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Broomand Lomer N, Nouri A, Singh R, Asgari S. Diagnostic performance of radiomics models for preoperative prediction of microsatellite instability status in endometrial cancer: a systematic review and meta-analysis. Abdom Radiol (NY) 2025:10.1007/s00261-025-04933-9. [PMID: 40195139 DOI: 10.1007/s00261-025-04933-9] [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: 02/13/2025] [Revised: 03/25/2025] [Accepted: 04/01/2025] [Indexed: 04/09/2025]
Abstract
PURPOSE Microsatellite instability (MSI), caused by defects in mismatch repair (MMR) genes, serves as a critical molecular biomarker with therapeutic implications for endometrial cancer (EC). This study aims to assess the diagnostic performance of radiomics as a non-invasive approach for predicting MSI status in EC. METHODS A systematic search across PubMed, Scopus, Embase, Web of Science, Cochrane library and Clinical Trials was conducted. Quality assessment was performed using QUADAS-2 and METRICS. Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) were computed using a bivariate model. Separate meta-analyses for radiomics and combined models were conducted. Subgroup analysis and sensitivity analysis were conducted to find potential sources of heterogeneity. Likelihood ratio scattergram was used to evaluate the clinical applicability. RESULTS A total of 9 studies (1650 patients) were included in the systematic review, with seven studies contributing to the meta-analysis of radiomics model and five for combined model. The pooled diagnostic performance of the radiomics model was as follows: sensitivity, 0.66; specificity, 0.89; PLR, 5.48; NLR, 0.43; DOR, 18.56; and AUC, 0.87. For combined model, the pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.58, 0.94, 7.37, 0.50, 16.43, and 0.85, respectively. Subgroup analysis of radiomics models revealed that studies employing non-linear classifiers achieved superior performance compared to those utilizing linear classifiers. CONCLUSION Radiomics showed promise as non-invasive tool for MSI prediction in EC, with potential clinical utility in guiding personalized treatments. However, further studies are required to validate these findings.
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Affiliation(s)
- Nima Broomand Lomer
- Department of Radiology, University of Pennsylvania, PA, 19104, Philadelphia, USA.
| | | | - Roshan Singh
- Department of Radiology, University of Pennsylvania, PA, 19104, Philadelphia, USA
| | - Sonia Asgari
- Islamic Azad University Rasht Branch, Rasht, Iran, Islamic Republic of
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Peng L, Zhang X, Zhu Y, Shi L, Ai K, Huang G, Ma W, Wei Z, Wang L, Ma Y, Wang L. T2WI and ADC radiomics combined with a nomogram based on clinicopathologic features to quantitatively predict microsatellite instability in colorectal cancer. Acad Radiol 2025; 32:1431-1450. [PMID: 39490321 DOI: 10.1016/j.acra.2024.10.002] [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: 05/14/2024] [Revised: 10/02/2024] [Accepted: 10/09/2024] [Indexed: 11/05/2024]
Abstract
RATIONALE AND OBJECTIVES Microsatellite instability (MSI) stratification can guide the clinical management of patients with colorectal cancer (CRC). This study aimed to establish a radiomics model for predicting the MSI status of patients with CRC before treatment. MATERIALS AND METHODS This retrospective study was performed on 366 patients diagnosed with CRC who underwent preoperative magnetic resonance imaging (MRI) and immunohistochemical staining between February 2016 and September 2023. The participants were divided randomly into training and testing cohorts in a 7:3 ratio. The tumor volume of interest (VOI) was manually delineated on T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences using 3D Slicer software, and radiomics features were extracted. Feature selection was performed using the least absolute shrinkage and selection operator method. A radiomics nomogram was developed using multiple logistic regression, and the predictive performance of the models was evaluated and compared using receiver operating characteristic curves. The calibration curve, clinical decision curve analysis (DCA) and clinical impact curve (CIC) were used to evaluate the clinical application value of the model. RESULTS The radiomics normogram combined with history of chronic enteritis, tumor location, MR-reported inflammatory response, D2-40, carcinoembryonic antigen, tumor protein 53, and monocyte was an excellent predictive tool. The area under the curve for the training and testing cohorts were 0.927 and 0.984, respectively. The DCA and CIC demonstrated favorable clinical application and net benefit. CONCLUSIONS A radiomics nomogram based on T2WI and ADC sequences and clinicopathologic features can effectively and noninvasively predict the MSI status in CRC. This approach helps clinicians in stratifying CRC patients and making clinical decisions for personalized treatment.
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Affiliation(s)
- Leping Peng
- Gansu University of Chinese Medicine, Lanzhou 730000, China
| | - Xiuling Zhang
- Gansu University of Chinese Medicine, Lanzhou 730000, China
| | - Yuanhui Zhu
- Department of Radiology, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Liuyan Shi
- Department of Radiology, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Kai Ai
- Department of Clinical and Technical Support, Philips Healthcare, Xi'an 710065, China
| | - Gang Huang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Wenting Ma
- Department of Radiology, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Zhaokun Wei
- Department of Radiology, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Ling Wang
- Department of Pathology, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Yaqiong Ma
- Department of Radiology, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Lili Wang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou 730000, China.
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Zhou M, Huang H, Bao D, Chen M, Lu F. Assessment of prognostic indicators and KRAS mutations in rectal cancer using a fractional-order calculus MR diffusion model: whole tumor histogram analysis. Abdom Radiol (NY) 2025; 50:569-578. [PMID: 39152230 DOI: 10.1007/s00261-024-04523-1] [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: 06/10/2024] [Revised: 08/04/2024] [Accepted: 08/10/2024] [Indexed: 08/19/2024]
Abstract
PURPOSE This study aims to explore the relationship between apparent diffusion coefficient (ADC) and fractional-order calculus (FROC)-specific parameters with prognostic indicators and Kirsten rat sarcoma viral oncogene homologue (KRAS) mutation status in rectal cancer. METHODS One hundred fifty-eight patients with rectal cancer were retrospectively enrolled. Histogram measurements of ADC, diffusion coefficient (D), intravoxel diffusion heterogeneity (β), and a microstructural quantity (μ) were estimated for the whole-tumor volume. The relationships between histogram measurements and prognostic indicators were evaluated. The efficacy of histogram measurements, both conducted singly and in conjunction, for evaluating different KRAS mutation statuses was also assessed. The performance of mean and median histogram measurements in evaluating various KRAS mutation statuses was assessed using Receiver Operating Characteristic (ROC) curve analysis. A p-value of less than 0.05 was considered statistically significant. RESULTS The histogram measurements of ADC, D, β, and μ differed significantly between well-moderately differentiated groups and poorly differentiated groups, T1-2 and T3-4 subgroups, lymph node metastasis (LNM)-negative and LNM-positive subgroups, extranodal extension (ENE)-negative and ENE-positive subgroups, tumor deposit (TD)-negative and TD-positive subgroups, and lymphovascular invasion (LVI)-negative and LVI-positive subgroups. The combination of Dmean, βmean, and μmean achieved the highest performance [The area under the ROC curve (AUC) = 0.904] in evaluating the KRAS mutation status. CONCLUSION When assessing parameters from the FROC model as potential biomarkers through histograms, they surpass traditional ADC values in distinguishing prognostic indicators and determining KRAS mutation status in rectal cancer.
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Affiliation(s)
- Mi Zhou
- Department of Radiology, Sichuan Provincial Orthpaedics Hospital, Chengdu, 610041, People's Republic of China.
| | - Hongyun Huang
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, People's Republic of China
| | - Deying Bao
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, People's Republic of China
| | - Meining Chen
- Department of MR Scientific Marketing, Siemens Healthineers, Shanghai, 200135, China
| | - Fulin Lu
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, People's Republic of China
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Li H, Zhuang Y, Yuan W, Gu Y, Dai X, Li M, Chen H, Zhou H. Radiomics in precision medicine for colorectal cancer: a bibliometric analysis (2013-2023). Front Oncol 2024; 14:1464104. [PMID: 39558950 PMCID: PMC11571149 DOI: 10.3389/fonc.2024.1464104] [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: 07/28/2024] [Accepted: 10/04/2024] [Indexed: 11/20/2024] Open
Abstract
Background The incidence and mortality of colorectal cancer (CRC) have been rising steadily. Early diagnosis and precise treatment are essential for improving patient survival outcomes. Over the past decade, the integration of artificial intelligence (AI) and medical imaging technologies has positioned radiomics as a critical area of research in the diagnosis, treatment, and prognosis of CRC. Methods We conducted a comprehensive review of CRC-related radiomics literature published between 1 January 2013 and 31 December 2023 using the Web of Science Core Collection database. Bibliometric tools such as Bibliometrix, VOSviewer, and CiteSpace were employed to perform an in-depth bibliometric analysis. Results Our search yielded 1,226 publications, revealing a consistent annual growth in CRC radiomics research, with a significant rise after 2019. China led in publication volume (406 papers), followed by the United States (263 papers), whereas the United States dominated in citation numbers. Notable institutions included General Electric, Harvard University, University of London, Maastricht University, and the Chinese Academy of Sciences. Prominent researchers in this field are Tian J from the Chinese Academy of Sciences, with the highest publication count, and Ganeshan B from the University of London, with the most citations. Journals leading in publication and citation counts are Frontiers in Oncology and Radiology. Keyword and citation analysis identified deep learning, texture analysis, rectal cancer, image analysis, and management as prevailing research themes. Additionally, recent trends indicate the growing importance of AI and multi-omics integration, with a focus on improving precision medicine applications in CRC. Emerging keywords such as deep learning and AI have shown rapid growth in citation bursts over the past 3 years, reflecting a shift toward more advanced technological applications. Conclusions Radiomics plays a crucial role in the clinical management of CRC, providing valuable insights for precision medicine. It significantly contributes to predicting molecular biomarkers, assessing tumor aggressiveness, and monitoring treatment efficacy. Future research should prioritize advancing AI algorithms, enhancing multi-omics data integration, and further expanding radiomics applications in CRC precision medicine.
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Affiliation(s)
- Hao Li
- Department of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, The First Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Yupei Zhuang
- Department of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, The First Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Weichen Yuan
- Department of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, The First Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Yutian Gu
- Department of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, The First Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Xinyan Dai
- Department of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, The First Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Muhan Li
- Department of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, The First Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Haibin Chen
- Science and Technology Department, Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, Nanjing University of Chinese Medicine, Nanjing, China
| | - Hongguang Zhou
- Department of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, The First Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
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Curcean S, Curcean A, Martin D, Fekete Z, Irimie A, Muntean AS, Caraiani C. The Role of Predictive and Prognostic MRI-Based Biomarkers in the Era of Total Neoadjuvant Treatment in Rectal Cancer. Cancers (Basel) 2024; 16:3111. [PMID: 39272969 PMCID: PMC11394290 DOI: 10.3390/cancers16173111] [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: 08/13/2024] [Revised: 09/02/2024] [Accepted: 09/06/2024] [Indexed: 09/15/2024] Open
Abstract
The role of magnetic resonance imaging (MRI) in rectal cancer management has significantly increased over the last decade, in line with more personalized treatment approaches. Total neoadjuvant treatment (TNT) plays a pivotal role in the shift from traditional surgical approach to non-surgical approaches such as 'watch-and-wait'. MRI plays a central role in this evolving landscape, providing essential morphological and functional data that support clinical decision-making. Key MRI-based biomarkers, including circumferential resection margin (CRM), extramural venous invasion (EMVI), tumour deposits, diffusion-weighted imaging (DWI), and MRI tumour regression grade (mrTRG), have proven valuable for staging, response assessment, and patient prognosis. Functional imaging techniques, such as dynamic contrast-enhanced MRI (DCE-MRI), alongside emerging biomarkers derived from radiomics and artificial intelligence (AI) have the potential to transform rectal cancer management offering data that enhance T and N staging, histopathological characterization, prediction of treatment response, recurrence detection, and identification of genomic features. This review outlines validated morphological and functional MRI-derived biomarkers with both prognostic and predictive significance, while also exploring the potential of radiomics and artificial intelligence in rectal cancer management. Furthermore, we discuss the role of rectal MRI in the 'watch-and-wait' approach, highlighting important practical aspects in selecting patients for non-surgical management.
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Affiliation(s)
- Sebastian Curcean
- Department of Radiation Oncology, Iuliu Hatieganu University of Medicine and Pharmacy, 8 Victor Babes Street, 400012 Cluj-Napoca, Romania
- Department of Radiation Oncology, 'Prof. Dr. Ion Chiricuta' Oncology Institute, 34-36 Republicii Street, 400015 Cluj-Napoca, Romania
| | - Andra Curcean
- Department of Imaging, Affidea Center, 15c Ciresilor Street, 400487 Cluj-Napoca, Romania
| | - Daniela Martin
- Department of Radiation Oncology, 'Prof. Dr. Ion Chiricuta' Oncology Institute, 34-36 Republicii Street, 400015 Cluj-Napoca, Romania
| | - Zsolt Fekete
- Department of Radiation Oncology, Iuliu Hatieganu University of Medicine and Pharmacy, 8 Victor Babes Street, 400012 Cluj-Napoca, Romania
- Department of Radiation Oncology, 'Prof. Dr. Ion Chiricuta' Oncology Institute, 34-36 Republicii Street, 400015 Cluj-Napoca, Romania
| | - Alexandru Irimie
- Department of Oncological Surgery and Gynecological Oncology, Iuliu Hatieganu University of Medicine and Pharmacy, 8 Victor Babes Street, 400012 Cluj-Napoca, Romania
- Department of Oncological Surgery, 'Prof. Dr. Ion Chiricuta' Oncology Institute, 34-36 Republicii Street, 400015 Cluj-Napoca, Romania
| | - Alina-Simona Muntean
- Department of Radiation Oncology, 'Prof. Dr. Ion Chiricuta' Oncology Institute, 34-36 Republicii Street, 400015 Cluj-Napoca, Romania
| | - Cosmin Caraiani
- Department of Medical Imaging and Nuclear Medicine, Iuliu Hațieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
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Wu S, Wang N, Ao W, Hu J, Xu W, Mao G. Deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram for predicting Ki-67 expression in rectal cancer. Abdom Radiol (NY) 2024; 49:3003-3014. [PMID: 38489038 DOI: 10.1007/s00261-024-04232-9] [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: 12/21/2023] [Revised: 02/01/2024] [Accepted: 02/01/2024] [Indexed: 03/17/2024]
Abstract
PURPOSE To explore the value of deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram in predicting the Ki-67 expression in rectal cancer. METHODS The data of 491 patients with rectal cancer from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. They were categorized into high- and low-expression group based on postoperative pathological Ki-67 expression. Each patient's mp-MRI data were analyzed to extract and select the most relevant features of deep learning, and a deep learning model was constructed. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a nomogram for the prediction of Ki-67 expression. The performance characteristics of the DL-model, clinical model, and nomogram were assessed using ROCs, calibration curve, decision curve, and clinical impact curve analysis. RESULTS The strongest deep learning features were extracted and screened from mp-MRI data. Two independent predictive factors, namely Magnetic Resonance Imaging T (mrT) staging and differentiation degree, were identified through clinical feature selection. Three models were constructed: a deep learning (DL)-model, a clinical model, and a nomogram. The AUCs of clinical model in the training, internal validation, and external validation set were 0.69, 0.78, and 0.67, respectively. The AUCs of the deep model and nomogram ranged from 0.88 to 0.98. The prediction performance of the deep learning model and nomogram was significantly better than the clinical model (P < 0.001). CONCLUSION The nomogram based on deep learning can help clinicians accurately and conveniently predict the expression status of Ki-67 in rectal cancer.
