16.7%), or melanoma (n = 4/24, 16.7%). A total of 28 treatment regimens were employed, including anti-PD-(L)1 (n = 13/28, 46.4%) and anti-CTLA-4 (n = 4/28, 14.3%) monoclonal antibodies. Predictive models were built from imaging features using univariate radiomics (n = 7/24, 29.2%), radiomics (n = 12/24, 50.0%), or deep learning (n = 5/24, 20.8%) and were most often used to prognosticate (n = 6/24, 25.0%) or describe tumor phenotype (n = 5/24, 20.8%). Eighteen studies (75.0%) performed AI model validation. CONCLUSION Preliminary results suggest broad potential for the application of AI-guided immunotherapy management after further validation of models on large, prospective, multicenter cohorts. CLINICAL RELEVANCE STATEMENT This scoping review describes how artificial intelligence models are built to make predictions based on medical imaging and explores their application specifically in the PET and SPECT examination of immunotherapy-treated cancers. KEY POINTS • Immunotherapy has drastically altered the cancer treatment landscape but is known to precipitate response patterns that are not accurately accounted for by traditional imaging methods. • There is an unmet need for better tools to not only facilitate in-treatment evaluation but also to predict, a priori, which patients are likely to achieve a good response with a certain treatment as well as those who are likely to develop side effects. • Artificial intelligence applied to PET/SPECT imaging of immunotherapy-treated patients is mainly used to make predictions about prognosis or tumor phenotype and is built from baseline, pre-treatment images. Further testing is required before a true transition to clinical application can be realized.
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Affiliation(s)
- Jeremy P McGale
- Department of Radiology, New York-Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA.
| | - Delphine L Chen
- Department of Molecular Imaging and Therapy, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Stefano Trebeschi
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School of Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Michael D Farwell
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anna M Wu
- Department of Immunology and Theranostics, Beckman Research Institute of City of Hope, Duarte, CA, USA
| | - Cathy S Cutler
- Collider Accelerator Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Lawrence H Schwartz
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Laurent Dercle
- Department of Radiology, New York-Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA.
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Russo L, Charles-Davies D, Bottazzi S, Sala E, Boldrini L. Radiomics for clinical decision support in radiation oncology. Clin Oncol (R Coll Radiol) 2024; 36:e269-e281. [PMID: 38548581 DOI: 10.1016/j.clon.2024.03.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 02/14/2024] [Accepted: 03/08/2024] [Indexed: 07/09/2024]
Abstract
Radiomics is a promising tool for the development of quantitative biomarkers to support clinical decision-making. It has been shown to improve the prediction of response to treatment and outcome in different settings, particularly in the field of radiation oncology by optimising the dose delivery solutions and reducing the rate of radiation-induced side effects, leading to a fully personalised approach. Despite the promising results offered by radiomics at each of these stages, standardised methodologies, reproducibility and interpretability of results are still lacking, limiting the potential clinical impact of these tools. In this review, we briefly describe the principles of radiomics and the most relevant applications of radiomics at each stage of cancer management in the framework of radiation oncology. Furthermore, the integration of radiomics into clinical decision support systems is analysed, defining the challenges and offering possible solutions for translating radiomics into a clinically applicable tool.
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Affiliation(s)
- L Russo
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Dipartimento di Scienze Radiologiche ed Ematologiche. Università Cattolica Del Sacro Cuore, Rome, Italy.
| | - D Charles-Davies
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - S Bottazzi
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - E Sala
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Dipartimento di Scienze Radiologiche ed Ematologiche. Università Cattolica Del Sacro Cuore, Rome, Italy
| | - L Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
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Yang X, Niu W, Wu K, Li X, Hou H, Tan Y, Wang X, Yang G, Wang L, Zhang H. Diffusion kurtosis imaging-based habitat analysis identifies high-risk molecular subtypes and heterogeneity matching in diffuse gliomas. Ann Clin Transl Neurol 2024; 11:2073-2087. [PMID: 38887966 PMCID: PMC11330218 DOI: 10.1002/acn3.52128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 05/14/2024] [Accepted: 06/02/2024] [Indexed: 06/20/2024] Open
Abstract
OBJECTIVE High-risk types of diffuse gliomas in adults include isocitrate dehydrogenase (IDH) wild-type glioblastomas and grade 4 astrocytomas. Achieving noninvasive prediction of high-risk molecular subtypes of gliomas is important for personalized and precise diagnosis and treatment. METHODS We retrospectively collected data from 116 patients diagnosed with adult diffuse gliomas. Multiple high-risk molecular markers were tested, and various habitat models and whole-tumor models were constructed based on preoperative routine and diffusion kurtosis imaging (DKI) sequences to predict high-risk molecular subtypes of gliomas. Feature selection and model construction utilized Least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM). Finally, the Wilcoxon rank-sum test was employed to explore the correlation between habitat quantitative features (intra-tumor heterogeneity score,ITH score) and heterogeneity, as well as high-risk molecular subtypes. RESULTS The results showed that the habitat analysis model based on DKI performed remarkably well (with AUC values reaching 0.977 and 0.902 in the training and test sets, respectively). The model's performance was further enhanced when combined with clinical variables. (The AUC values were 0.994 and 0.920, respectively.) Additionally, we found a close correlation between ITH score and heterogeneity, with statistically significant differences observed between high-risk and non-high-risk molecular subtypes. INTERPRETATION The habitat model based on DKI is an ideal means for preoperatively predicting high-risk molecular subtypes of gliomas, holding significant value for noninvasively alerting malignant gliomas and those with malignant transformation potential.
