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Van Camp A, Woodruff HC, Cockmartin L, Lobbes M, Majer M, Balleyguier C, Marshall NW, Bosmans H, Lambin P. Impact of synthetic data on training a deep learning model for lesion detection and classification in contrast-enhanced mammography. J Med Imaging (Bellingham) 2025; 12:S22006. [PMID: 40302983 PMCID: PMC12036226 DOI: 10.1117/1.jmi.12.s2.s22006] [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: 10/01/2024] [Revised: 04/02/2025] [Accepted: 04/07/2025] [Indexed: 05/02/2025] Open
Abstract
Purpose Predictive models for contrast-enhanced mammography often perform better at detecting and classifying enhancing masses than (non-enhancing) microcalcification clusters. We aim to investigate whether incorporating synthetic data with simulated microcalcification clusters during training can enhance model performance. Approach Microcalcification clusters were simulated in low-energy images of lesion-free breasts from 782 patients, considering local texture features. Enhancement was simulated in the corresponding recombined images. A deep learning (DL) model for lesion detection and classification was trained with varying ratios of synthetic and real (850 patients) data. In addition, a handcrafted radiomics classifier was trained using delineations and class labels from real data, and predictions from both models were ensembled. Validation was performed on internal (212 patients) and external (279 patients) real datasets. Results The DL model trained exclusively with synthetic data detected over 60% of malignant lesions. Adding synthetic data to smaller real training sets improved detection sensitivity for malignant lesions but decreased precision. Performance plateaued at a detection sensitivity of 0.80. The ensembled DL and radiomics models performed worse than the standalone DL model, decreasing the area under this receiver operating characteristic curve from 0.75 to 0.60 on the external validation set, likely due to falsely detected suspicious regions of interest. Conclusions Synthetic data can enhance DL model performance, provided model setup and data distribution are optimized. The possibility to detect malignant lesions without real data present in the training set confirms the utility of synthetic data. It can serve as a helpful tool, especially when real data are scarce, and it is most effective when complementing real data.
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Affiliation(s)
- Astrid Van Camp
- Maastricht University, GROW – Research Institute for Oncology and Reproduction, Department of Precision Medicine, Maastricht, The Netherlands
- KU Leuven, Division of Medical Physics & Quality Assessment, Department of Imaging and Pathology, Leuven, Belgium
| | - Henry C. Woodruff
- Maastricht University, GROW – Research Institute for Oncology and Reproduction, Department of Precision Medicine, Maastricht, The Netherlands
- Maastricht University Medical Centre+, GROW – Research Institute for Oncology and Reproduction, Department of Radiology and Nuclear Medicine, Maastricht, The Netherlands
| | | | - Marc Lobbes
- Zuyderland Medical Center, Department of Medical Imaging, Sittard-Geleen, The Netherlands
| | - Michael Majer
- Université Paris Saclay, Institut Gustave Roussy, Department of Imaging, Villejuif, France
| | - Corinne Balleyguier
- Université Paris Saclay, Institut Gustave Roussy, Department of Imaging, Villejuif, France
| | - Nicholas W. Marshall
- KU Leuven, Division of Medical Physics & Quality Assessment, Department of Imaging and Pathology, Leuven, Belgium
- UZ Leuven, Department of Radiology, Leuven, Belgium
| | - Hilde Bosmans
- KU Leuven, Division of Medical Physics & Quality Assessment, Department of Imaging and Pathology, Leuven, Belgium
- UZ Leuven, Department of Radiology, Leuven, Belgium
| | - Philippe Lambin
- Maastricht University, GROW – Research Institute for Oncology and Reproduction, Department of Precision Medicine, Maastricht, The Netherlands
- Maastricht University Medical Centre+, GROW – Research Institute for Oncology and Reproduction, Department of Radiology and Nuclear Medicine, Maastricht, The Netherlands
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Michalet M, Valenzuela G, Nougaret S, Tardieu M, Azria D, Riou O. Development of Multiparametric Prognostic Models for Stereotactic Magnetic Resonance Guided Radiation Therapy of Pancreatic Cancers. Int J Radiat Oncol Biol Phys 2025; 122:678-689. [PMID: 40185208 DOI: 10.1016/j.ijrobp.2025.03.039] [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: 12/20/2024] [Revised: 03/17/2025] [Accepted: 03/19/2025] [Indexed: 04/07/2025]
Abstract
PURPOSE Stereotactic magnetic resonance guided adaptive radiation therapy (SMART) is a new option for local treatment of unresectable pancreatic ductal adenocarcinoma, showing interesting survival and local control (LC) results. Despite this, some patients will experience early local and/or metastatic recurrence leading to death. We aimed to develop multiparametric prognostic models for these patients. METHODS AND MATERIALS All patients treated in our institution with SMART for an unresectable pancreatic ductal adenocarcinoma between October 21, 2019, and August 5, 2022 were included. Several initial clinical characteristics as well as dosimetric data of SMART were recorded. Radiomics data from 0.35-T simulation magnetic resonance imaging were extracted. All these data were combined to build prognostic models of overall survival (OS) and LC using machine learning algorithms. RESULTS Eighty-three patients with a median age of 64.9 years were included. A majority of patients had a locally advanced pancreatic cancer (77%). The median OS was 21 months after SMART completion and 27 months after chemotherapy initiation. The 6- and 12-month post-SMART OS was 87.8% (IC95%, 78.2%-93.2%) and 70.9% (IC95%, 58.8%-80.0%), respectively. The best model for OS was the Cox proportional hazard survival analysis using clinical data, with a concordance index inverse probability of censoring weighted of 0.87. Tested on its 12-month OS prediction capacity, this model had good performance (sensitivity 67%, specificity 71%, and area under the curve 0.90). The median LC was not reached. The 6- and 12-month post-SMART LC was 92.4% [IC95%, 83.7%-96.6%] and 76.3% [IC95%, 62.6%-85.5%], respectively. The best model for LC was the component-wise gradient boosting survival analysis using clinical and radiomics data, with a concordance index inverse probability of censoring weighted of 0.80. Tested on its 9-month LC prediction capacity, this model had good performance (sensitivity 50%, specificity 97%, and area under the curve 0.78). CONCLUSIONS Combining clinical and radiomics data in multiparametric prognostic models using machine learning algorithms showed good performance for the prediction of OS and LC. External validation of these models will be needed.