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Affiliation(s)
- Sikai Wu
- Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Neng Wang
- Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Weiqun Ao
- Department of Radiology, Tongde Hospital of Zhejiang Province, No. 234, Gucui Road, Hangzhou, 310012, Zhejiang, China
| | - Jinwen Hu
- Department of Radiology, Putuo People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Wenjie Xu
- Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Guoqun Mao
- Department of Radiology, Tongde Hospital of Zhejiang Province, No. 234, Gucui Road, Hangzhou, 310012, Zhejiang, China.
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Cai M, Zhao L, Qiang Y, Wang L, Zhao J. CHNet: A multi-task global-local Collaborative Hybrid Network for KRAS mutation status prediction in colorectal cancer. Artif Intell Med 2024; 155:102931. [PMID: 39094228 DOI: 10.1016/j.artmed.2024.102931] [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: 09/25/2023] [Revised: 06/29/2024] [Accepted: 07/03/2024] [Indexed: 08/04/2024]
Abstract
Accurate prediction of Kirsten rat sarcoma (KRAS) mutation status is crucial for personalized treatment of advanced colorectal cancer patients. However, despite the excellent performance of deep learning models in certain aspects, they often overlook the synergistic promotion among multiple tasks and the consideration of both global and local information, which can significantly reduce prediction accuracy. To address these issues, this paper proposes an innovative method called the Multi-task Global-Local Collaborative Hybrid Network (CHNet) aimed at more accurately predicting patients' KRAS mutation status. CHNet consists of two branches that can extract global and local features from segmentation and classification tasks, respectively, and exchange complementary information to collaborate in executing these tasks. Within the two branches, we have designed a Channel-wise Hybrid Transformer (CHT) and a Spatial-wise Hybrid Transformer (SHT). These transformers integrate the advantages of both Transformer and CNN, employing cascaded hybrid attention and convolution to capture global and local information from the two tasks. Additionally, we have created an Adaptive Collaborative Attention (ACA) module to facilitate the collaborative fusion of segmentation and classification features through guidance. Furthermore, we introduce a novel Class Activation Map (CAM) loss to encourage CHNet to learn complementary information between the two tasks. We evaluate CHNet on the T2-weighted MRI dataset, and achieve an accuracy of 88.93% in KRAS mutation status prediction, which outperforms the performance of representative KRAS mutation status prediction methods. The results suggest that our CHNet can more accurately predict KRAS mutation status in patients via a multi-task collaborative facilitation and considering global-local information way, which can assist doctors in formulating more personalized treatment strategies for patients.
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Affiliation(s)
- Meiling Cai
- College of computer science and technology (College of data science), Taiyuan University of Technology, Taiyuan, 030024, Shanxi, China.
| | - Lin Zhao
- Southeast University, Nanjing, 210037, Jiangsu, China
| | - Yan Qiang
- College of computer science and technology (College of data science), Taiyuan University of Technology, Taiyuan, 030024, Shanxi, China
| | - Long Wang
- Jinzhong College of Information, Jinzhong, 030800, Shanxi, China
| | - Juanjuan Zhao
- College of computer science and technology (College of data science), Taiyuan University of Technology, Taiyuan, 030024, Shanxi, China.
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Xiang F, Meng QT, Deng JJ, Wang J, Liang XY, Liu XY, Yan S. A deep learning model based on contrast-enhanced computed tomography for differential diagnosis of gallbladder carcinoma. Hepatobiliary Pancreat Dis Int 2024; 23:376-384. [PMID: 37080813 DOI: 10.1016/j.hbpd.2023.04.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 04/07/2023] [Indexed: 04/22/2023]
Abstract
BACKGROUND Gallbladder carcinoma (GBC) is highly malignant, and its early diagnosis remains difficult. This study aimed to develop a deep learning model based on contrast-enhanced computed tomography (CT) images to assist radiologists in identifying GBC. METHODS We retrospectively enrolled 278 patients with gallbladder lesions (> 10 mm) who underwent contrast-enhanced CT and cholecystectomy and divided them into the training (n = 194) and validation (n = 84) datasets. The deep learning model was developed based on ResNet50 network. Radiomics and clinical models were built based on support vector machine (SVM) method. We comprehensively compared the performance of deep learning, radiomics, clinical models, and three radiologists. RESULTS Three radiomics features including LoG_3.0 gray-level size zone matrix zone variance, HHL first-order kurtosis, and LHL gray-level co-occurrence matrix dependence variance were significantly different between benign gallbladder lesions and GBC, and were selected for developing radiomics model. Multivariate regression analysis revealed that age ≥ 65 years [odds ratios (OR) = 4.4, 95% confidence interval (CI): 2.1-9.1, P < 0.001], lesion size (OR = 2.6, 95% CI: 1.6-4.1, P < 0.001), and CA-19-9 > 37 U/mL (OR = 4.0, 95% CI: 1.6-10.0, P = 0.003) were significant clinical risk factors of GBC. The deep learning model achieved the area under the receiver operating characteristic curve (AUC) values of 0.864 (95% CI: 0.814-0.915) and 0.857 (95% CI: 0.773-0.942) in the training and validation datasets, which were comparable with radiomics, clinical models and three radiologists. The sensitivity of deep learning model was the highest both in the training [90% (95% CI: 82%-96%)] and validation [85% (95% CI: 68%-95%)] datasets. CONCLUSIONS The deep learning model may be a useful tool for radiologists to distinguish between GBC and benign gallbladder lesions.
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Affiliation(s)
- Fei Xiang
- Department of Hepatobiliary Pancreatic Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Qing-Tao Meng
- Department of Radiology, Affiliated Chuzhou First People's Hospital, Anhui Medical University, Chuzhou 239000, China
| | - Jing-Jing Deng
- Department of Radiology, Affiliated Chuzhou First People's Hospital, Anhui Medical University, Chuzhou 239000, China
| | - Jie Wang
- Department of Radiology, Affiliated Chuzhou First People's Hospital, Anhui Medical University, Chuzhou 239000, China
| | - Xiao-Yuan Liang
- Department of Hepatobiliary Pancreatic Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Xing-Yu Liu
- Department of Hepatobiliary Pancreatic Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Sheng Yan
- Department of Hepatobiliary Pancreatic Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China.
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Hu T, Gong J, Sun Y, Li M, Cai C, Li X, Cui Y, Zhang X, Tong T. Magnetic resonance imaging-based radiomics analysis for prediction of treatment response to neoadjuvant chemoradiotherapy and clinical outcome in patients with locally advanced rectal cancer: A large multicentric and validated study. MedComm (Beijing) 2024; 5:e609. [PMID: 38911065 PMCID: PMC11190348 DOI: 10.1002/mco2.609] [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: 11/12/2023] [Revised: 04/18/2024] [Accepted: 05/16/2024] [Indexed: 06/25/2024] Open
Abstract
Our study investigated whether magnetic resonance imaging (MRI)-based radiomics features could predict good response (GR) to neoadjuvant chemoradiotherapy (nCRT) and clinical outcome in patients with locally advanced rectal cancer (LARC). Radiomics features were extracted from the T2 weighted (T2W) and Apparent diffusion coefficient (ADC) images of 1070 LARC patients retrospectively and prospectively recruited from three hospitals. To create radiomic models for GR prediction, three classifications were utilized. The radiomic model with the best performance was integrated with important clinical MRI features to create the combined model. Finally, two clinical MRI features and ten radiomic features were chosen for GR prediction. The combined model, constructed with the tumor size, MR-detected extramural venous invasion, and radiomic signature generated by Support Vector Machine (SVM), showed promising discrimination of GR, with area under the curves of 0.799 (95% CI, 0.760-0.838), 0.797 (95% CI, 0.733-0.860), 0.754 (95% CI, 0.678-0.829), and 0.727 (95% CI, 0.641-0.813) in the training and three validation datasets, respectively. Decision curve analysis verified the clinical usefulness. Furthermore, according to Kaplan-Meier curves, patients with a high likelihood of GR as determined by the combined model had better disease-free survival than those with a low probability. This radiomics model was developed based on large-sample size, multicenter datasets, and prospective validation with high radiomics quality score, and also had clinical utility.
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Affiliation(s)
- TingDan Hu
- Department of RadiologyFudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Jing Gong
- Department of RadiologyFudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - YiQun Sun
- Department of RadiologyFudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - MengLei Li
- Department of RadiologyFudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - ChongPeng Cai
- Department of RadiologyFudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - XinXiang Li
- Department of Colorectal SurgeryFudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - YanFen Cui
- Department of RadiologyShanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical UniversityTaiyuanChina
| | - XiaoYan Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing)Department of Radiology, Peking University Cancer Hospital and InstituteBeijingChina
| | - Tong Tong
- Department of RadiologyFudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan UniversityShanghaiChina
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He J, Wang SX, Liu P. Machine learning in predicting pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer using MRI: a systematic review and meta-analysis. Br J Radiol 2024; 97:1243-1254. [PMID: 38730550 PMCID: PMC11186567 DOI: 10.1093/bjr/tqae098] [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: 09/13/2023] [Revised: 01/15/2024] [Accepted: 05/07/2024] [Indexed: 05/13/2024] Open
Abstract
OBJECTIVES To evaluate the performance of machine learning models in predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer using magnetic resonance imaging. METHODS We searched PubMed, Embase, Cochrane Library, and Web of Science for studies published before March 2024. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) was used to assess the methodological quality of the included studies, random-effects models were used to calculate sensitivity and specificity, I2 values were used for heterogeneity measurements, and subgroup analyses were carried out to detect potential sources of heterogeneity. RESULTS A total of 1699 patients from 24 studies were included. For machine learning models in predicting pCR to nCRT, the meta-analysis calculated a pooled area under the curve (AUC) of 0.91 (95% CI, 0.88-0.93), pooled sensitivity of 0.83 (95% CI, 0.74-0.89), and pooled specificity of 0.86 (95% CI, 0.80-0.91). We investigated 6 studies that mainly contributed to heterogeneity. After performing meta-analysis again excluding these 6 studies, the heterogeneity was significantly reduced. In subgroup analysis, the pooled AUC of the deep-learning model was 0.93 and 0.89 for the traditional statistical model; the pooled AUC of studies that used diffusion-weighted imaging (DWI) was 0.90 and 0.92 in studies that did not use DWI; the pooled AUC of studies conducted in China was 0.93, and was 0.83 in studies conducted in other countries. CONCLUSIONS This systematic study showed that machine learning has promising potential in predicting pCR to nCRT in patients with locally advanced rectal cancer. Compared to traditional machine learning models, although deep-learning-based studies are less predominant and more heterogeneous, they are able to obtain higher AUC. ADVANCES IN KNOWLEDGE Compared to traditional machine learning models, deep-learning-based studies are able to obtain higher AUC, although they are less predominant and more heterogeneous. Together with clinical information, machine learning-based models may bring us closer towards precision medicine.
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Affiliation(s)
- Jia He
- Department of Radiology, The First Affiliated Hospital of Hunan Normal University, Hunan Provincial People’s Hospital, Changsha 410002, China
| | | | - Peng Liu
- Department of Radiology, The First Affiliated Hospital of Hunan Normal University, Hunan Provincial People’s Hospital, Changsha 410002, China
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Ma Y, Guo Y, Cui W, Liu J, Li Y, Wang Y, Qiang Y. SG-Transunet: A segmentation-guided Transformer U-Net model for KRAS gene mutation status identification in colorectal cancer. Comput Biol Med 2024; 173:108293. [PMID: 38574528 DOI: 10.1016/j.compbiomed.2024.108293] [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: 12/19/2023] [Revised: 02/28/2024] [Accepted: 03/12/2024] [Indexed: 04/06/2024]
Abstract
Accurately identifying the Kirsten rat sarcoma virus (KRAS) gene mutation status in colorectal cancer (CRC) patients can assist doctors in deciding whether to use specific targeted drugs for treatment. Although deep learning methods are popular, they are often affected by redundant features from non-lesion areas. Moreover, existing methods commonly extract spatial features from imaging data, which neglect important frequency domain features and may degrade the performance of KRAS gene mutation status identification. To address this deficiency, we propose a segmentation-guided Transformer U-Net (SG-Transunet) model for KRAS gene mutation status identification in CRC. Integrating the strength of convolutional neural networks (CNNs) and Transformers, SG-Transunet offers a unique approach for both lesion segmentation and KRAS mutation status identification. Specifically, for precise lesion localization, we employ an encoder-decoder to obtain segmentation results and guide the KRAS gene mutation status identification task. Subsequently, a frequency domain supplement block is designed to capture frequency domain features, integrating it with high-level spatial features extracted in the encoding path to derive advanced spatial-frequency domain features. Furthermore, we introduce a pre-trained Xception block to mitigate the risk of overfitting associated with small-scale datasets. Following this, an aggregate attention module is devised to consolidate spatial-frequency domain features with global information extracted by the Transformer at shallow and deep levels, thereby enhancing feature discriminability. Finally, we propose a mutual-constrained loss function that simultaneously constrains the segmentation mask acquisition and gene status identification process. Experimental results demonstrate the superior performance of SG-Transunet over state-of-the-art methods in discriminating KRAS gene mutation status.