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Affiliation(s)
- Xiangli Yang
- Department of RadiologyFirst Hospital of Shanxi Medical UniversityTaiyuan030001China
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi HospitalTaiyuan030032China
- College of Medical Imaging, Shanxi Medical UniversityTaiyuan030001China
| | - Wenju Niu
- College of Medical Imaging, Shanxi Medical UniversityTaiyuan030001China
| | - Kai Wu
- Department of Information ManagementFirst Hospital of Shanxi Medical UniversityTaiyuan030001China
| | - Xiang Li
- College of Medical Imaging, Shanxi Medical UniversityTaiyuan030001China
| | - Heng Hou
- Department of RadiologyFirst Hospital of Shanxi Medical UniversityTaiyuan030001China
| | - Yan Tan
- Department of RadiologyFirst Hospital of Shanxi Medical UniversityTaiyuan030001China
| | - Xiaochun Wang
- Department of RadiologyFirst Hospital of Shanxi Medical UniversityTaiyuan030001China
| | - Guoqiang Yang
- Department of RadiologyFirst Hospital of Shanxi Medical UniversityTaiyuan030001China
- College of Medical Imaging, Shanxi Medical UniversityTaiyuan030001China
- Shanxi Key Laboratory of Intelligent Imaging and NanomedicineFirst Hospital of Shanxi Medical UniversityTaiyuan030001China
| | - Lei Wang
- Beijing Tiantan HospitalCapital Medical UniversityBeijing100050China
| | - Hui Zhang
- Department of RadiologyFirst Hospital of Shanxi Medical UniversityTaiyuan030001China
- College of Medical Imaging, Shanxi Medical UniversityTaiyuan030001China
- Shanxi Key Laboratory of Intelligent Imaging and NanomedicineFirst Hospital of Shanxi Medical UniversityTaiyuan030001China
- Intelligent Imaging Big Data and Functional Nano‐imaging Engineering Research Center of Shanxi ProvinceFirst Hospital of Shanxi Medical UniversityTaiyuan030001China
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Zhi H, Xiang Y, Chen C, Zhang W, Lin J, Gao Z, Shen Q, Shao J, Yang X, Yang Y, Chen X, Zheng J, Lu M, Pan B, Dong Q, Shen X, Ma C. Development and validation of a machine learning-based 18F-fluorodeoxyglucose PET/CT radiomics signature for predicting gastric cancer survival. Cancer Imaging 2024; 24:99. [PMID: 39080806 PMCID: PMC11290137 DOI: 10.1186/s40644-024-00741-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Accepted: 07/13/2024] [Indexed: 08/02/2024] Open
Abstract
BACKGROUND Survival prognosis of patients with gastric cancer (GC) often influences physicians' choice of their follow-up treatment. This study aimed to develop a positron emission tomography (PET)-based radiomics model combined with clinical tumor-node-metastasis (TNM) staging to predict overall survival (OS) in patients with GC. METHODS We reviewed the clinical information of a total of 327 patients with pathological confirmation of GC undergoing 18 F-fluorodeoxyglucose (18 F-FDG) PET scans. The patients were randomly classified into training (n = 229) and validation (n = 98) cohorts. We extracted 171 PET radiomics features from the PET images and determined the PET radiomics scores (RS) using the least absolute shrinkage and selection operator (LASSO) and random survival forest (RSF). A radiomics model, including PET RS and clinical TNM staging, was constructed to predict the OS of patients with GC. This model was evaluated for discrimination, calibration, and clinical usefulness. RESULTS On multivariate COX regression analysis, the difference between age, carcinoembryonic antigen (CEA), clinical TNM, and PET RS in GC patients was statistically significant (p < 0.05). A radiomics model was developed based on the results of COX regression. The model had the Harrell's concordance index (C-index) of 0.817 in the training cohort and 0.707 in the validation cohort and performed better than a single clinical model and a model with clinical features combined with clinical TNM staging. Further analyses showed higher PET RS in patients who were older (p < 0.001) and those who had elevated CEA (p < 0.001) and higher clinical TNM (p < 0.001). At different clinical TNM stages, a higher PET RS was associated with a worse survival prognosis. CONCLUSIONS Radiomics models based on PET RS, clinical TNM, and clinical features may provide new tools for predicting OS in patients with GC.