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Affiliation(s)
- Morgan Michalet
- University Federation of Radiation Oncology of Mediterranean Occitanie, Montpellier Cancer Institute, Montpellier University, Montpellier, France; Montpellier Cancer Research Institute, Montpellier University, Montpellier, France.
| | - Gladis Valenzuela
- Montpellier Cancer Research Institute, Montpellier University, Montpellier, France
| | - Stéphanie Nougaret
- Montpellier Cancer Research Institute, Montpellier University, Montpellier, France
| | - Marion Tardieu
- Montpellier Cancer Research Institute, Montpellier University, Montpellier, France
| | - David Azria
- University Federation of Radiation Oncology of Mediterranean Occitanie, Montpellier Cancer Institute, Montpellier University, Montpellier, France; University Federation of Radiation Oncology of Mediterranean Occitanie, Institut de Cancérologie du Gard, Centre Hospitalier Universitaire Carémeau, Nîmes, France; Montpellier Cancer Research Institute, Montpellier University, Montpellier, France
| | - Olivier Riou
- University Federation of Radiation Oncology of Mediterranean Occitanie, Montpellier Cancer Institute, Montpellier University, Montpellier, France; Montpellier Cancer Research Institute, Montpellier University, Montpellier, France
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Raza A, Guzzo A, Ianni M, Lappano R, Zanolini A, Maggiolini M, Fortino G. Federated Learning in radiomics: A comprehensive meta-survey on medical image analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2025; 267:108768. [PMID: 40279838 DOI: 10.1016/j.cmpb.2025.108768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2024] [Revised: 03/03/2025] [Accepted: 04/09/2025] [Indexed: 04/29/2025]
Abstract
Federated Learning (FL) has emerged as a promising approach for collaborative medical image analysis while preserving data privacy, making it particularly suitable for radiomics tasks. This paper presents a systematic meta-analysis of recent surveys on Federated Learning in Medical Imaging (FL-MI), published in reputable venues over the past five years. We adopt the PRISMA methodology, categorizing and analyzing the existing body of research in FL-MI. Our analysis identifies common trends, challenges, and emerging strategies for implementing FL in medical imaging, including handling data heterogeneity, privacy concerns, and model performance in non-IID settings. The paper also highlights the most widely used datasets and a comparison of adopted machine learning models. Moreover, we examine FL frameworks in FL-MI applications, such as tumor detection, organ segmentation, and disease classification. We identify several research gaps, including the need for more robust privacy protection. Our findings provide a comprehensive overview of the current state of FL-MI and offer valuable directions for future research and development in this rapidly evolving field.
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Affiliation(s)
- Asaf Raza
- Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, Rende, Italy
| | - Antonella Guzzo
- Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, Rende, Italy
| | - Michele Ianni
- Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, Rende, Italy.