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Affiliation(s)
- Yulan Ma
- Department of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China
| | - Yuzhu Guo
- Department of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China
| | - Weigang Cui
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
| | - Jingyu Liu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yang Li
- Department of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China.
| | - Yingsen Wang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Yan Qiang
- School of Software, North University of China, Taiyuan, China; College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.
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12
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Bian X, Sun Q, Wang M, Dong H, Dai X, Zhang L, Fan G, Chen G. Preoperative prediction of microsatellite instability status in colorectal cancer based on a multiphasic enhanced CT radiomics nomogram model. BMC Med Imaging 2024; 24:77. [PMID: 38566000 PMCID: PMC10988858 DOI: 10.1186/s12880-024-01252-1] [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: 07/31/2023] [Accepted: 03/18/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND To investigate the value of a nomogram model based on the combination of clinical-CT features and multiphasic enhanced CT radiomics for the preoperative prediction of the microsatellite instability (MSI) status in colorectal cancer (CRC) patients. METHODS A total of 347 patients with a pathological diagnosis of colorectal adenocarcinoma, including 276 microsatellite stabilized (MSS) patients and 71 MSI patients (243 training and 104 testing), were included. Univariate and multivariate regression analyses were used to identify the clinical-CT features of CRC patients linked with MSI status to build a clinical model. Radiomics features were extracted from arterial phase (AP), venous phase (VP), and delayed phase (DP) CT images. Different radiomics models for the single phase and multiphase (three-phase combination) were developed to determine the optimal phase. A nomogram model that combines clinical-CT features and the optimal phasic radscore was also created. RESULTS Platelet (PLT), systemic immune inflammation index (SII), tumour location, enhancement pattern, and AP contrast ratio (ACR) were independent predictors of MSI status in CRC patients. Among the AP, VP, DP, and three-phase combination models, the three-phase combination model was selected as the best radiomics model. The best MSI prediction efficacy was demonstrated by the nomogram model built from the combination of clinical-CT features and the three-phase combination model, with AUCs of 0.894 and 0.839 in the training and testing datasets, respectively. CONCLUSION The nomogram model based on the combination of clinical-CT features and three-phase combination radiomics features can be used as an auxiliary tool for the preoperative prediction of the MSI status in CRC patients.
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Affiliation(s)
- Xuelian Bian
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, 215004, Suzhou, Jiangsu, China
| | - Qi Sun
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, 215004, Suzhou, Jiangsu, China
| | - Mi Wang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, 215004, Suzhou, Jiangsu, China
| | - Hanyun Dong
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, 215004, Suzhou, Jiangsu, China
| | - Xiaoxiao Dai
- Department of Pathlogy, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, 215004, Suzhou, Jiangsu, China
| | - Liyuan Zhang
- Department of Radiotherapy, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, 215004, Suzhou, Jiangsu, China
| | - Guohua Fan
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, 215004, Suzhou, Jiangsu, China
| | - Guangqiang Chen
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, 215004, Suzhou, Jiangsu, China.
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13
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Shur JD, Qiu S, Johnston E, Tait D, Fotiadis N, Kontovounisios C, Rasheed S, Tekkis P, Riddell A, Koh DM. Multimodality Imaging to Direct Management of Primary and Recurrent Rectal Adenocarcinoma Beyond the Total Mesorectal Excision Plane. Radiol Imaging Cancer 2024; 6:e230077. [PMID: 38363197 PMCID: PMC10988347 DOI: 10.1148/rycan.230077] [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: 06/01/2023] [Revised: 10/11/2023] [Accepted: 01/10/2024] [Indexed: 02/17/2024]
Abstract
Rectal tumors extending beyond the total mesorectal excision (TME) plane (beyond-TME) require particular multidisciplinary expertise and oncologic considerations when planning treatment. Imaging is used at all stages of the pathway, such as local tumor staging/restaging, creating an imaging-based "roadmap" to plan surgery for optimal tumor clearance, identifying treatment-related complications, which may be suitable for radiology-guided intervention, and to detect recurrent or metastatic disease, which may be suitable for radiology-guided ablative therapies. Beyond-TME and exenterative surgery have gained acceptance as potentially curative procedures for advanced tumors. Understanding the role, techniques, and pitfalls of current imaging techniques is important for both radiologists involved in the treatment of these patients and general radiologists who may encounter patients undergoing surveillance or patients presenting with surgical complications or intercurrent abdominal pathology. This review aims to outline the current and emerging roles of imaging in patients with beyond-TME and recurrent rectal malignancy, focusing on practical tips for image interpretation and surgical planning in the beyond-TME setting. Keywords: Abdomen/GI, Rectum, Oncology © RSNA, 2024.
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Affiliation(s)
- Joshua D. Shur
- From the Royal Marsden Hospital NHS Foundation Trust, Downs Road,
Sutton SM2 5PT, England (J.D.S., S.Q., E.J., D.T., N.F., C.K., S.R.,
P.T., A.R., D.M.K.); and Institute of Cancer Research, Sutton, England (E.J.,
N.F., D.M.K.)
| | - Sheng Qiu
- From the Royal Marsden Hospital NHS Foundation Trust, Downs Road,
Sutton SM2 5PT, England (J.D.S., S.Q., E.J., D.T., N.F., C.K., S.R.,
P.T., A.R., D.M.K.); and Institute of Cancer Research, Sutton, England (E.J.,
N.F., D.M.K.)
| | - Edward Johnston
- From the Royal Marsden Hospital NHS Foundation Trust, Downs Road,
Sutton SM2 5PT, England (J.D.S., S.Q., E.J., D.T., N.F., C.K., S.R.,
P.T., A.R., D.M.K.); and Institute of Cancer Research, Sutton, England (E.J.,
N.F., D.M.K.)
| | - Diana Tait
- From the Royal Marsden Hospital NHS Foundation Trust, Downs Road,
Sutton SM2 5PT, England (J.D.S., S.Q., E.J., D.T., N.F., C.K., S.R.,
P.T., A.R., D.M.K.); and Institute of Cancer Research, Sutton, England (E.J.,
N.F., D.M.K.)
| | - Nicos Fotiadis
- From the Royal Marsden Hospital NHS Foundation Trust, Downs Road,
Sutton SM2 5PT, England (J.D.S., S.Q., E.J., D.T., N.F., C.K., S.R.,
P.T., A.R., D.M.K.); and Institute of Cancer Research, Sutton, England (E.J.,
N.F., D.M.K.)
| | - Christos Kontovounisios
- From the Royal Marsden Hospital NHS Foundation Trust, Downs Road,
Sutton SM2 5PT, England (J.D.S., S.Q., E.J., D.T., N.F., C.K., S.R.,
P.T., A.R., D.M.K.); and Institute of Cancer Research, Sutton, England (E.J.,
N.F., D.M.K.)
| | - Shahnawaz Rasheed
- From the Royal Marsden Hospital NHS Foundation Trust, Downs Road,
Sutton SM2 5PT, England (J.D.S., S.Q., E.J., D.T., N.F., C.K., S.R.,
P.T., A.R., D.M.K.); and Institute of Cancer Research, Sutton, England (E.J.,
N.F., D.M.K.)
| | - Paris Tekkis
- From the Royal Marsden Hospital NHS Foundation Trust, Downs Road,
Sutton SM2 5PT, England (J.D.S., S.Q., E.J., D.T., N.F., C.K., S.R.,
P.T., A.R., D.M.K.); and Institute of Cancer Research, Sutton, England (E.J.,
N.F., D.M.K.)
| | - Angela Riddell
- From the Royal Marsden Hospital NHS Foundation Trust, Downs Road,
Sutton SM2 5PT, England (J.D.S., S.Q., E.J., D.T., N.F., C.K., S.R.,
P.T., A.R., D.M.K.); and Institute of Cancer Research, Sutton, England (E.J.,
N.F., D.M.K.)
| | - Dow-Mu Koh
- From the Royal Marsden Hospital NHS Foundation Trust, Downs Road,
Sutton SM2 5PT, England (J.D.S., S.Q., E.J., D.T., N.F., C.K., S.R.,
P.T., A.R., D.M.K.); and Institute of Cancer Research, Sutton, England (E.J.,
N.F., D.M.K.)
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Mao J, Ye W, Ma W, Liu J, Zhong W, Yuan H, Li T, Guan L, Wu D. Prediction by a multiparametric magnetic resonance imaging-based radiomics signature model of disease-free survival in patients with rectal cancer treated by surgery. Front Oncol 2024; 14:1255438. [PMID: 38454930 PMCID: PMC10917947 DOI: 10.3389/fonc.2024.1255438] [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: 07/08/2023] [Accepted: 01/31/2024] [Indexed: 03/09/2024] Open
Abstract
Objective The aim of this study was to assess the ability of a multiparametric magnetic resonance imaging (MRI)-based radiomics signature model to predict disease-free survival (DFS) in patients with rectal cancer treated by surgery. Materials and methods We evaluated data of 194 patients with rectal cancer who had undergone radical surgery between April 2016 and September 2021. The mean age of all patients was 62.6 ± 9.7 years (range: 37-86 years). The study endpoint was DFS and 1132 radiomic features were extracted from preoperative MRIs, including contrast-enhanced T1- and T2-weighted imaging and apparent diffusion coefficient values. The study patients were randomly allocated to training (n=97) and validation cohorts (n=97) in a ratio of 5:5. A multivariable Cox regression model was used to generate a radiomics signature (rad score). The associations of rad score with DFS were evaluated using Kaplan-Meier analysis. Three models, namely a radiomics nomogram, radiomics signature, and clinical model, were compared using the Akaike information criterion. Result The rad score, which was composed of four MRI features, stratified rectal cancer patients into low- and high-risk groups and was associated with DFS in both the training (p = 0.0026) and validation sets (p = 0.036). Moreover, a radiomics nomogram model that combined rad score and independent clinical risk factors performed better (Harrell concordance index [C-index] =0.77) than a purely radiomics signature (C-index=0.73) or clinical model (C-index=0.70). Conclusion An MRI radiomics model that incorporates a radiomics signature and clinicopathological factors more accurately predicts DFS than does a clinical model in patients with rectal cancer.
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Affiliation(s)
- Jiwei Mao
- Department of Radiation Oncology, Shaoxing People’s Hospital, Shaoxing, China
| | - Wanli Ye
- Department of Radiation Oncology, Shaoxing People’s Hospital, Shaoxing, China
| | - Weili Ma
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China
| | - Jianjiang Liu
- Department of Radiation Oncology, Shaoxing People’s Hospital, Shaoxing, China
| | - Wangyan Zhong
- Department of Radiation Oncology, Shaoxing People’s Hospital, Shaoxing, China
| | - Hang Yuan
- Department of Radiation Oncology, Shaoxing People’s Hospital, Shaoxing, China
| | - Ting Li
- Department of Radiation Oncology, Shaoxing People’s Hospital, Shaoxing, China
| | - Le Guan
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China
| | - Dongping Wu
- Department of Radiation Oncology, Shaoxing People’s Hospital, Shaoxing, China
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15
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Zhao H, Su Y, Wang Y, Lyu Z, Xu P, Gu W, Tian L, Fu P. Using tumor habitat-derived radiomic analysis during pretreatment 18F-FDG PET for predicting KRAS/NRAS/BRAF mutations in colorectal cancer. Cancer Imaging 2024; 24:26. [PMID: 38342905 PMCID: PMC10860234 DOI: 10.1186/s40644-024-00670-2] [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: 08/14/2023] [Accepted: 01/29/2024] [Indexed: 02/13/2024] Open
Abstract
BACKGROUND To investigate the association between Kirsten rat sarcoma viral oncogene homolog (KRAS) / neuroblastoma rat sarcoma viral oncogene homolog (NRAS) /v-raf murine sarcoma viral oncogene homolog B (BRAF) mutations and the tumor habitat-derived radiomic features obtained during pretreatment 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) in patients with colorectal cancer (CRC). METHODS We retrospectively enrolled 62 patients with CRC who had undergone 18F-FDG PET/computed tomography from January 2017 to July 2022 before the initiation of therapy. The patients were randomly split into training and validation cohorts with a ratio of 6:4. The whole tumor region radiomic features, habitat-derived radiomic features, and metabolic parameters were extracted from 18F-FDG PET images. After reducing the feature dimension and selecting meaningful features, we constructed a hierarchical model of KRAS/NRAS/BRAF mutations by using the support vector machine. The convergence of the model was evaluated by using learning curve, and its performance was assessed based on the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis. The SHapley Additive exPlanation was used to interpret the contributions of various features to predictions of the model. RESULTS The model constructed by using habitat-derived radiomic features had adequate predictive power with respect to KRAS/NRAS/BRAF mutations, with an AUC of 0.759 (95% CI: 0.585-0.909) on the training cohort and that of 0.701 (95% CI: 0.468-0.916) on the validation cohort. The model exhibited good convergence, suitable calibration, and clinical application value. The results of the SHapley Additive explanation showed that the peritumoral habitat and a high_metabolism habitat had the greatest impact on predictions of the model. No meaningful whole tumor region radiomic features or metabolic parameters were retained during feature selection. CONCLUSION The habitat-derived radiomic features were found to be helpful in stratifying the status of KRAS/NRAS/BRAF in CRC patients. The approach proposed here has significant implications for adjuvant treatment decisions in patients with CRC, and needs to be further validated on a larger prospective cohort.