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Affiliation(s)
- Huaiqing Zhi
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Yilan Xiang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Chenbin Chen
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
- Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Weiteng Zhang
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Jie Lin
- Department of PET/CT, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
- Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Zekan Gao
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Qingzheng Shen
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Jiancan Shao
- Department of General Surgery, The Second Affiliated Hospital & Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Xinxin Yang
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Yunjun Yang
- Department of PET/CT, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Xiaodong Chen
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
- Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Jingwei Zheng
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Mingdong Lu
- Department of General Surgery, The Second Affiliated Hospital & Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Bujian Pan
- Department of General Surgery, The Second Affiliated Hospital & Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Qiantong Dong
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
- Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
| | - Xian Shen
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
- Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
| | - Chunxue Ma
- Department of Gastrointestinal Surgery Nursing Unit, Ward 443, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
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Liu J, Sui C, Bian H, Li Y, Wang Z, Fu J, Qi L, Chen K, Xu W, Li X. Radiomics based on 18F-FDG PET/CT for prediction of pathological complete response to neoadjuvant therapy in non-small cell lung cancer. Front Oncol 2024; 14:1425837. [PMID: 39132503 PMCID: PMC11310012 DOI: 10.3389/fonc.2024.1425837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 07/09/2024] [Indexed: 08/13/2024] Open
Abstract
Purpose This study aimed to establish and evaluate the value of integrated models involving 18F-FDG PET/CT-based radiomics and clinicopathological information in the prediction of pathological complete response (pCR) to neoadjuvant therapy (NAT) for non-small cell lung cancer (NSCLC). Methods A total of 106 eligible NSCLC patients were included in the study. After volume of interest (VOI) segmentation, 2,016 PET-based and 2,016 CT-based radiomic features were extracted. To select an optimal machine learning model, a total of 25 models were constructed based on five sets of machine learning classifiers combined with five sets of predictive feature resources, including PET-based alone radiomics, CT-based alone radiomics, PET/CT-based radiomics, clinicopathological features, and PET/CT-based radiomics integrated with clinicopathological features. Area under the curves (AUCs) of receiver operator characteristic (ROC) curves were used as the main outcome to assess the model performance. Results The hybrid PET/CT-derived radiomic model outperformed PET-alone and CT-alone radiomic models in the prediction of pCR to NAT. Moreover, addition of clinicopathological information further enhanced the predictive performance of PET/CT-derived radiomic model. Ultimately, the support vector machine (SVM)-based PET/CT radiomics combined clinicopathological information presented an optimal predictive efficacy with an AUC of 0.925 (95% CI 0.869-0.981) in the training cohort and an AUC of 0.863 (95% CI 0.740-0.985) in the test cohort. The developed nomogram involving radiomics and pathological type was suggested as a convenient tool to enable clinical application. Conclusions The 18F-FDG PET/CT-based SVM radiomics integrated with clinicopathological information was an optimal model to non-invasively predict pCR to NAC for NSCLC.
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Affiliation(s)
- Jianjing Liu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
| | - Chunxiao Sui
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Haiman Bian
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Yue Li
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Lung Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Ziyang Wang
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
| | - Jie Fu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
| | - Lisha Qi
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Kun Chen
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Wengui Xu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Xiaofeng Li
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
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Jiang Y, Peng Y, Wu Y, Sun Q, Hua T. Multimodal Machine Learning-Based Ductal Carcinoma in situ Prediction from Breast Fibromatosis. Cancer Manag Res 2024; 16:811-823. [PMID: 39044747 PMCID: PMC11264379 DOI: 10.2147/cmar.s467400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 06/26/2024] [Indexed: 07/25/2024] Open
Abstract
OBJECTIVE To develop a clinical-radiomics model using a multimodal machine learning method for distinguishing ductal carcinoma in situ (DCIS) from breast fibromatosis. METHODS The clinical factors, ultrasound features, and related ultrasound images of 306 patients (198 DCIS patients) were retrospectively collected. Patients in the development and validation cohort were 184 and 122, respectively. The independent clinical and ultrasound factors identified by the multivariable logistic regression analysis were used for the clinical-ultrasound model construction. Then, the region of interest of breast lesions was delineated and radiomics features were extracted. Six machine learning algorithms were trained to develop a radiomics model. The algorithm with higher and more stable prediction ability was chosen to convert the output of the results into the Radscore. Further, the independent clinical predictors and Radscore were enrolled into the logistic regression analysis to generate a combined clinical-radiomics model. The receiver operating characteristic curve analysis, DeLong test, and decision curve analysis were adopted to compare the prediction ability and clinical efficacy of three different models. RESULTS Among the six classifiers, logistic regression model was selected as the final radiomics model. Besides, the combined clinical-radiomics model exhibited a superior ability in distinguishing DCIS from breast fibromatosis to the clinical-ultrasound model and the radiomics model. CONCLUSION The combined model by integrating clinical-ultrasound factors and radiomics features performed well in predicting DCIS, which might promote prompt interventions to improve the early diagnosis and prognosis of the patients.
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Affiliation(s)
- Yan Jiang
- Department of Ultrasound, Ningbo Medical Centre Lihuili Hospital, Ningbo, Zhejiang, People’s Republic of China
| | - Yuanyuan Peng
- Department of Ultrasound, Ningbo Medical Centre Lihuili Hospital, Ningbo, Zhejiang, People’s Republic of China
| | - Yingyi Wu
- Department of Ultrasound, Ningbo Medical Centre Lihuili Hospital, Ningbo, Zhejiang, People’s Republic of China
| | - Qing Sun
- Department of Ultrasound, Ningbo Medical Centre Lihuili Hospital, Ningbo, Zhejiang, People’s Republic of China
| | - Tebo Hua
- Department of Thyroid Breast Surgery, Ningbo Medical Centre Lihuili Hospital, Ningbo, Zhejiang, People’s Republic of China
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Wu J, Meng H, Zhou L, Wang M, Jin S, Ji H, Liu B, Jin P, Du C. Habitat radiomics and deep learning fusion nomogram to predict EGFR mutation status in stage I non-small cell lung cancer: a multicenter study. Sci Rep 2024; 14:15877. [PMID: 38982267 PMCID: PMC11233600 DOI: 10.1038/s41598-024-66751-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 07/03/2024] [Indexed: 07/11/2024] Open
Abstract
Develop a radiomics nomogram that integrates deep learning, radiomics, and clinical variables to predict epidermal growth factor receptor (EGFR) mutation status in patients with stage I non-small cell lung cancer (NSCLC). We retrospectively included 438 patients who underwent curative surgery and completed driver-gene mutation tests for stage I NSCLC from four academic medical centers. Predictive models were established by extracting and analyzing radiomic features in intratumoral, peritumoral, and habitat regions of CT images to identify EGFR mutation status in stage I NSCLC. Additionally, three deep learning models based on the intratumoral region were constructed. A nomogram was developed by integrating representative radiomic signatures, deep learning, and clinical features. Model performance was assessed by calculating the area under the receiver operating characteristic (ROC) curve. The established habitat radiomics features demonstrated encouraging performance in discriminating between EGFR mutant and wild-type, with predictive ability superior to other single models (AUC 0.886, 0.812, and 0.790 for the training, validation, and external test sets, respectively). The radiomics-based nomogram exhibited excellent performance, achieving the highest AUC values of 0.917, 0.837, and 0.809 in the training, validation, and external test sets, respectively. Decision curve analysis (DCA) indicated that the nomogram provided a higher net benefit than other radiomics models, offering valuable information for treatment.