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Affiliation(s)
- Hongyue Zhao
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Yexin Su
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Yan Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Zhehao Lyu
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Peng Xu
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Wenchao Gu
- Department of Diagnostic and Interventional Radiology, University of Tsukuba, Ibaraki, Japan
| | - Lin Tian
- Department of Pathology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
| | - Peng Fu
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
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Jia Y, Hou L, Zhao J, Ren J, Li D, Li H, Cui Y. Radiomics analysis of multiparametric MRI for preoperative prediction of microsatellite instability status in endometrial cancer: a dual-center study. Front Oncol 2024; 14:1333020. [PMID: 38347846 PMCID: PMC10860747 DOI: 10.3389/fonc.2024.1333020] [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: 11/27/2023] [Accepted: 01/08/2024] [Indexed: 02/15/2024] Open
Abstract
Objective To develop and validate a multiparametric MRI-based radiomics model for prediction of microsatellite instability (MSI) status in patients with endometrial cancer (EC). Methods A total of 225 patients from Center I including 158 in the training cohort and 67 in the internal testing cohort, and 132 patients from Center II were included as an external validation cohort. All the patients were pathologically confirmed EC who underwent pelvic MRI before treatment. The MSI status was confirmed by immunohistochemistry (IHC) staining. A total of 4245 features were extracted from T2-weighted imaging (T2WI), contrast enhanced T1-weighted imaging (CE-T1WI) and apparent diffusion coefficient (ADC) maps for each patient. Four feature selection steps were used, and then five machine learning models, including Logistic Regression (LR), k-Nearest Neighbors (KNN), Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF), were built for MSI status prediction in the training cohort. Receiver operating characteristics (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance of these models. Results The SVM model showed the best performance with an AUC of 0.905 (95%CI, 0.848-0.961) in the training cohort, and was subsequently validated in the internal testing cohort and external validation cohort, with the corresponding AUCs of 0.875 (95%CI, 0.762-0.988) and 0.862 (95%CI, 0.781-0.942), respectively. The DCA curve demonstrated favorable clinical utility. Conclusion We developed and validated a multiparametric MRI-based radiomics model with gratifying performance in predicting MSI status, and could potentially be used to facilitate the decision-making on clinical treatment options in patients with EC.
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Affiliation(s)
- Yaju Jia
- Department of Radiology, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
- Department of Radiology, Shanxi Traditional Chinese Medical Hospital, Taiyuan, China
| | - Lina Hou
- Department of Radiology, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Jintao Zhao
- Department of Radiology, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Jialiang Ren
- Department of Pharmaceuticals Diagnostics, GE HealthCare, Beijing, China
| | - Dandan Li
- Department of Radiology, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Haiming Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yanfen Cui
- Department of Radiology, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
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Yang J, Huang J, Han D, Ma X. Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review. Clin Med Insights Oncol 2024; 18:11795549231220320. [PMID: 38187459 PMCID: PMC10771756 DOI: 10.1177/11795549231220320] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 11/26/2023] [Indexed: 01/09/2024] Open
Abstract
Colorectal cancer is the third most prevalent cancer worldwide, and its treatment has been a demanding clinical problem. Beyond traditional surgical therapy and chemotherapy, newly revealed molecular mechanisms diversify therapeutic approaches for colorectal cancer. However, the selection of personalized treatment among multiple treatment options has become another challenge in the era of precision medicine. Artificial intelligence has recently been increasingly investigated in the treatment of colorectal cancer. This narrative review mainly discusses the applications of artificial intelligence in the treatment of colorectal cancer patients. A comprehensive literature search was conducted in MEDLINE, EMBASE, and Web of Science to identify relevant papers, resulting in 49 articles being included. The results showed that, based on different categories of data, artificial intelligence can predict treatment outcomes and essential guidance information of traditional and novel therapies, thus enabling individualized treatment strategy selection for colorectal cancer patients. Some frequently implemented machine learning algorithms and deep learning frameworks have also been employed for long-term prognosis prediction in patients with colorectal cancer. Overall, artificial intelligence shows encouraging results in treatment strategy selection and prognosis evaluation for colorectal cancer patients.
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Affiliation(s)
- Jiaqing Yang
- Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Huang
- Department of Ultrasound, West China Hospital, Sichuan University, Chengdu, China
| | - Deqian Han
- Department of Oncology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
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Xiang Y, Li S, Song M, Wang H, Hu K, Wang F, Wang Z, Niu Z, Liu J, Cai Y, Li Y, Zhu X, Geng J, Zhang Y, Teng H, Wang W. KRAS status predicted by pretreatment MRI radiomics was associated with lung metastasis in locally advanced rectal cancer patients. BMC Med Imaging 2023; 23:210. [PMID: 38087207 PMCID: PMC10717608 DOI: 10.1186/s12880-023-01173-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 12/05/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Mutated KRAS may indicate an invasive nature and predict prognosis in locally advanced rectal cancer (LARC). We aimed to establish a radiomic model using pretreatment T2W MRIs to predict KRAS status and explore the association between the KRAS status or model predictions and lung metastasis. METHODS In this retrospective multicentre study, LARC patients from two institutions between January 2012 and January 2019 were randomly divided into training and testing cohorts. Least absolute shrinkage and selection operator (LASSO) regression and the support vector machine (SVM) classifier were utilized to select significant radiomic features and establish a prediction model, which was validated by radiomic score distribution and decision curve analysis. The association between the model stratification and lung metastasis was investigated by Cox regression and Kaplan‒Meier survival analysis; the results were compared by the log-rank test. RESULTS Overall, 103 patients were enrolled (73 and 30 in the training and testing cohorts, respectively). The median follow-up was 38.1 months (interquartile range: 26.9, 49.4). The radiomic model had an area under the curve (AUC) of 0.983 in the training cohort and 0.814 in the testing cohort. Using a cut-off of 0.679 defined by the receiver operating characteristic (ROC) curve, patients with a high radiomic score (RS) had a higher risk for lung metastasis (HR 3.565, 95% CI 1.337, 9.505, p = 0.011), showing similar predictive performances for the mutant and wild-type KRAS groups (HR 3.225, 95% CI 1.249, 8.323, p = 0.016, IDI: 1.08%, p = 0.687; NRI 2.23%, p = 0.766). CONCLUSIONS We established and validated a radiomic model for predicting KRAS status in LARC. Patients with high RS experienced more lung metastases. The model could noninvasively detect KRAS status and may help individualize clinical decision-making.
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Affiliation(s)
- Yirong Xiang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Shuai Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Maxiaowei Song
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Hongzhi Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Ke Hu
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Fengwei Wang
- Department of Oncology, Tianjin Union Medical Center, Tianjin, China
| | - Zhi Wang
- Blot Info & Tech (Beijing) Co. Ltd, Beijing, China
| | - Zhiyong Niu
- Blot Info & Tech (Beijing) Co. Ltd, Beijing, China
| | - Jin Liu
- Blot Info & Tech (Beijing) Co. Ltd, Beijing, China
| | - Yong Cai
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Yongheng Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Xianggao Zhu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Jianhao Geng
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Yangzi Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Huajing Teng
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Weihu Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China.
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Miranda J, Horvat N, Araujo-Filho JAB, Albuquerque KS, Charbel C, Trindade BMC, Cardoso DL, de Padua Gomes de Farias L, Chakraborty J, Nomura CH. The Role of Radiomics in Rectal Cancer. J Gastrointest Cancer 2023; 54:1158-1180. [PMID: 37155130 PMCID: PMC11301614 DOI: 10.1007/s12029-022-00909-w] [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] [Accepted: 12/26/2022] [Indexed: 05/10/2023]
Abstract
PURPOSE Radiomics is a promising method for advancing imaging assessment in rectal cancer. This review aims to describe the emerging role of radiomics in the imaging assessment of rectal cancer, including various applications of radiomics based on CT, MRI, or PET/CT. METHODS We conducted a literature review to highlight the progress of radiomic research to date and the challenges that need to be addressed before radiomics can be implemented clinically. RESULTS The results suggest that radiomics has the potential to provide valuable information for clinical decision-making in rectal cancer. However, there are still challenges in terms of standardization of imaging protocols, feature extraction, and validation of radiomic models. Despite these challenges, radiomics holds great promise for personalized medicine in rectal cancer, with the potential to improve diagnosis, prognosis, and treatment planning. Further research is needed to validate the clinical utility of radiomics and to establish its role in routine clinical practice. CONCLUSION Overall, radiomics has emerged as a powerful tool for improving the imaging assessment of rectal cancer, and its potential benefits should not be underestimated.
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Affiliation(s)
- Joao Miranda
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Natally Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA.
| | - Jose A B Araujo-Filho
- Department of Radiology, Hospital Sirio-Libanes, 91 Adma Jafet, Sao Paulo, SP, 01308-050, Brazil
| | - Kamila S Albuquerque
- Department of Radiology, Hospital Beneficência Portuguesa, 637 Maestro Cardim, Sao Paulo, SP, 01323-001, Brazil
| | - Charlotte Charbel
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Bruno M C Trindade
- Department of Radiology, University of Sao Paulo, 75 Dr. Ovídio Pires de Campos, Sao Paulo, SP, 05403-010, Brazil
| | - Daniel L Cardoso
- Department of Radiology, Hospital Sirio-Libanes, 91 Adma Jafet, Sao Paulo, SP, 01308-050, Brazil
| | | | - Jayasree Chakraborty
- Department of Surgery, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Cesar Higa Nomura
- Department of Radiology, University of Sao Paulo, 75 Dr. Ovídio Pires de Campos, Sao Paulo, SP, 05403-010, Brazil
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Niu Y, Yu X, Wen L, Bi F, Jian L, Liu S, Yang Y, Zhang Y, Lu Q. Comparison of preoperative CT- and MRI-based multiparametric radiomics in the prediction of lymph node metastasis in rectal cancer. Front Oncol 2023; 13:1230698. [PMID: 38074652 PMCID: PMC10708912 DOI: 10.3389/fonc.2023.1230698] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 11/08/2023] [Indexed: 01/22/2025] Open
Abstract
OBJECTIVE To compare computed tomography (CT)- and magnetic resonance imaging (MRI)-based multiparametric radiomics models and validate a multi-modality, multiparametric clinical-radiomics nomogram for individual preoperative prediction of lymph node metastasis (LNM) in rectal cancer (RC) patients. METHODS 234 rectal adenocarcinoma patients from our retrospective study cohort were randomly selected as the training (n = 164) and testing (n = 70) cohorts. The radiomics features of the primary tumor were extracted from the non-contrast enhanced computed tomography (NCE-CT), the enhanced computed tomography (CE-CT), the T2-weighted imaging (T2WI) and the gadolinium contrast-enhanced T1-weighted imaging (CE-TIWI) of each patient. Three kinds of models were constructed based on training cohort, including the Clinical model (based on the clinical features), the radiomics models (based on NCE-CT, CE-CT, T2WI, CE-T1WI, CT, MRI, CT combing with MRI) and the clinical-radiomics models (based on CT or MRI radiomics model combing with clinical data) and Clinical-IMG model (based on CT and MRI radiomics model combing with clinical data). The performances of the 11 models were evaluated via the area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, and specificity in the training and validation cohort. Differences in the AUCs among the 11 models were compared using DeLong's test. Finally, the optimal model (Clinical-IMG model) was selected to create a radiomics nomogram. The performance of the nomogram to evaluate clinical efficacy was verified by ROC curves and decision curve analysis (DCA). RESULTS The MRI radiomics model in the validation cohort significantly outperformed than CT radiomics model (AUC, 0.785 vs. 0.721, p<0.05). The Clinical-IMG nomogram had the highest prediction efficiency than all other predictive models (p<0.05), of which the AUC was 0.947, the sensitivity was 0.870 and the specificity was 0.884. CONCLUSION MRI radiomics model performed better than both CT radiomics model and Clinical model in predicting LNM of RC. The clinical-radiomics nomogram that combines the radiomics features obtained from both CT and MRI along with preoperative clinical characteristics exhibits the best diagnostic performance.
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Affiliation(s)
- Yue Niu
- Department of Diagnostic Radiology, Graduate Collaborative Training Base of Hunan Cancer Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
- Department of Diagnostic Radiology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Xiaoping Yu
- Department of Diagnostic Radiology, Graduate Collaborative Training Base of Hunan Cancer Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
- Department of Diagnostic Radiology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Lu Wen
- Department of Diagnostic Radiology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Feng Bi
- Department of Diagnostic Radiology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Lian Jian
- Department of Diagnostic Radiology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Siye Liu
- Department of Diagnostic Radiology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Yanhui Yang
- Department of Diagnostic Radiology, Graduate Collaborative Training Base of Hunan Cancer Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
- Department of Diagnostic Radiology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Yi Zhang
- Department of Diagnostic Radiology, Graduate Collaborative Training Base of Hunan Cancer Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
- Department of Diagnostic Radiology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Qiang Lu
- Department of Diagnostic Radiology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
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Jia LL, Lei J. Response to letter by Fusco Roberta & Vincenza Granata-Re: Comments on "Current status and quality of radiomic studies for predicting KRAS mutations in colorectal cancer patients: A systematic review and meta‑analysis". Eur J Radiol 2023; 175:111195. [PMID: 38669754 DOI: 10.1016/j.ejrad.2023.111195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 11/11/2023] [Indexed: 04/28/2024]
Affiliation(s)
- Lu-Lu Jia
- The First Clinical Medical College of Lanzhou University, Lanzhou City, Gansu Province, China
| | - Junqiang Lei
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou City, Gansu Province, China; Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou City, Gansu Province, China.
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Lin Z, Gu W, Guo Q, Xiao M, Li R, Deng L, Li Y, Cui Y, Li H, Qiang J. Multisequence MRI-based radiomics model for predicting POLE mutation status in patients with endometrial cancer. Br J Radiol 2023; 96:20221063. [PMID: 37660398 PMCID: PMC10607390 DOI: 10.1259/bjr.20221063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 07/19/2023] [Accepted: 07/28/2023] [Indexed: 09/05/2023] Open
Abstract
OBJECTIVES Preoperative identification of POLE mutation status would help tailor the surgical procedure and adjuvant treatment strategy. This study aimed to explore the feasibility of developing a radiomics model to pre-operatively predict the pathogenic POLE mutation status in patients with EC. METHODS The retrospective study involved 138 patients with histopathologically confirmed EC (35 POLE-mutant vs 103 non-POLE-mutant). After selecting relevant features with a series of steps, three radiomics signatures were built based on axial fat-saturation T2WI, DWI, and CE-T1WI images, respectively. Then, two radiomics models which integrated features from T2WI + DWI and T2WI + DWI+CE-T1WI were further developed using multivariate logistic regression. The performance of the radiomics model was evaluated from discrimination, calibration, and clinical utility aspects. RESULTS Among all the models, radiomics model2 (RM2), which integrated features from all three sequences, showed the best performance, with AUCs of 0.885 (95%CI: 0.828-0.942) and 0.810 (95%CI: 0.653-0.967) in the training and validation cohorts, respectively. The net reclassification index (NRI) and integrated discrimination improvement (IDI) analyses indicated that RM2 had improvement in predicting POLE mutation status when compared with the single-sequence-based signatures and the radiomics model1 (RM1). The calibration curve, decision curve analysis, and clinical impact curve suggested favourable calibration and clinical utility of RM2. CONCLUSIONS The RM2, fusing features from three sequences, could be a potential tool for the non-invasive preoperative identification of patients with POLE-mutant EC, which is helpful for developing individualized therapeutic strategies. ADVANCES IN KNOWLEDGE This study developed a potential surrogate of POLE sequencing, which is cost-efficient and non-invasive.
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Affiliation(s)
| | - Weiyong Gu
- Department of Pathology, Obstetrics & Gynecology Hospital, Fudan University, Shanghai, China
| | | | - Meiling Xiao
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | | | - Lin Deng
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | - Ying Li
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | - Yanfen Cui
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | | | - Jinwei Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
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Amintas S, Giraud N, Fernandez B, Dupin C, Denost Q, Garant A, Frulio N, Smith D, Rullier A, Rullier E, Vuong T, Dabernat S, Vendrely V. The Crying Need for a Better Response Assessment in Rectal Cancer. Curr Treat Options Oncol 2023; 24:1507-1523. [PMID: 37702885 PMCID: PMC10643426 DOI: 10.1007/s11864-023-01125-9] [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] [Accepted: 07/09/2023] [Indexed: 09/14/2023]
Abstract
OPINION STATEMENT Since total neoadjuvant treatment achieves almost 30% pathologic complete response, organ preservation has been increasingly debated for good responders after neoadjuvant treatment for patients diagnosed with rectal cancer. Two organ preservation strategies are available: a watch and wait strategy and a local excision strategy including patients with a near clinical complete response. A major issue is the selection of patients according to the initial tumor staging or the response assessment. Despite modern imaging improvement, identifying complete response remains challenging. A better selection could be possible by radiomics analyses, exploiting numerous image features to feed data characterization algorithms. The subsequent step is to include baseline and/or pre-therapeutic MRI, PET-CT, and CT radiomics added to the patients' clinicopathological data, inside machine learning (ML) prediction models, with predictive or prognostic purposes. These models could be further improved by the addition of new biomarkers such as circulating tumor biomarkers, molecular profiling, or pathological immune biomarkers.
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Affiliation(s)
- Samuel Amintas
- Tumor Biology and Tumor Bank Laboratory, CHU Bordeaux, F-33600, Pessac, France.
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France.
| | - Nicolas Giraud
- Department of Radiation Oncology, CHU Bordeaux, F-33000, Bordeaux, France
| | | | - Charles Dupin
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France
- Department of Radiation Oncology, CHU Bordeaux, F-33000, Bordeaux, France
| | - Quentin Denost
- Bordeaux Colorectal Institute, F-33000, Bordeaux, France
| | - Aurelie Garant
- UT Southwestern Department of Radiation Oncology, Dallas, USA
| | - Nora Frulio
- Radiology Department, CHU Bordeaux, F-33600, Pessac, France
| | - Denis Smith
- Department of Digestive Oncology, CHU Bordeaux, F-33600, Pessac, France
| | - Anne Rullier
- Histology Department, CHU Bordeaux, F-33000, Bordeaux, France
| | - Eric Rullier
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France
- Surgery Department, CHU Bordeaux, F-33600, Pessac, France
| | - Te Vuong
- Department of Radiation Oncology, McGill University, Jewish General Hospital, Montreal, Canada
| | - Sandrine Dabernat
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France
- Biochemistry Department, CHU Bordeaux, F-33000, Bordeaux, France
| | - Véronique Vendrely
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France
- Department of Radiation Oncology, CHU Bordeaux, F-33000, Bordeaux, France
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Cao Y, Zhang J, Huang L, Zhao Z, Zhang G, Ren J, Li H, Zhang H, Guo B, Wang Z, Xing Y, Zhou J. Construction of prediction model for KRAS mutation status of colorectal cancer based on CT radiomics. Jpn J Radiol 2023; 41:1236-1246. [PMID: 37311935 PMCID: PMC10613595 DOI: 10.1007/s11604-023-01458-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 06/04/2023] [Indexed: 06/15/2023]
Abstract
BACKGROUND In this study, we used computed tomography (CT)-based radiomics signatures to predict the mutation status of KRAS in patients with colorectal cancer (CRC) and to identify the phase of radiomics signature with the most robust and high performance from triphasic enhanced CT. METHODS This study involved 447 patients who underwent KRAS mutation testing and preoperative triphasic enhanced CT. They were categorized into training (n = 313) and validation cohorts (n = 134) in a 7:3 ratio. Radiomics features were extracted using triphasic enhanced CT imaging. The Boruta algorithm was used to retain the features closely associated with KRAS mutations. The Random Forest (RF) algorithm was used to develop radiomics, clinical, and combined clinical-radiomics models for KRAS mutations. The receiver operating characteristic curve, calibration curve, and decision curve were used to evaluate the predictive performance and clinical usefulness of each model. RESULTS Age, CEA level, and clinical T stage were independent predictors of KRAS mutation status. After rigorous feature screening, four arterial phase (AP), three venous phase (VP), and seven delayed phase (DP) radiomics features were retained as the final signatures for predicting KRAS mutations. The DP models showed superior predictive performance compared to AP or VP models. The clinical-radiomics fusion model showed excellent performance, with an AUC, sensitivity, and specificity of 0.772, 0.792, and 0.646 in the training cohort, and 0.755, 0.724, and 0.684 in the validation cohort, respectively. The decision curve showed that the clinical-radiomics fusion model had more clinical practicality than the single clinical or radiomics model in predicting KRAS mutation status. CONCLUSION The clinical-radiomics fusion model, which combines the clinical and DP radiomics model, has the best predictive performance for predicting the mutation status of KRAS in CRC, and the constructed model has been effectively verified by an internal validation cohort.
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Affiliation(s)
- Yuntai Cao
- Department of Radiology, Affiliated Hospital of Qinghai University, Tongren Road No. 29, Xining, 810001, People's Republic of China.
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou, 730030, People's Republic of China.
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, People's Republic of China.
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, People's Republic of China.
| | - Jing Zhang
- The Fifth Affiliated Hospital of Zunyi Medical University, Zunyi, 519100, People's Republic of China
| | - Lele Huang
- Department of Nuclear Medicine, Lanzhou University Second Hospital, Lanzhou, China
| | - Zhiyong Zhao
- Department of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, China
| | - Guojin Zhang
- Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China
| | - Jialiang Ren
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Beijing, China
| | - Hailong Li
- Affiliated Hospital of Qinghai University, Xining, China
| | - Hongqian Zhang
- Affiliated Hospital of Qinghai University, Xining, China
| | - Bin Guo
- Affiliated Hospital of Qinghai University, Xining, China
| | - Zhan Wang
- Affiliated Hospital of Qinghai University, Xining, China
| | - Yue Xing
- Xinxiang Medical University, Henan, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou, 730030, People's Republic of China.
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, People's Republic of China.
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, People's Republic of China.
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Kang W, Qiu X, Luo Y, Luo J, Liu Y, Xi J, Li X, Yang Z. Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis. J Transl Med 2023; 21:598. [PMID: 37674169 PMCID: PMC10481579 DOI: 10.1186/s12967-023-04437-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/12/2023] [Indexed: 09/08/2023] Open
Abstract
The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has given rise to the prominence of the tumor microenvironment (TME) as a critical area of research. The clinical implications of an improved understanding of the TME are significant and far-reaching. Radiomics has been increasingly utilized in the comprehensive assessment of the TME and cancer prognosis. Similarly, the advancement of pathomics, which is based on pathological images, can offer additional insights into the panoramic view and microscopic information of tumors. The combination of pathomics and radiomics has revolutionized the concept of a "digital biopsy". As genomics and transcriptomics continue to evolve, integrating radiomics with genomic and transcriptomic datasets can offer further insights into tumor and microenvironment heterogeneity and establish correlations with biological significance. Therefore, the synergistic analysis of digital image features (radiomics, pathomics) and genetic phenotypes (genomics) can comprehensively decode and characterize the heterogeneity of the TME as well as predict cancer prognosis. This review presents a comprehensive summary of the research on important radiomics biomarkers for predicting the TME, emphasizing the interplay between radiomics, genomics, transcriptomics, and pathomics, as well as the application of multiomics in decoding the TME and predicting cancer prognosis. Finally, we discuss the challenges and opportunities in multiomics research. In conclusion, this review highlights the crucial role of radiomics and multiomics associations in the assessment of the TME and cancer prognosis. The combined analysis of radiomics, pathomics, genomics, and transcriptomics is a promising research direction with substantial research significance and value for comprehensive TME evaluation and cancer prognosis assessment.
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Affiliation(s)
- Wendi Kang
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Xiang Qiu
- Obstetrics and Gynecology Hospital of, Fudan University, Shanghai, 200011, China
| | - Yingen Luo
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Jianwei Luo
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, 410013, Hunan, China
| | - Yang Liu
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Junqing Xi
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Xiao Li
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Zhengqiang Yang
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China.
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Liu Z, Duan T, Zhang Y, Weng S, Xu H, Ren Y, Zhang Z, Han X. Radiogenomics: a key component of precision cancer medicine. Br J Cancer 2023; 129:741-753. [PMID: 37414827 PMCID: PMC10449908 DOI: 10.1038/s41416-023-02317-8] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 05/02/2023] [Accepted: 06/12/2023] [Indexed: 07/08/2023] Open
Abstract
Radiogenomics, focusing on the relationship between genomics and imaging phenotypes, has been widely applied to address tumour heterogeneity and predict immune responsiveness and progression. It is an inevitable consequence of current trends in precision medicine, as radiogenomics costs less than traditional genetic sequencing and provides access to whole-tumour information rather than limited biopsy specimens. By providing voxel-by-voxel genetic information, radiogenomics can allow tailored therapy targeting a complete, heterogeneous tumour or set of tumours. In addition to quantifying lesion characteristics, radiogenomics can also be used to distinguish benign from malignant entities, as well as patient characteristics, to better stratify patients according to disease risk, thereby enabling more precise imaging and screening. Here, we have characterised the radiogenomic application in precision medicine using a multi-omic approach. we outline the main applications of radiogenomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalised medicine. Finally, we discuss the challenges in the field of radiogenomics and the scope and clinical applicability of these methods.
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Affiliation(s)
- Zaoqu Liu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
- Interventional Institute of Zhengzhou University, 450052, Zhengzhou, Henan, China
- Interventional Treatment and Clinical Research Center of Henan Province, 450052, Zhengzhou, Henan, China
| | - Tian Duan
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Yuyuan Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Siyuan Weng
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Hui Xu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Yuqing Ren
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Zhenyu Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China.
| | - Xinwei Han
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China.
- Interventional Institute of Zhengzhou University, 450052, Zhengzhou, Henan, China.
- Interventional Treatment and Clinical Research Center of Henan Province, 450052, Zhengzhou, Henan, China.
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Kocak B, Yardimci AH, Nazli MA, Yuzkan S, Mutlu S, Guzelbey T, Sam Ozdemir M, Akin M, Yucel S, Bulut E, Bayrak ON, Okumus AA. REliability of consensus-based segMentatIoN in raDiomic feature reproducibility (REMIND): A word of caution. Eur J Radiol 2023; 165:110893. [PMID: 37285646 DOI: 10.1016/j.ejrad.2023.110893] [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: 03/31/2023] [Revised: 05/01/2023] [Accepted: 05/23/2023] [Indexed: 06/09/2023]
Abstract
OBJECTIVE To evaluate the reliability of consensus-based segmentation in terms of reproducibility of radiomic features. METHODS In this retrospective study, three tumor data sets were investigated: breast cancer (n = 30), renal cell carcinoma (n = 30), and pituitary macroadenoma (n = 30). MRI was utilized for breast and pituitary data sets, while CT was used for renal data set. 12 readers participated in the segmentation process. Consensus segmentation was created by making corrections on a previous region or volume of interest. Four experiments were designed to evaluate the reproducibility of radiomic features. Reliability was assessed with intraclass correlation coefficient (ICC) with two cut-off values: 0.75 and 0.9. RESULTS Considering the lower bound of the 95% confidence interval and the ICC threshold of 0.90, at least 61% of the radiomic features were not reproducible in the inter-consensus analysis. In the susceptibility experiment, at least half (54%) became non-reproducible when the first reader is replaced with a different reader. In the intra-consensus analysis, at least about one-third (32%) were non-reproducible when the same second reader segmented the image over the same first reader two weeks later. Compared to inter-reader analysis based on independent single readers, the inter-consensus analysis did not statistically significantly improve the rates of reproducible features in all data sets and analyses. CONCLUSIONS Despite the positive connotation of the word "consensus", it is essential to REMIND that consensus-based segmentation has significant reproducibility issues. Therefore, the usage of consensus-based segmentation alone should be avoided unless a reliability analysis is performed, even if it is not practical in clinical settings.