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Affiliation(s)
- Jingran Wu
- Department of Oncology, General Hospital of Northern Theater Command, Shenyang, 110840, China
| | - Hao Meng
- Department of Thoracic Surgery, General Hospital of Northern Theater Command, Shenyang, 110840, China
| | - Lin Zhou
- Department of Thoracic Surgery, Yuebei People's Hospital Affiliated to Shantou University Medical College, Shaoguan, 512025, China
| | - Meiling Wang
- Department of Oncology, General Hospital of Northern Theater Command, Shenyang, 110840, China
| | - Shanxiu Jin
- Department of Oncology, General Hospital of Northern Theater Command, Shenyang, 110840, China
| | - Hongjuan Ji
- Department of Oncology, General Hospital of Northern Theater Command, Shenyang, 110840, China
| | - Bona Liu
- Department of Oncology, General Hospital of Northern Theater Command, Shenyang, 110840, China.
| | - Peng Jin
- Department of Oncology, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, China.
| | - Cheng Du
- Department of Oncology, General Hospital of Northern Theater Command, Shenyang, 110840, China.
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Zuo Y, Liu L, Chang C, Yan H, Wang L, Sun D, Ruan M, Lei B, Xia X, Xie W, Song S, Huang G. Value of multi-center 18F-FDG PET/CT radiomics in predicting EGFR mutation status in lung adenocarcinoma. Med Phys 2024; 51:4872-4887. [PMID: 38285641 DOI: 10.1002/mp.16947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 12/08/2023] [Accepted: 12/27/2023] [Indexed: 01/31/2024] Open
Abstract
BACKGROUND Accurate, noninvasive, and reliable assessment of epidermal growth factor receptor (EGFR) mutation status and EGFR molecular subtypes is essential for treatment plan selection and individualized therapy in lung adenocarcinoma (LUAD). Radiomics models based on 18F-FDG PET/CT have great potential in identifying EGFR mutation status and EGFR subtypes in patients with LUAD. The validation of multi-center data, model visualization, and interpretation are significantly important for the management, application and trust of machine learning predictive models. However, few EGFR-related research involved model visualization and interpretation, and multi-center trial. PURPOSE To develop explainable optimal predictive models based on handcrafted radiomics features (HRFs) extracted from multi-center 18F-FDG PET/CT to predict EGFR mutation status and molecular subtypes in LUAD. METHODS Baseline 18F-FDG PET/CT images of 383 LUAD patients from three hospitals and one public data set were collected. Further, 1808 HRFs were extracted from the primary tumor regions using Pyradiomics. Predictive models were built based on cross-combination of seven feature selection methods and seven machine learning algorithms. Yellowbrick and explainable artificial intelligence technology were used for model visualization and interpretation. Receiver operating characteristic curve, classification report and confusion matrix were used for model performance evaluation. Clinical applicability of the optimal models was assessed by decision curve analysis. RESULTS STACK feature selection method combined with light gradient boosting machine (LGBM) reached optimal performance in identifying EGFR mutation status ([area under the curve] AUC = 0.81 in the internal test cohort; AUC = 0.62 in the external test cohort). Random forest feature selection method combined with LGBM reached optimal performance in predicting EGFR mutation molecular subtypes (AUC = 0.89 in the internal test cohort; AUC = 0.61 in the external test cohort). CONCLUSIONS Explainable machine learning models combined with radiomics features extracted from multi-center/scanner 18F-FDG PET/CT have certain potential to identify EGFR mutation status and subtypes in LUAD, which might be helpful to the treatment of LUAD.
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Affiliation(s)
- Yan Zuo
- Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
- Department of nuclear medicine, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Liu Liu
- Department of nuclear medicine, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Cheng Chang
- Department of nuclear medicine, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Hui Yan
- Department of nuclear medicine, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Lihua Wang
- Department of nuclear medicine, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Dazhen Sun
- Department of Automation, Shanghai Jiaotong University, Shanghai, China
| | - Maomei Ruan
- Department of nuclear medicine, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Bei Lei
- Department of nuclear medicine, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xunpeng Xia
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Wenhui Xie
- Department of nuclear medicine, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Shaoli Song
- Department of nuclear medicine, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Gang Huang
- Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
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Eifer M, Peters-Founshtein G, Yoel LC, Pinian H, Steiner R, Klang E, Catalano OA, Eshet Y, Domachevsky L. The role of FDG PET/CT radiomics in the prediction of pathological response to neoadjuvant treatment in patients with esophageal cancer. Rep Pract Oncol Radiother 2024; 29:211-218. [PMID: 39143975 PMCID: PMC11321767 DOI: 10.5603/rpor.99906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 03/18/2024] [Indexed: 08/16/2024] Open
Abstract
Background Attainment of a complete histopathological response following neoadjuvant therapy has been associated with favorable long-term survival outcomes in esophageal cancer patients. We investigated the ability of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) radiomic features to predict the pathological response to neoadjuvant treatment in patients with esophageal cancer. Materials and methods A retrospective review of medical records of patients with locally advanced resectable esophageal or esophagogastric junctional cancers. Included patients had a baseline FDG PET/CT scan and underwent Chemoradiotherapy for Oesophageal Cancer Followed by Surgery Study (CROSS) protocol followed by surgery. Four demographic variables and 107 PET radiomic features were extracted and analyzed using univariate and multivariate analyses to predict response to neoadjuvant therapy. Results Overall, 53 FDG-avid primary esophageal cancer lesions were segmented and radiomic features were extracted. Seventeen radiomic features and 2 non-radiomics variables were found to exhibit significant differences between neoadjuvant therapy responders and non-responders. An unsupervised hierarchical clustering analysis using these 19 variables classified patients in a manner significantly associated with response to neoadjuvant treatment (p < 0.01). Conclusion Our findings highlight the potential of FDG PET/CT radiomic features as a predictor for the response to neoadjuvant therapy in esophageal cancer patients. The combination of these radiomic features with select non-radiomic variables provides a model for stratifying patients based on their likelihood to respond to neoadjuvant treatment.
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Affiliation(s)
- Michal Eifer
- Department of Nuclear Imaging, Sheba Medical Center, Ramat Gan, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Gregory Peters-Founshtein
- Department of Nuclear Imaging, Sheba Medical Center, Ramat Gan, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Lotem Cohn Yoel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Hodaya Pinian
- Department of Nuclear Imaging, Sheba Medical Center, Ramat Gan, Israel
- Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
| | | | - Eyal Klang
- Department of Nuclear Imaging, Sheba Medical Center, Ramat Gan, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Sami Sagol AI Hub, ARC, Chaim Sheba Medical Center, Ramat Gan, Israel
| | - Onofrio A. Catalano
- Department of Radiology, Division of Abdominal Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Boston and Harvard Medical School, Boston, MA, United States
| | - Yael Eshet
- Department of Nuclear Imaging, Sheba Medical Center, Ramat Gan, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Liran Domachevsky
- Department of Nuclear Imaging, Sheba Medical Center, Ramat Gan, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
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Zhang Y, Chen J, Yang C, Dai Y, Zeng M. Preoperative prediction of microvascular invasion in hepatocellular carcinoma using diffusion-weighted imaging-based habitat imaging. Eur Radiol 2024; 34:3215-3225. [PMID: 37853175 DOI: 10.1007/s00330-023-10339-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 07/27/2023] [Accepted: 08/20/2023] [Indexed: 10/20/2023]
Abstract
OBJECTIVES Habitat imaging allows for the quantification and visualization of various subregions within the tumor. We aim to develop an approach using diffusion-weighted imaging (DWI)-based habitat imaging for preoperatively predicting the microvascular invasion (MVI) of hepatocellular carcinoma (HCC). METHODS Sixty-five patients were prospectively included and underwent multi-b DWI examinations. Based on the true diffusion coefficient (Dt), perfusion fraction (f), and mean kurtosis coefficient (MK), which respectively characterize cellular density, perfusion, and heterogeneity, the HCCs were divided into four habitats. The volume fraction of each habitat was quantified. The logistic regression was used to explore the risk factors from habitat fraction and clinical variables. Clinical, habitat, and nomogram models were constructed using the identified risk factors from clinical characteristics, habitat fraction, and their combination, respectively. The diagnostic accuracy was evaluated using the area under the receiver operating characteristic curves (AUCs). RESULTS MVI-positive HCC exhibited a significantly higher fraction of habitat 4 (f4) and a significantly lower fraction of habitat 2 (f2) (p < 0.001), which were selected as risk factors. Additionally, tumor size and elevated alpha-fetoprotein (AFP) were also included as risk factors for MVI. The nomogram model demonstrated the highest diagnostic performance (AUC = 0.807), followed by the habitat model (AUC = 0.777) and the clinical model (AUC = 0.708). Decision curve analysis indicated that the nomogram model offered more net benefit in identifying MVI compared to the clinical model. CONCLUSIONS DWI-based habitat imaging shows clinical potential for noninvasively and preoperatively determining the MVI of HCC with high accuracy. CLINICAL RELEVANCE STATEMENT The proposed strategy, diffusion-weighted imaging-based habitat imaging, can be applied for preoperatively and noninvasively identifying microvascular invasion in hepatocellular carcinoma, which offers potential benefits in terms of prognostic prediction and clinical management. KEY POINTS • This study proposed a strategy of DWI-based habitat imaging for hepatocellular carcinoma. • The habitat imaging-derived metrics can serve as diagnostic markers for identifying the microvascular invasion. • Integrating the habitat-based metric and clinical variable, a predictive nomogram was constructed and displayed high accuracy for predicting microvascular invasion.