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Affiliation(s)
- Burak Kocak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey.
| | - Aytul Hande Yardimci
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Mehmet Ali Nazli
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Sabahattin Yuzkan
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Samet Mutlu
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Tevfik Guzelbey
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Merve Sam Ozdemir
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Meliha Akin
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Serap Yucel
- Department of Radiology, Baskent University, Istanbul Hospital, Istanbul, Turkey
| | - Elif Bulut
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Osman Nuri Bayrak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Ahmet Arda Okumus
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
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Di Costanzo G, Ascione R, Ponsiglione A, Tucci AG, Dell’Aversana S, Iasiello F, Cavaglià E. Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: a review. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:406-421. [PMID: 37455833 PMCID: PMC10344900 DOI: 10.37349/etat.2023.00142] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 03/01/2023] [Indexed: 07/18/2023] Open
Abstract
Rectal cancer (RC) is one of the most common tumours worldwide in both males and females, with significant morbidity and mortality rates, and it accounts for approximately one-third of colorectal cancers (CRCs). Magnetic resonance imaging (MRI) has been demonstrated to be accurate in evaluating the tumour location and stage, mucin content, invasion depth, lymph node (LN) metastasis, extramural vascular invasion (EMVI), and involvement of the mesorectal fascia (MRF). However, these features alone remain insufficient to precisely guide treatment decisions. Therefore, new imaging biomarkers are necessary to define tumour characteristics for staging and restaging patients with RC. During the last decades, RC evaluation via MRI-based radiomics and artificial intelligence (AI) tools has been a research hotspot. The aim of this review was to summarise the achievement of MRI-based radiomics and AI for the evaluation of staging, response to therapy, genotyping, prediction of high-risk factors, and prognosis in the field of RC. Moreover, future challenges and limitations of these tools that need to be solved to favour the transition from academic research to the clinical setting will be discussed.
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Affiliation(s)
- Giuseppe Di Costanzo
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Raffaele Ascione
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy
| | - Anna Giacoma Tucci
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Serena Dell’Aversana
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Francesca Iasiello
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Enrico Cavaglià
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
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Lee S, Surabhi VR, Kassam Z, Chang KJ, Kaur H. Imaging of colon and rectal cancer. Curr Probl Cancer 2023:100970. [PMID: 37330400 DOI: 10.1016/j.currproblcancer.2023.100970] [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: 01/31/2023] [Revised: 06/01/2023] [Accepted: 06/01/2023] [Indexed: 06/19/2023]
Abstract
Colon and rectal cancer imaging has traditionally been performed to assess for distant disease (typically lung and liver metastases) and to assess the resectability of the primary tumor. With technological and scientific advances in imaging and the evolution of treatment options, the role of imaging has expanded. Radiologists are now expected to provide a precise description of primary tumor invasion extent, including adjacent organ invasion, involvement of the surgical resection plane, extramural vascular invasion, lymphadenopathy, and response to neoadjuvant treatment, and to monitor for recurrence after clinical complete response.
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Affiliation(s)
- Sonia Lee
- Department of Radiological Sciences, University of California, Irvine, CA.
| | - Venkateswar R Surabhi
- Department of Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Zahra Kassam
- Department of Medical Imaging, Schulich School of Medicine, Western University, St Joseph's Hospital, London, Ontario, Canada
| | - Kevin J Chang
- Department of Radiology, Boston University Medical Center, Boston, MA
| | - Harmeet Kaur
- Department of Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX
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Panic J, Defeudis A, Balestra G, Giannini V, Rosati S. Normalization Strategies in Multi-Center Radiomics Abdominal MRI: Systematic Review and Meta-Analyses. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2023; 4:67-76. [PMID: 37283773 PMCID: PMC10241248 DOI: 10.1109/ojemb.2023.3271455] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/18/2023] [Accepted: 04/25/2023] [Indexed: 06/08/2023] Open
Abstract
Goal: Artificial intelligence applied to medical image analysis has been extensively used to develop non-invasive diagnostic and prognostic signatures. However, these imaging biomarkers should be largely validated on multi-center datasets to prove their robustness before they can be introduced into clinical practice. The main challenge is represented by the great and unavoidable image variability which is usually addressed using different pre-processing techniques including spatial, intensity and feature normalization. The purpose of this study is to systematically summarize normalization methods and to evaluate their correlation with the radiomics model performances through meta-analyses. This review is carried out according to the PRISMA statement: 4777 papers were collected, but only 74 were included. Two meta-analyses were carried out according to two clinical aims: characterization and prediction of response. Findings of this review demonstrated that there are some commonly used normalization approaches, but not a commonly agreed pipeline that can allow to improve performance and to bridge the gap between bench and bedside.
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Affiliation(s)
- Jovana Panic
- Department of Surgical Science, and Polytechnic of Turin, Department of Electronics and TelecommunicationsUniversity of Turin10129TurinItaly
| | - Arianna Defeudis
- Department of Surgical ScienceUniversity of Turin10129TurinItaly
- Candiolo Cancer InstituteFPO-IRCCS10060CandioloItaly
| | - Gabriella Balestra
- Department of Electronics and TelecommunicationsPolytechnic of Turin10129TurinItaly
| | - Valentina Giannini
- Department of Surgical ScienceUniversity of Turin10129TurinItaly
- Candiolo Cancer InstituteFPO-IRCCS10060CandioloItaly
| | - Samanta Rosati
- Department of Electronics and TelecommunicationsPolytechnic of Turin10129TurinItaly
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Liu Y, Wei X, Feng X, Liu Y, Feng G, Du Y. Repeatability of radiomics studies in colorectal cancer: a systematic review. BMC Gastroenterol 2023; 23:125. [PMID: 37059990 PMCID: PMC10105401 DOI: 10.1186/s12876-023-02743-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 03/22/2023] [Indexed: 04/16/2023] Open
Abstract
BACKGROUND Recently, radiomics has been widely used in colorectal cancer, but many variable factors affect the repeatability of radiomics research. This review aims to analyze the repeatability of radiomics studies in colorectal cancer and to evaluate the current status of radiomics in the field of colorectal cancer. METHODS The included studies in this review by searching from the PubMed and Embase databases. Then each study in our review was evaluated using the Radiomics Quality Score (RQS). We analyzed the factors that may affect the repeatability in the radiomics workflow and discussed the repeatability of the included studies. RESULTS A total of 188 studies was included in this review, of which only two (2/188, 1.06%) studies controlled the influence of individual factors. In addition, the median score of RQS was 11 (out of 36), range-1 to 27. CONCLUSIONS The RQS score was moderately low, and most studies did not consider the repeatability of radiomics features, especially in terms of Intra-individual, scanners, and scanning parameters. To improve the generalization of the radiomics model, it is necessary to further control the variable factors of repeatability.
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Affiliation(s)
- Ying Liu
- School of Medical Imaging, North Sichuan Medical College, Sichuan Province, Nanchong City, 637000, China
| | - Xiaoqin Wei
- School of Medical Imaging, North Sichuan Medical College, Sichuan Province, Nanchong City, 637000, China
| | | | - Yan Liu
- Department of Radiology, the Affiliated Hospital of North Sichuan Medical College, 1 Maoyuannan Road, Sichuan Province, 637000, Nanchong City, China
| | - Guiling Feng
- Department of Radiology, the Affiliated Hospital of North Sichuan Medical College, 1 Maoyuannan Road, Sichuan Province, 637000, Nanchong City, China
| | - Yong Du
- Department of Radiology, the Affiliated Hospital of North Sichuan Medical College, 1 Maoyuannan Road, Sichuan Province, 637000, Nanchong City, China.
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Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology. Semin Cancer Biol 2023; 91:1-15. [PMID: 36801447 DOI: 10.1016/j.semcancer.2023.02.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 01/30/2023] [Accepted: 02/15/2023] [Indexed: 02/21/2023]
Abstract
Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays. Historically, these processes typically required single-gene sequencing, next-generation sequencing, or visual inspection of histopathology slides by experienced pathologists in a clinical context. In the past decade, advances in artificial intelligence (AI) technologies have demonstrated remarkable potential in assisting physicians with accurate diagnosis of oncology image-recognition tasks. Meanwhile, AI techniques make it possible to integrate multimodal data such as radiology, histology, and genomics, providing critical guidance for the stratification of patients in the context of precision therapy. Given that the mutation detection is unaffordable and time-consuming for a considerable number of patients, predicting gene mutations based on routine clinical radiological scans or whole-slide images of tissue with AI-based methods has become a hot issue in actual clinical practice. In this review, we synthesized the general framework of multimodal integration (MMI) for molecular intelligent diagnostics beyond standard techniques. Then we summarized the emerging applications of AI in the prediction of mutational and molecular profiles of common cancers (lung, brain, breast, and other tumor types) pertaining to radiology and histology imaging. Furthermore, we concluded that there truly exist multiple challenges of AI techniques in the way of its real-world application in the medical field, including data curation, feature fusion, model interpretability, and practice regulations. Despite these challenges, we still prospect the clinical implementation of AI as a highly potential decision-support tool to aid oncologists in future cancer treatment management.
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Yu MM, Shi D, Li Q, Li JB, Li Q, Yu RS. KRAS mutation status between left- and right-sided colorectal cancer: are there any differences in computed tomography? Jpn J Radiol 2023; 41:83-91. [PMID: 35976561 DOI: 10.1007/s11604-022-01326-6] [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: 06/15/2022] [Accepted: 08/03/2022] [Indexed: 02/07/2023]
Abstract
PURPOSE To investigate the differences in clinicopathological and imaging features according to KRAS mutation status in left- and right-sided colorectal cancer. METHOD A total of 157 patients with pathologically proven colorectal cancer and preoperative contrast-enhanced multidetector CT examinations were enrolled. According to the tumor location and KRAS status, they were divided into two groups: the left-sided colorectal cancer (LCC) group (wild type, mutant type) and the right-sided colorectal cancer (RCC) group (wild type, mutant type). Clinicopathological and imaging features were recorded in each group. The imaging observation indicators included short axis diameter (SAD), longitudinal tumor length (LTL), tumor shape, pericolic fat stranding, bowel stenosis, intratumoral low-density range, enhancement pattern, and bowel obstruction. Univariate and multivariate logistic regression analyses were performed to compare the difference in KRAS mutation status between groups. RESULTS In the LCC group, SAD, tumor shape, degree of pericolic fat stranding, and bowel obstruction were significant indicators for predicting KRAS status (P < 0.05). In the RCC group, CA19-9, SAD, and intratumoral low-density range were significant indicators for predicting KRAS status (P < 0.05.). The area under the curve (AUC) of the combination image indicators in the LCC group was 0.802 [cutoff point 0.372, 95% confidence interval (CI) 0.718-0.888, sensitivity 85.4%, specificity 72.0%]. The AUC in the RCC group was 0.828 (cutoff point 0.647, 95% CI 0.726-0.931, sensitivity 79.5%, specificity 75.0%). CONCLUSION The CT imaging features associated with KRAS mutation status in the LCC and RCC groups were different. The combination of tumor location and imaging features can help to further improve the predictive value of KRAS status.
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Affiliation(s)
- Ming-Ming Yu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, No. 88 Jiefang Road, Shangcheng District, Hangzhou, 310009, China.,Department of Radiology, The Affiliated People's Hospital of Ningbo University, No. 251 Baizhang Road, Yinzhou District, Ningbo, China
| | - Dan Shi
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, No. 88 Jiefang Road, Shangcheng District, Hangzhou, 310009, China
| | - Qi Li
- Department of Colorectal Surgery, Ningbo Medical Center Lihuili Hospital, No. 57 Xingning Road, Yinzhou District, Ningbo, China
| | - Jian-Bin Li
- Department of Radiology, The Affiliated People's Hospital of Ningbo University, No. 251 Baizhang Road, Yinzhou District, Ningbo, China
| | - Qiang Li
- Department of Radiology, The Affiliated People's Hospital of Ningbo University, No. 251 Baizhang Road, Yinzhou District, Ningbo, China
| | - Ri-Sheng Yu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, No. 88 Jiefang Road, Shangcheng District, Hangzhou, 310009, China.
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Liu H, Yin H, Li J, Dong X, Zheng H, Zhang T, Yin Q, Zhang Z, Lu M, Zhang H, Wang D. A Deep Learning Model Based on MRI and Clinical Factors Facilitates Noninvasive Evaluation of KRAS Mutation in Rectal Cancer. J Magn Reson Imaging 2022; 56:1659-1668. [PMID: 35587946 DOI: 10.1002/jmri.28237] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 05/04/2022] [Accepted: 05/04/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Recent studies showed the potential of MRI-based deep learning (DL) for assessing treatment response in rectal cancer, but the role of MRI-based DL in evaluating Kirsten rat sarcoma viral oncogene homologue (KRAS) mutation remains unclear. PURPOSE To develop a DL method based on T2-weighted imaging (T2WI) and clinical factors for noninvasively evaluating KRAS mutation in rectal cancer. STUDY TYPE Retrospective. SUBJECTS A total of 376 patients (108 women [28.7%]) with histopathology-confirmed rectal adenocarcinoma and KRAS mutation status. FIELD STRENGTH/SEQUENCE A 3 T, turbo spin echo T2WI and single-shot echo-planar diffusion-weighted imaging (b = 0, 1000 sec/mm2 ). ASSESSMENT A clinical model was constructed with clinical factors (age, gender, carcinoembryonic antigen level, and carbohydrate antigen 199 level) and MRI features (tumor length, tumor location, tumor stage, lymph node stage, and extramural vascular invasion), and two DL models based on modified MobileNetV2 architecture were evaluated for diagnosing KRAS mutation based on T2WI alone (image model) or both T2WI and clinical factors (combined model). The clinical usefulness of these models was evaluated through calibration analysis and decision curve analysis (DCA). STATISTICAL TESTS Mann-Whitney U test, Chi-squared test, Fisher's exact test, logistic regression analysis, receiver operating characteristic curve (ROC), Delong's test, Hosmer-Lemeshow test, interclass correlation coefficients, and Fleiss kappa coefficients (P < 0.05 was considered statistically significant). RESULTS All the nine clinical-MRI characteristics were included for clinical model development. The clinical model, image model, and combined model in the testing cohort demonstrated good calibration and achieved areas under the curve (AUCs) of 0.668, 0.765, and 0.841, respectively. The combined model showed improved performance compared to the clinical model and image model in two cohorts. DCA confirmed the higher net benefit of the combined model than the other two models when the threshold probability is between 0.05 and 0.85. DATA CONCLUSION The proposed combined DL model incorporating T2WI and clinical factors may show good diagnostic performance. Thus, it could potentially serve as a supplementary approach for noninvasively evaluating KRAS mutation in rectal cancer. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Huanhuan Liu
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongkun Yin
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Jinning Li
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xue Dong
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hui Zheng
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tingting Zhang
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qiufeng Yin
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhongyang Zhang
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Minda Lu
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huiling Zhang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Dengbin Wang
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Jia LL, Zheng QY, Tian JH, He DL, Zhao JX, Zhao LP, Huang G. Artificial intelligence with magnetic resonance imaging for prediction of pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer: A systematic review and meta-analysis. Front Oncol 2022; 12:1026216. [PMID: 36313696 PMCID: PMC9597310 DOI: 10.3389/fonc.2022.1026216] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 09/21/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose The purpose of this study was to evaluate the diagnostic accuracy of artificial intelligence (AI) models with magnetic resonance imaging(MRI) in predicting pathological complete response(pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer. Furthermore, assessed the methodological quality of the models. Methods We searched PubMed, Embase, Cochrane Library, and Web of science for studies published before 21 June 2022, without any language restrictions. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools were used to assess the methodological quality of the included studies. We calculated pooled sensitivity and specificity using random-effects models, I2 values were used to measure heterogeneity, and subgroup analyses to explore potential sources of heterogeneity. Results We selected 21 papers for inclusion in the meta-analysis from 1562 retrieved publications, with a total of 1873 people in the validation groups. The meta-analysis showed that AI models based on MRI predicted pCR to nCRT in patients with rectal cancer: a pooled area under the curve (AUC) 0.91 (95% CI, 0.88-0.93), sensitivity of 0.82(95% CI,0.71-0.90), pooled specificity 0.86(95% CI,0.80-0.91). In the subgroup analysis, the pooled AUC of the deep learning(DL) model was 0.97, the pooled AUC of the radiomics model was 0.85; the pooled AUC of the combined model with clinical factors was 0.92, and the pooled AUC of the radiomics model alone was 0.87. The mean RQS score of the included studies was 10.95, accounting for 30.4% of the total score. Conclusions Radiomics is a promising noninvasive method with high value in predicting pathological response to nCRT in patients with rectal cancer. DL models have higher predictive accuracy than radiomics models, and combined models incorporating clinical factors have higher diagnostic accuracy than radiomics models alone. In the future, prospective, large-scale, multicenter investigations using radiomics approaches will strengthen the diagnostic power of pCR. Systematic Review Registration https://www.crd.york.ac.uk/prospero/, identifier CRD42021285630.
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Affiliation(s)
- Lu-Lu Jia
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, China
| | - Qing-Yong Zheng
- Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China
| | - Jin-Hui Tian
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Di-Liang He
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, China
| | - Jian-Xin Zhao
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, China
| | - Lian-Ping Zhao
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Gang Huang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
- *Correspondence: Gang Huang,
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Ma T, Cui J, Wang L, Li H, Ye Z, Gao X. A multiphase contrast-enhanced CT radiomics model for prediction of human epidermal growth factor receptor 2 status in advanced gastric cancer. Front Genet 2022; 13:968027. [PMID: 36276942 PMCID: PMC9585247 DOI: 10.3389/fgene.2022.968027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 09/27/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Accurate evaluation of human epidermal growth factor receptor 2 (HER2) status is of great importance for appropriate management of advanced gastric cancer (AGC) patients. This study aims to develop and validate a CT-based radiomics model for prediction of HER2 overexpression in AGC. Materials and Methods: Seven hundred and forty-five consecutive AGC patients (median age, 59 years; interquartile range, 52–66 years; 515 male and 230 female) were enrolled and separated into training set (n = 521) and testing set (n = 224) in this retrospective study. Radiomics features were extracted from three phases images of contrast-enhanced CT scans. A radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator method. Univariable and multivariable logistical regression analysis were used to establish predictive model with independent risk factors of HER2 overexpression. The predictive performance of radiomics model was assessed in the training and testing sets. Results: The positive rate of HER2 was 15.9% and 13.8% in the training set and testing set, respectively. The positive rate of HER2 in intestinal-type GC was significantly higher than that in diffuse-type GC. The radiomics signature comprised eight robust features demonstrated good discrimination ability for HER2 overexpression in the training set (AUC = 0.84) and the testing set (AUC = 0.78). A radiomics-based model that incorporated radiomics signature and pathological type showed good discrimination and calibration in the training (AUC = 0.85) and testing (AUC = 0.84) sets. Conclusion: The proposed radiomics model showed favorable accuracy for prediction of HER2 overexpression in AGC.
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Affiliation(s)
- Tingting Ma
- Department of Radiology, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Jingli Cui
- National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- Department of Bone and Soft Tissue Tumor, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Department of General Surgery, Weifang People’s Hospital, Weifang, Shandong, China
| | - Lingwei Wang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Hui Li
- National Clinical Research Center for Cancer, Tianjin, China
- Department of Gastrointestinal Cancer Biology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- *Correspondence: Zhaoxiang Ye, ; Xujie Gao,
| | - Xujie Gao
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- *Correspondence: Zhaoxiang Ye, ; Xujie Gao,
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Ma Y, Lin C, Liu S, Wei Y, Ji C, Shi F, Lin F, Zhou Z. Radiomics features based on internal and marginal areas of the tumor for the preoperative prediction of microsatellite instability status in colorectal cancer. Front Oncol 2022; 12:1020349. [PMID: 36276101 PMCID: PMC9583004 DOI: 10.3389/fonc.2022.1020349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 09/20/2022] [Indexed: 11/21/2022] Open
Abstract
Objectives To explore whether the preoperative CT radiomics can predict the status of microsatellite instability (MSI) in colorectal cancer (CRC) patients and identify the region with the most stable and high-efficiency radiomics features. Methods This retrospective study involved 230 CRC patients with preoperative computed tomography scans and available MSI status between December 2019 and October 2021. Image segmentation and radiomic feature extraction were performed as follows. First, slices with the maximum tumor area (region of interest, ROI) were manually contoured. Subsequently, each ROI was shrunk inward by 1, 2, and 3 mm, respectively, where the remaining ROIs were considered as the internal region of the tumor (named as IROI1, IROI2, and IROI3), and the shrunk regions were considered as marginal regions of the tumor (named as MROI1, MROI2, and MROI3). Finally, radiomics features were extracted from each of the ROI. The intraclass correlation coefficient and least absolute shrinkage and selection operator method were used to choose the most reliable and relevant features of MSI status. Clinical, radiomics, and combined clinical radiomics models have been established. Calibration curve and decision curve analyses (DCA) were generated to explore the correction effect and assess the clinical applicability of the above models, respectively. Results In the testing cohort, the radiomics model based on IROI3 yielded the highest average area under the curve (AUC) value of 0.908, compared with the remaining radiomics models. Additionally, hypertension and N stage were considered as clinically independent factors of MSI status. The combined clinical radiomics model achieved excellent diagnostic efficacy (AUC: 0.928; sensitivity: 0.840; specificity: 0.867) in the testing cohort, as well as favorable calibration and clinical utility by calibration curve and DCA analyses. Conclusions The IROI3 model, which is based on a 3-mm shrink in the largest areas of the tumor, could noninvasively reflect the heterogeneity and genetic instability within the tumor. This suggests that it is an important biomarker for the preoperative prediction of MSI status. The model can extract more robust and effective radiomics features, which lays a foundation for the radiomics study of hollow organs, such as in CRC.
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Affiliation(s)
- Yi Ma
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
| | - Changsong Lin
- Department of Bioinformatics, Nanjing Medical University, Nanjing, China
| | - Song Liu
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
| | - Ying Wei
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Changfeng Ji
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Fan Lin
- Department of Cell Biology, Nanjing Medical University, Nanjing, China
- *Correspondence: Fan Lin, ; Zhengyang Zhou,
| | - Zhengyang Zhou
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
- *Correspondence: Fan Lin, ; Zhengyang Zhou,
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Computed tomography-based radiomics nomogram for the preoperative prediction of perineural invasion in colorectal cancer: a multicentre study. ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:3251-3263. [PMID: 35960308 DOI: 10.1007/s00261-022-03620-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/10/2022] [Accepted: 07/11/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE To develop and validate a computed tomography (CT) radiomics nomogram from multicentre datasets for preoperative prediction of perineural invasion (PNI) in colorectal cancer. METHODS A total of 299 patients with histologically confirmed colorectal cancer from three hospitals were enrolled in this retrospective study. Radiomic features were extracted from the whole tumour volume. The least absolute shrinkage and selection operator logistic regression was applied for feature selection and radiomics signature construction. Finally, a radiomics nomogram combining the radiomics score and clinical predictors was established. The receiver operating characteristic curve and decision curve analysis (DCA) were used to evaluate the predictive performance of the radiomics nomogram in the training cohort, internal validation and external validation cohorts. RESULTS Twelve radiomics features extracted from the whole tumour volume were used to construct the radiomics model. The area under the curve (AUC) values of the radiomics model in the training cohort, internal validation cohort, external validation cohort 1, and external validation cohort 2 were 0.82 (0.75-0.90), 0.77 (0.62-0.92), 0.71 (0.56-0.85), and 0.73 (0.60-0.85), respectively. The nomogram, which combined the radiomics score with T category and N category by CT, yielded better performance in the training cohort (AUC = 0.88), internal validation cohort (AUC = 0.80), external validation cohort 1 (AUC = 0.75), and external validation cohort 2 (AUC = 0.76). DCA confirmed the clinical utility of the nomogram. CONCLUSIONS The CT-based radiomics nomogram has the potential to accurately predict PNI in patients with colorectal cancer.
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A segmentation-based sequence residual attention model for KRAS gene mutation status prediction in colorectal cancer. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04011-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization. Cancers (Basel) 2022; 14:cancers14143349. [PMID: 35884409 PMCID: PMC9321521 DOI: 10.3390/cancers14143349] [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: 06/05/2022] [Revised: 07/07/2022] [Accepted: 07/08/2022] [Indexed: 12/10/2022] Open
Abstract
Simple Summary Modern, personalized therapy approaches are increasingly changing advanced cancer into a chronic disease. Compared to imaging, novel omics methodologies in molecular biology have already achieved an individual characterization of cancerous lesions. With quantitative imaging biomarkers, analyzed by radiomics or deep learning, an imaging-based assessment of tumoral biology can be brought into clinical practice. Combining these with other non-invasive methods, e.g., liquid profiling, could allow for more individual decision making regarding therapies and applications. Abstract Similar to the transformation towards personalized oncology treatment, emerging techniques for evaluating oncologic imaging are fostering a transition from traditional response assessment towards more comprehensive cancer characterization via imaging. This development can be seen as key to the achievement of truly personalized and optimized cancer diagnosis and treatment. This review gives a methodological introduction for clinicians interested in the potential of quantitative imaging biomarkers, treating of radiomics models, texture visualization, convolutional neural networks and automated segmentation, in particular. Based on an introduction to these methods, clinical evidence for the corresponding imaging biomarkers—(i) dignity and etiology assessment; (ii) tumoral heterogeneity; (iii) aggressiveness and response; and (iv) targeting for biopsy and therapy—is summarized. Further requirements for the clinical implementation of these imaging biomarkers and the synergistic potential of personalized molecular cancer diagnostics and liquid profiling are discussed.
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Hu J, Xia X, Wang P, Peng Y, Liu J, Xie X, Liao Y, Wan Q, Li X. Predicting Kirsten Rat Sarcoma Virus Gene Mutation Status in Patients With Colorectal Cancer by Radiomics Models Based on Multiphasic CT. Front Oncol 2022; 12:848798. [PMID: 35814386 PMCID: PMC9263192 DOI: 10.3389/fonc.2022.848798] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveTo develop and validate radiomics models based on multiphasic CT in predicting Kirsten rat sarcoma virus (KRAS) gene mutation status in patients with colorectal cancer (CRC).Materials and MethodsA total of 231 patients with pathologically confirmed CRC were retrospectively enrolled and randomly divided into training(n=184) and test groups(n=47) in a ratio of 4:1. A total of 1316 quantitative radiomics features were extracted from non-contrast phase (NCP), arterial-phase (AP) and venous-phase (VP) CT for each patient. Four steps were applied for feature selection including Spearman correlation analysis, variance threshold, least absolute contraction and selection operator, and multivariate stepwise regression analysis. Clinical and pathological characteristics were also assessed. Subsequently, three classification methods, logistic regression (LR), support vector machine (SVM) and random tree (RT) algorithm, were applied to develop seven groups of prediction models (NCP, AP, VP, AP+VP, AP+VP+NCP, AP&VP, AP&VP&NCP) for KRAS mutation prediction. The performance of these models was evaluated by receiver operating characteristics curve (ROC) analysis.ResultsAmong the three groups of single-phase models, the AP model, developed by LR algorithm, showed the best prediction performance with an AUC value of 0.811 (95% CI:0.685–0.938) in the test cohort. Compared with the single-phase models, the dual-phase (AP+VP) model with the LR algorithm showed better prediction performance (AUC=0.826, 95% CI:0.700-0.952). The performance of multiphasic (AP+VP+NCP) model with the LR algorithm (AUC=0.811, 95%CI: 0.679-0.944) is comparable to the model with the SVM algorithm (AUC=0.811, 95%CI: 0.695-0.918) in the test cohort, but the sensitivity, specificity, and accuracy of the multiphasic (AP+VP+NCP) model with the LR algorithm were 0.810, 0.808, 0.809 respectively, which were highest among these seven groups of prediction models in the test cohort.ConclusionThe CT radiomics models have the potential to predict KRAS mutation in patients with CRC; different phases may affect the predictive efficacy of radiomics model, of which arterial-phase CT is more informative. The combination of multiphasic CT images can further improve the performance of radiomics model.
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Affiliation(s)
- Jianfeng Hu
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiaoying Xia
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Peng Wang
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yu Peng
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jieqiong Liu
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiaobin Xie
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yuting Liao
- Department of Pharmaceutical Diagnostics, GE Healthcare, Shanghai, China
| | - Qi Wan
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- *Correspondence: Qi Wan, ; Xinchun Li,
| | - Xinchun Li
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- *Correspondence: Qi Wan, ; Xinchun Li,
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Abdollahi H, Chin E, Clark H, Hyde DE, Thomas S, Wu J, Uribe CF, Rahmim A. Radiomics-guided radiation therapy: opportunities and challenges. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac6fab] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/13/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.