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Affiliation(s)
- Yunfei Zhang
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Jiejun Chen
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Chun Yang
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Yongming Dai
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, 200032, China
| | - Mengsu Zeng
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
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Huang YC, Huang SM, Yeh JH, Chang TC, Tsan DL, Lin CY, Tu SJ. Utility of CT Radiomics and Delta Radiomics for Survival Evaluation in Locally Advanced Nasopharyngeal Carcinoma with Concurrent Chemoradiotherapy. Diagnostics (Basel) 2024; 14:941. [PMID: 38732355 PMCID: PMC11083477 DOI: 10.3390/diagnostics14090941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/23/2024] [Accepted: 04/26/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND A high incidence rate of nasopharyngeal carcinoma (NPC) has been observed in Southeast Asia compared to other parts of the world. Radiomics is a computational tool to predict outcomes and may be used as a prognostic biomarker for advanced NPC treated with concurrent chemoradiotherapy. Recently, radiomic analysis of the peripheral tumor microenvironment (TME), which is the region surrounding the gross tumor volume (GTV), has shown prognostic usefulness. In this study, not only was gross tumor volume (GTVt) analyzed but also tumor peripheral regions (GTVp) were explored in terms of the TME concept. Both radiomic features and delta radiomic features were analyzed using CT images acquired in a routine radiotherapy process. METHODS A total of 50 patients with NPC stages III, IVA, and IVB were enrolled between September 2004 and February 2014. Survival models were built using Cox regression with clinical factors (i.e., gender, age, overall stage, T stage, N stage, and treatment dose) and radiomic features. Radiomic features were extracted from GTVt and GTVp. GTVp was created surrounding GTVt for TME consideration. Furthermore, delta radiomics, which is the longitudinal change in quantitative radiomic features, was utilized for analysis. Finally, C-index values were computed using leave-one-out cross-validation (LOOCV) to evaluate the performances of all prognosis models. RESULTS Models were built for three different clinical outcomes, including overall survival (OS), local recurrence-free survival (LRFS), and progression-free survival (PFS). The range of the C-index in clinical factor models was (0.622, 0.729). All radiomics models, including delta radiomics models, were in the range of (0.718, 0.872). Among delta radiomics models, GTVt and GTVp were in the range of (0.833, 0.872) and (0.799, 0.834), respectively. CONCLUSIONS Radiomic analysis on the proximal region surrounding the gross tumor volume of advanced NPC patients for survival outcome evaluation was investigated, and preliminary positive results were obtained. Radiomic models and delta radiomic models demonstrated performance that was either superior to or comparable with that of conventional clinical models.
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Affiliation(s)
- Yen-Cho Huang
- Department of Radiation Oncology, Keelung Chang Gung Memorial Hospital, Keelung 20445, Taiwan; (Y.-C.H.); (S.-M.H.); (J.-H.Y.)
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Tao-Yuan 33302, Taiwan
| | - Shih-Ming Huang
- Department of Radiation Oncology, Keelung Chang Gung Memorial Hospital, Keelung 20445, Taiwan; (Y.-C.H.); (S.-M.H.); (J.-H.Y.)
| | - Jih-Hsiang Yeh
- Department of Radiation Oncology, Keelung Chang Gung Memorial Hospital, Keelung 20445, Taiwan; (Y.-C.H.); (S.-M.H.); (J.-H.Y.)
| | - Tung-Chieh Chang
- Department of Radiation Oncology, Linkuo Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan; (T.-C.C.); (D.-L.T.)
| | - Din-Li Tsan
- Department of Radiation Oncology, Linkuo Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan; (T.-C.C.); (D.-L.T.)
| | - Chien-Yu Lin
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Tao-Yuan 33302, Taiwan
- Department of Radiation Oncology, Linkuo Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan; (T.-C.C.); (D.-L.T.)
- Particle Physics and Beam Delivery Core Laboratory, Linkou Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan
| | - Shu-Ju Tu
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Tao-Yuan 33302, Taiwan
- Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan
- Department of Nuclear Medicine, Linkou Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan
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Sujit SJ, Aminu M, Karpinets TV, Chen P, Saad MB, Salehjahromi M, Boom JD, Qayati M, George JM, Allen H, Antonoff MB, Hong L, Hu X, Heeke S, Tran HT, Le X, Elamin YY, Altan M, Vokes NI, Sheshadri A, Lin J, Zhang J, Lu Y, Behrens C, Godoy MCB, Wu CC, Chang JY, Chung C, Jaffray DA, Wistuba II, Lee JJ, Vaporciyan AA, Gibbons DL, Heymach J, Zhang J, Cascone T, Wu J. Enhancing NSCLC recurrence prediction with PET/CT habitat imaging, ctDNA, and integrative radiogenomics-blood insights. Nat Commun 2024; 15:3152. [PMID: 38605064 PMCID: PMC11009351 DOI: 10.1038/s41467-024-47512-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 03/21/2024] [Indexed: 04/13/2024] Open
Abstract
While we recognize the prognostic importance of clinicopathological measures and circulating tumor DNA (ctDNA), the independent contribution of quantitative image markers to prognosis in non-small cell lung cancer (NSCLC) remains underexplored. In our multi-institutional study of 394 NSCLC patients, we utilize pre-treatment computed tomography (CT) and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) to establish a habitat imaging framework for assessing regional heterogeneity within individual tumors. This framework identifies three PET/CT subtypes, which maintain prognostic value after adjusting for clinicopathologic risk factors including tumor volume. Additionally, these subtypes complement ctDNA in predicting disease recurrence. Radiogenomics analysis unveil the molecular underpinnings of these imaging subtypes, highlighting downregulation in interferon alpha and gamma pathways in the high-risk subtype. In summary, our study demonstrates that these habitat imaging subtypes effectively stratify NSCLC patients based on their risk levels for disease recurrence after initial curative surgery or radiotherapy, providing valuable insights for personalized treatment approaches.
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Affiliation(s)
- Sheeba J Sujit
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Muhammad Aminu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tatiana V Karpinets
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Pingjun Chen
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Maliazurina B Saad
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Morteza Salehjahromi
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John D Boom
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Mohamed Qayati
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - James M George
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Haley Allen
- Natural Sciences, Rice University, Houston, TX, USA
| | - Mara B Antonoff
- Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Lingzhi Hong
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xin Hu
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Simon Heeke
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Hai T Tran
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xiuning Le
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yasir Y Elamin
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mehmet Altan
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Natalie I Vokes
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ajay Sheshadri
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Julie Lin
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jianhua Zhang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yang Lu
- Department of Nuclear Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Carmen Behrens
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Myrna C B Godoy
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Carol C Wu
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Joe Y Chang
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Institute of Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - David A Jaffray
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Institute of Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ignacio I Wistuba
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - J Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ara A Vaporciyan
- Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Don L Gibbons
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John Heymach
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jianjun Zhang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Lung Cancer Genomics Program, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Lung Cancer Interception Program, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tina Cascone
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jia Wu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- Institute of Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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Sagreiya H. Finding the Pieces to Treat the Whole: Using Radiomics to Identify Tumor Habitats. Radiol Artif Intell 2024; 6:e230547. [PMID: 38416038 PMCID: PMC10982906 DOI: 10.1148/ryai.230547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 12/14/2023] [Accepted: 12/18/2023] [Indexed: 02/29/2024]
Affiliation(s)
- Hersh Sagreiya
- From the Department of Radiology, Hospital of the University of
Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104-4283
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Fu L, Wang W, Lin L, Gao F, Yang J, Lv Y, Ge R, Wu M, Chen L, Liu A, Xin E, Yu J, Cheng J, Wang Y. Multitask prediction models for serous ovarian cancer by preoperative CT image assessments based on radiomics. Front Med (Lausanne) 2024; 11:1334062. [PMID: 38384418 PMCID: PMC10880444 DOI: 10.3389/fmed.2024.1334062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 01/11/2024] [Indexed: 02/23/2024] Open
Abstract
Objective High-grade serous ovarian cancer (HGSOC) has the highest mortality rate among female reproductive system tumors. Accurate preoperative assessment is crucial for treatment planning. This study aims to develop multitask prediction models for HGSOC using radiomics analysis based on preoperative CT images. Methods This study enrolled 112 patients diagnosed with HGSOC. Laboratory findings, including serum levels of CA125, HE-4, and NLR, were collected. Radiomic features were extracted from manually delineated ROI on CT images by two radiologists. Classification models were developed using selected optimal feature sets to predict R0 resection, lymph node invasion, and distant metastasis status. Model evaluation was conducted by quantifying receiver operating curves (ROC), calculating the area under the curve (AUC), De Long's test. Results The radiomics models applied to CT images demonstrated superior performance in the testing set compared to the clinical models. The area under the curve (AUC) values for the combined model in predicting R0 resection were 0.913 and 0.881 in the training and testing datasets, respectively. De Long's test indicated significant differences between the combined and clinical models in the testing set (p = 0.003). For predicting lymph node invasion, the AUCs of the combined model were 0.868 and 0.800 in the training and testing datasets, respectively. The results also revealed significant differences between the combined and clinical models in the testing set (p = 0.002). The combined model for predicting distant metastasis achieved AUCs of 0.872 and 0.796 in the training and test datasets, respectively. The combined model displayed excellent agreement between observed and predicted results in predicting R0 resection, while the radiomics model demonstrated better calibration than both the clinical model and combined model in predicting lymph node invasion and distant metastasis. The decision curve analysis (DCA) for predicting R0 resection favored the combined model over both the clinical and radiomics models, whereas for predicting lymph node invasion and distant metastasis, DCA favored the radiomics model over both the clinical model and combined model. Conclusion The identified radiomics signature holds potential value in preoperatively evaluating the R0, lymph node invasion and distant metastasis in patients with HGSC. The radiomics nomogram demonstrated the incremental value of clinical predictors for surgical outcome and metastasis estimation.
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Affiliation(s)
- Le Fu
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Wenjing Wang
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Lingling Lin
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Feng Gao
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jiani Yang
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yunyun Lv
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Ruiqiu Ge
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Meixuan Wu
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Lei Chen
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Aie Liu
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Enhui Xin
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Jianli Yu
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jiejun Cheng
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yu Wang
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
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Caloro E, Gnocchi G, Quarrella C, Ce M, Carrafiello G, Cellina M. Artificial Intelligence in Bone Metastasis Imaging: Recent Progresses from Diagnosis to Treatment - A Narrative Review. Crit Rev Oncog 2024; 29:77-90. [PMID: 38505883 DOI: 10.1615/critrevoncog.2023050470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
The introduction of artificial intelligence (AI) represents an actual revolution in the radiological field, including bone lesion imaging. Bone lesions are often detected both in healthy and oncological patients and the differential diagnosis can be challenging but decisive, because it affects the diagnostic and therapeutic process, especially in case of metastases. Several studies have already demonstrated how the integration of AI-based tools in the current clinical workflow could bring benefits to patients and to healthcare workers. AI technologies could help radiologists in early bone metastases detection, increasing the diagnostic accuracy and reducing the overdiagnosis and the number of unnecessary deeper investigations. In addition, radiomics and radiogenomics approaches could go beyond the qualitative features, visible to the human eyes, extrapolating cancer genomic and behavior information from imaging, in order to plan a targeted and personalized treatment. In this article, we want to provide a comprehensive summary of the most promising AI applications in bone metastasis imaging and their role from diagnosis to treatment and prognosis, including the analysis of future challenges and new perspectives.