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Xue T, Peng H, Chen Q, Li M, Duan S, Feng F. Preoperative prediction of KRAS mutation status in colorectal cancer using a CT-based radiomics nomogram. Br J Radiol 2022; 95:20211014. [PMID: 35312376 PMCID: PMC10996413 DOI: 10.1259/bjr.20211014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 03/08/2022] [Accepted: 03/14/2022] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE This study aimed to develop a model to predict KRAS mutations in colorectal cancer according to radiomic signatures based on CT and clinical risk factors. METHODS This retrospective study included 172 patients with colorectal cancer. All patients were randomized at a 7:3 ratio into a training cohort (n = 121, 38.8% positive for KRAS mutation) and a validation cohort (n = 51, 39.2% positive for KRAS mutation). Radiomics features were extracted from single-slice and full-volume regions of interest on the portal-venous CT images. The least absolute shrinkage and selection operator (LASSO) algorithm was adopted to construct a radiomics signature, and logistic regression was applied to select the significant variables to develop the clinical-radiomics model. The predictive performance was evaluated by receiver operating characteristic curve (ROC) analysis, calibration curve analysis, and decision curve analysis (DCA). RESULTS 1018 radiomics features were extracted from single-slice and full-volume ROIs. Eight features were retained to construct 2D (two-dimensional, 2D) radiomics model. Similarly, eight features were retained to construct 3D (three-dimensional, 3D) radiomics model. The area under the curve (AUC) values of the test cohort were 0.75 and 0.84, respectively. Delong test showed that the integrated nomogram (AUC = 0.92 in the test cohort) had better clinical predictive efficiency than 2D radiomics (p-value < 0.05) model and 3D radiomics model (p-value < 0.05). CONCLUSION The 2D and 3D radiomics models can both predict KRAS mutations. And, the integrated nomogram can be better applied to predict KRAS mutation status in colorectal cancer. ADVANCES IN KNOWLEDGE CT-based radiomics showed satisfactory diagnostic significance for the KRAS status in colorectal cancer, the clinical-combined model may be applied in the individual pre-operative prediction of KRAS mutation.
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Affiliation(s)
- Ting Xue
- Department of Radiology, Nantong University,
Nantong, Jiangsu, PR China
| | - Hui Peng
- Department of Radiology, Nantong University,
Nantong, Jiangsu, PR China
| | - Qiaoling Chen
- Department of Radiology, Nantong University,
Nantong, Jiangsu, PR China
| | - Manman Li
- Department of Radiology, Nantong University,
Nantong, Jiangsu, PR China
| | | | - Feng Feng
- Department of Radiology, Affiliated Tumor Hospital of Nantong
University, Nantong, Jiangsu,
PR China
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Tian R, Li Y, Jia C, Mou Y, Zhang H, Wu X, Li J, Yu G, Mao N, Song X. Radiomics Model for Predicting TP53 Status Using CT and Machine Learning Approach in Laryngeal Squamous Cell Carcinoma. Front Oncol 2022; 12:823428. [PMID: 35574352 PMCID: PMC9095903 DOI: 10.3389/fonc.2022.823428] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 04/04/2022] [Indexed: 11/16/2022] Open
Abstract
Objective We aim to establish and validate computed tomography (CT)-based radiomics model for predicting TP53 status in patients with laryngeal squamous cell carcinoma (LSCC). Methods We divided all patients into a training set 1 (n=66) and a testing set 1 (n=30) to establish and validate radiomics model to predict TP53. Radiomics features were selected by analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (Lasso) regression analysis. Five radiomics models were established by using K-Nearest Neighbor, logistics regressive, linear-support vector machine (SVM), gaussian-SVM, and polynomial-SVM in training set 1. We also divided all patients into a training set 2 and a testing set 2 according to different CT equipment to establish and evaluate the stability of the radiomics models. Results After ANOVA and subsequent Lasso regression analysis, 22 radiomics features were selected to build the radiomics model in training set 1. The radiomics model based on linear-SVM has the best predictive performance of the five models, and the area under the receiver operating characteristic curve in training set 1 and testing set 1 were 0.831(95% confidence interval [CI] 0.692–0.970) and 0.797(95% CI 0.632–0.957) respectively. The specificity, sensitivity, and accuracy were 0.971(95% CI 0.834–0.999), 0.714(95% CI 0.535–0.848), and 0.843(95% CI 0.657–0.928) in training set 1 and 0.750(95% CI 0.500–0.938), 0.786(95% CI 0.571–1.000), and 0.667(95% CI 0.467–0.720) in testing set 1, respectively. In addition, the radiomics model also achieved stable prediction results even in different CT equipment. Decision curve analysis showed that the radiomics model for predicting TP53 status could benefit LSCC patients. Conclusion We developed and validated a relatively optimal radiomics model for TP53 status prediction by trying five different machine learning methods in patients with LSCC. It shown great potential of radiomics features for predicting TP53 status preoperatively and guiding clinical treatment.
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Affiliation(s)
- Ruxian Tian
- Department of Otorhinolaryngology, Head and Neck Surgery, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Yumei Li
- Department of Otorhinolaryngology, Head and Neck Surgery, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Chuanliang Jia
- Department of Otorhinolaryngology, Head and Neck Surgery, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Yakui Mou
- Department of Otorhinolaryngology, Head and Neck Surgery, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Haicheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Xinxin Wu
- Department of Otorhinolaryngology, Head and Neck Surgery, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Jingjing Li
- Department of Otorhinolaryngology, Head and Neck Surgery, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Guohua Yu
- Department of Pathology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Xicheng Song
- Department of Otorhinolaryngology, Head and Neck Surgery, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China.,Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, China
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Song R, Cui Y, Ren J, Zhang J, Yang Z, Li D, Li Z, Yang X. CT-based radiomics analysis in the prediction of response to neoadjuvant chemotherapy in locally advanced gastric cancer: A dual-center study. Radiother Oncol 2022; 171:155-163. [DOI: 10.1016/j.radonc.2022.04.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 03/26/2022] [Accepted: 04/21/2022] [Indexed: 12/24/2022]
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Chen Y, Li B, Jiang Z, Li H, Dang Y, Tang C, Xia Y, Zhang H, Song B, Long L. Multi-parameter diffusion and perfusion magnetic resonance imaging and radiomics nomogram for preoperative evaluation of aquaporin-1 expression in rectal cancer. Abdom Radiol (NY) 2022; 47:1276-1290. [PMID: 35166938 DOI: 10.1007/s00261-021-03397-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/18/2021] [Accepted: 12/20/2021] [Indexed: 02/05/2023]
Abstract
PURPOSE The overexpression of aquaporin-1 (AQP1) is associated with poor prognosis in rectal cancer. This study aimed to explore the value of multi-parameter diffusion and perfusion MRI and radiomics models in predicting AQP1 high expression. METHODS This prospective study was performed from July 2019 to February 2021, which included rectal cancer participants after preoperative rectal MRI, with diffusion-weighted imaging, intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and dynamic contrast-enhanced (DCE) sequences. Radiomic features were extracted from MR images, and immunohistochemical tests assessed AQP1 expression. Selected quantitative MRI and radiomic features were analyzed. Receiver operating characteristic (ROC) curves evaluated the predictive performance. The nomogram performance was evaluated by its calibration, discrimen, and clinical utility. The intraclass correlation coefficient evaluated the interobserver agreement for the MRI features. RESULTS 110 participants with the age of 60.7 ± 12.5 years been enrolled in this study. The apparent diffusion coefficient (ADC), IVIM_D, DKI_diffusivity, and DCE_Ktrans were significantly higher in participants with high AQP1 expression than in those with low expression (P < 0.05). ADC (b = 1000, 2000, and 3000 s/mm2), IVIM_D, DKI_diffusivity, and DCE_Ktrans were positively correlated (r = 0.205, 0.275, 0.37, 0.235, 0.229, and 0.227, respectively; P < 0.05), whereas DKI_Kurtosis was negatively correlated (r = - 0.22, P = 0.021) with AQP1 expression. ADC (b = 3000 s/mm2), IVIM_D, DKI_ diffusivity, DKI_Kurtosis, and DCE_Ktrans had moderate diagnostic efficiencies for high AQP1 expression (AUC = 0.715, 0.636, 0.627, 0.633, and 0.632, respectively; P < 0.05). The radiomic features had excellent predictive efficiency for high AQP1 expression (AUC = 0.967 and 0.917 for training and validation). The model-based nomogram had C-indexes of 0.932 and 0.851 for the training and validation cohorts, which indicated good fitting to the calibration curves (p > 0.05). CONCLUSION Diffusion and perfusion MRI can indicate the aquaporin-1 expression in rectal cancer, and radiomic features can enhance the predictive efficiency for high AQP1 expression. A nomogram for high aquaporin-1 expression will improve clinical decision-making.
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Affiliation(s)
- Yidi Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Basen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Zijian Jiang
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Hui Li
- Department of Anus and Intestine Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Yiwu Dang
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Cheng Tang
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Yuwei Xia
- Huiying Medical Technology, Beijing, 100192, China
| | | | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Liling Long
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China.
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Ministry of Education, Gaungxi Medical University, Nanning, 530021, China.
- Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China.
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Shi Z, Ma C, Huang X, Cao D. Magnetic Resonance Imaging Radiomics-Based Nomogram From Primary Tumor for Pretreatment Prediction of Peripancreatic Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma: A Multicenter Study. J Magn Reson Imaging 2022; 55:823-839. [PMID: 34997795 DOI: 10.1002/jmri.28048] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 12/15/2021] [Accepted: 12/16/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Determining the absence or presence of peripancreatic lymph nodal metastasis (PLNM) is important to the pathologic staging, prognostication, and guidance of treatment in pancreatic ductal adenocarcinoma (PDAC) patients. Computed tomography and MRI had a poor sensitivity and diagnostic accuracy in the assessment of PLNM. PURPOSES To develop and validate a 3 T MRI primary tumor radiomics-based nomogram from multicenter datasets for pretreatment prediction of the PLNM in PDAC patients. STUDY TYPE Retrospective. SUBJECTS A total of 251 patients (156 men and 95 women; mean age, 60.85 ± 8.23 years) with histologically confirmed pancreatic ductal adenocarcinoma from three hospitals. FIELD STRENGTH AND SEQUENCES A 3.0 T and fat-suppressed T1-weighted imaging. ASSESSMENT Quantitative imaging features were extracted from fat-suppressed T1-weighted (FS T1WI) images at the arterial phase. STATISTICAL TESTS Normally distributed data were compared by using t-tests, while the Mann-Whitney U test was used to evaluate non-normally distributed data. The diagnostic performances of the preoperative and postoperative nomograms were assessed in the external validation cohort with the area under receiver operating characteristics curve (AUC), calibration curve, and decision curve analysis (DCA). AUCs were compared with the De Long test. A p value below 0.05 was considered to be statistically significant. RESULTS The AUCs of magnetic resonance imaging (MRI) Rad-score were 0.868 (95% confidence level [CI]: 0.613-0.852) and 0.772 (95% CI: 0.659-0.879) in the training and internal validation cohort, respectively. The preoperative and postoperative nomograms could accurately predict PLNM in the training cohort (AUC = 0.909 and 0.851) and were validated in both the internal and external cohorts (AUC = 0.835 and 0.805, 0.808 and 0.733, respectively). DCA indicated that the two novel nomograms are of similar clinical usefulness. DATA CONCLUSION Pre-/postoperative nomograms and the constructed radiomics signature from primary tumor based on FS T1WI of arterial phase could serve as a potential tool to predict PLNM in patients with PDAC. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Zhenshan Shi
- Department of Radiology, the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian Province, 350005, China
| | - Chengle Ma
- Department of Radiology, the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian Province, 350005, China
| | - Xinming Huang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, 350005, China
| | - Dairong Cao
- Department of Radiology, the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian Province, 350005, China
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The application of radiomics in predicting gene mutations in cancer. Eur Radiol 2022; 32:4014-4024. [DOI: 10.1007/s00330-021-08520-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 12/11/2021] [Accepted: 12/14/2021] [Indexed: 12/24/2022]
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Zhang J, Wang G, Ren J, Yang Z, Li D, Cui Y, Yang X. Multiparametric MRI-based radiomics nomogram for preoperative prediction of lymphovascular invasion and clinical outcomes in patients with breast invasive ductal carcinoma. Eur Radiol 2022; 32:4079-4089. [DOI: 10.1007/s00330-021-08504-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 12/01/2021] [Accepted: 12/04/2021] [Indexed: 12/22/2022]
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Wang T, Wang H, Wang Y, Liu X, Ling L, Zhang G, Yang G, Zhang H. MR-based radiomics-clinical nomogram in epithelial ovarian tumor prognosis prediction: tumor body texture analysis across various acquisition protocols. J Ovarian Res 2022; 15:6. [PMID: 35022079 PMCID: PMC8753904 DOI: 10.1186/s13048-021-00941-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 12/28/2021] [Indexed: 12/16/2022] Open
Abstract
Background Epithelial ovarian cancer (EOC) is the most malignant gynecological tumor in women. This study aimed to construct and compare radiomics-clinical nomograms based on MR images in EOC prognosis prediction. Methods A total of 186 patients with pathologically proven EOC were enrolled and randomly divided into a training cohort (n = 130) and a validation cohort (n = 56). Clinical characteristics of each patient were retrieved from the hospital information system. A total of 1116 radiomics features were extracted from tumor body on T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (CE-T1WI). Paired sequence signatures were constructed, selected and trained to build a prognosis prediction model. Radiomic-clinical nomogram was constructed based on multivariate logistic regression analysis with radiomics score and clinical features. The predictive performance was evaluated by receiver operating characteristic curve (ROC) analysis, decision curve analysis (DCA) and calibration curve. Results The T2WI radiomic-clinical nomogram achieved a favorable prediction performance in the training and validation cohort with an area under ROC curve (AUC) of 0.866 and 0.818, respectively. The DCA showed that the T2WI radiomic-clinical nomogram was better than other models with a greater clinical net benefit. Conclusion MR-based radiomics analysis showed the high accuracy in prognostic estimation of EOC patients and could help to predict therapeutic outcome before treatment. Supplementary Information The online version contains supplementary material available at 10.1186/s13048-021-00941-7.
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Affiliation(s)
- Tianping Wang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
| | - Haijie Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Yida Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Xuefen Liu
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
| | - Lei Ling
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
| | - Guofu Zhang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - He Zhang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
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