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Affiliation(s)
- Elena Caloro
- Università degli studi di Milano, via Festa del Perdono, 7, 20122 Milan, Italy
| | - Giulia Gnocchi
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Cettina Quarrella
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Maurizio Ce
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy; Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, Università di Milano, 20122 Milan, Italy
| | - Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milan, Italy
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49
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Li J, Yang G, Zhang L. Artificial Intelligence Empowered Nuclear Medicine and Molecular Imaging in Cardiology: A State-of-the-Art Review. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:586-596. [PMID: 38223683 PMCID: PMC10781930 DOI: 10.1007/s43657-023-00137-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 10/13/2023] [Accepted: 10/16/2023] [Indexed: 01/16/2024]
Abstract
Nuclear medicine and molecular imaging plays a significant role in the detection and management of cardiovascular disease (CVD). With recent advancements in computer power and the availability of digital archives, artificial intelligence (AI) is rapidly gaining traction in the field of medical imaging, including nuclear medicine and molecular imaging. However, the complex and time-consuming workflow and interpretation involved in nuclear medicine and molecular imaging, limit their extensive utilization in clinical practice. To address this challenge, AI has emerged as a fundamental tool for enhancing the role of nuclear medicine and molecular imaging. It has shown promising applications in various crucial aspects of nuclear cardiology, such as optimizing imaging protocols, facilitating data processing, aiding in CVD diagnosis, risk classification and prognosis. In this review paper, we will introduce the key concepts of AI and provide an overview of its current progress in the field of nuclear cardiology. In addition, we will discuss future perspectives for AI in this domain.
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Affiliation(s)
- Junhao Li
- Department of Nuclear Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002 Jiangsu China
| | - Guifen Yang
- Department of Nuclear Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002 Jiangsu China
| | - Longjiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002 Jiangsu China
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50
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Jing F, Liu Y, Zhao X, Wang N, Dai M, Chen X, Zhang Z, Zhang J, Wang J, Wang Y. Baseline 18F-FDG PET/CT radiomics for prognosis prediction in diffuse large B cell lymphoma. EJNMMI Res 2023; 13:92. [PMID: 37884763 PMCID: PMC10603012 DOI: 10.1186/s13550-023-01047-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 10/22/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma in adults. Standard treatment includes chemoimmunotherapy with R-CHOP or similar regimens. Despite treatment advancements, many patients with DLBCL experience refractory disease or relapse. While baseline 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) parameters have shown promise in predicting survival, they may not fully capture lesion heterogeneity. This study aimed to assess the prognostic value of baseline 18F-FDG PET radiomics features in comparison with clinical factors and metabolic parameters for assessing 2-year progression-free survival (PFS) and 5-year overall survival (OS) in patients with DLBCL. RESULTS A total of 201 patients with DLBCL were enrolled in this study, and 1328 radiomics features were extracted. The radiomics signatures, clinical factors, and metabolic parameters showed significant prognostic value for individualized prognosis prediction in patients with DLBCL. Radiomics signatures showed the lowest Akaike information criterion (AIC) value and highest Harrell's concordance index (C-index) value in comparison with clinical factors and metabolic parameters for both PFS (AIC: 571.688 vs. 596.040 vs. 576.481; C-index: 0.732 vs. 0.658 vs. 0.702, respectively) and OS (AIC: 339.843 vs. 363.671 vs. 358.412; C-index: 0.759 vs. 0.667 vs. 0.659, respectively). Statistically significant differences were observed in the area under the curve (AUC) values between the radiomics signatures and clinical factors for both PFS (AUC: 0.768 vs. 0.681, P = 0.017) and OS (AUC: 0.767 vs. 0.667, P = 0.023). For OS, the AUC of the radiomics signatures were significantly higher than those of metabolic parameters (AUC: 0.767 vs. 0.688, P = 0.007). However, for PFS, no significant difference was observed between the radiomics signatures and metabolic parameters (AUC: 0.768 vs. 0.756, P = 0.654). The combined model and the best-performing individual model (radiomics signatures) alone showed no significant difference for both PFS (AUC: 0.784 vs. 0.768, P = 0.163) or OS (AUC: 0.772 vs. 0.767, P = 0.403). CONCLUSIONS Radiomics signatures derived from PET images showed the high predictive power for progression in patients with DLBCL. The combination of radiomics signatures, clinical factors, and metabolic parameters may not significantly improve predictive value beyond that of radiomics signatures alone.
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Affiliation(s)
- Fenglian Jing
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, 050011, Hebei, China
| | - Yunuan Liu
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, 050011, Hebei, China
| | - Xinming Zhao
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China.
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, 050011, Hebei, China.
| | - Na Wang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, 050011, Hebei, China
| | - Meng Dai
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, 050011, Hebei, China
| | - Xiaolin Chen
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, 050011, Hebei, China
| | - Zhaoqi Zhang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, 050011, Hebei, China
| | - Jingmian Zhang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, 050011, Hebei, China
| | - Jianfang Wang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, 050011, Hebei, China
| | - Yingchen Wang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, 050011, Hebei, China
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