| | - Rosamaria Lappano
- Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, Rende, Italy
| | - Alfredo Zanolini
- Radiology Unit, "Annunziata" Hospital, Azienda Ospedaliera di Cosenza, Cosenza, Italy
| | - Marcello Maggiolini
- Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, Rende, Italy
| | - Giancarlo Fortino
- Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, Rende, Italy
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Ellmann S, von Rohr F, Komina S, Bayerl N, Amann K, Polifka I, Hartmann A, Sikic D, Wullich B, Uder M, Bäuerle T. Tumor grade-titude: XGBoost radiomics paves the way for RCC classification. Eur J Radiol 2025; 188:112146. [PMID: 40334367 DOI: 10.1016/j.ejrad.2025.112146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2025] [Revised: 04/21/2025] [Accepted: 04/28/2025] [Indexed: 05/09/2025]
Abstract
This study aimed to develop and evaluate a non-invasive XGBoost-based machine learning model using radiomic features extracted from pre-treatment CT images to differentiate grade 4 renal cell carcinoma (RCC) from lower-grade tumours. A total of 102 RCC patients who underwent contrast-enhanced CT scans were included in the analysis. Radiomic features were extracted, and a two-step feature selection methodology was applied to identify the most relevant features for classification. The XGBoost model demonstrated high performance in both training (AUC = 0.87) and testing (AUC = 0.92) sets, with no significant difference between the two (p = 0.521). The model also exhibited high sensitivity, specificity, positive predictive value, and negative predictive value. The selected radiomic features captured both the distribution of intensity values and spatial relationships, which may provide valuable insights for personalized treatment decision-making. Our findings suggest that the XGBoost model has the potential to be integrated into clinical workflows to facilitate personalized adjuvant immunotherapy decision-making, ultimately improving patient outcomes. Further research is needed to validate the model in larger, multicentre cohorts and explore the potential of combining radiomic features with other clinical and molecular data.
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Affiliation(s)
- Stephan Ellmann
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; Radiologisch-Nuklearmedizinisches Zentrum (RNZ), Martin-Richter-Straße 43, 90489 Nürnberg, Germany.
| | - Felicitas von Rohr
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany
| | - Selim Komina
- Institute of Pathology, Faculty of Medicine, Ss Cyril and Methodius University ul. 50 Divizija bb 1000 Skopje, North Macedonia
| | - Nadine Bayerl
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany
| | - Kerstin Amann
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, Germany
| | - Iris Polifka
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; Humanpathologie Dr. Weiß MVZ GmbH, Am Weichselgarten 30a, 91058 Erlangen-Tennenlohe, Germany
| | - Arndt Hartmann
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; Comprehensive Cancer Center Erlangen - EMD, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, Germany
| | - Danijel Sikic
- Clinic of Urology and Pediatric Urology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, Germany
| | - Bernd Wullich
- Clinic of Urology and Pediatric Urology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany; Comprehensive Cancer Center Erlangen - EMD, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, Germany
| | - Michael Uder
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; Comprehensive Cancer Center Erlangen - EMD, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, Germany
| | - Tobias Bäuerle
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; University Medical Center of Johannes Gutenberg-University Mainz, Department of Diagnostic and Interventional Radiology, Langenbeckstr. 1, 55131 Mainz, Germany
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Hage Chehade A, Abdallah N, Marion JM, Hatt M, Oueidat M, Chauvet P. Reconstruction-based approach for chest X-ray image segmentation and enhanced multi-label chest disease classification. Artif Intell Med 2025; 165:103135. [PMID: 40300339 DOI: 10.1016/j.artmed.2025.103135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2024] [Revised: 03/20/2025] [Accepted: 04/13/2025] [Indexed: 05/01/2025]
Abstract
U-Net is a commonly used model for medical image segmentation. However, when applied to chest X-ray images that show pathologies, it often fails to include these critical pathological areas in the generated masks. To address this limitation, in our study, we tackled the challenge of precise segmentation and mask generation by developing a novel approach, using CycleGAN, that encompasses the areas affected by pathologies within the region of interest, allowing the extraction of relevant radiomic features linked to pathologies. Furthermore, we adopted a feature selection approach to focus the analysis on the most significant features. The results of our proposed pipeline are promising, with an average accuracy of 92.05% and an average AUC of 89.48% for the multi-label classification of effusion and infiltration acquired from the ChestX-ray14 dataset, using the XGBoost model. Furthermore, applying our methodology to the classification of the 14 diseases in the ChestX-ray14 dataset resulted in an average AUC of 83.12%, outperforming previous studies. This research highlights the importance of effective pathological mask generation and features selection for accurate classification of chest diseases. The promising results of our approach underscore its potential for broader applications in the classification of chest diseases.
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Affiliation(s)
| | - Nassib Abdallah
- LARIS, University of Angers, France; LaTIM, INSERM UMR 1101, University of Brest, France
| | | | - Mathieu Hatt
- LaTIM, INSERM UMR 1101, University of Brest, France
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Zeng H, Ma Z, Tao Y, Cheng C, Lin J, Fang J, Wei Y, Liu H, Zou F, Cui E, Zhang Y. Predicting early recurrence in hepatocellular carcinoma after hepatectomy using GD-EOB-DTPA enhanced MRI-based model. Eur J Radiol 2025; 188:112130. [PMID: 40305886 DOI: 10.1016/j.ejrad.2025.112130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2025] [Revised: 03/19/2025] [Accepted: 04/22/2025] [Indexed: 05/02/2025]
Abstract
PURPOSE To develop and validate a comprehensive model for predicting postoperative early recurrence of hepatocellular carcinoma (HCC) based on gadoxetate disodium (Gd-EOB-DTPA)-enhanced MRI. METHODS 239 patients with HCC who underwent curative surgical resection were recruited from two centers between April 2017 and December 2022. Radiomics features were extracted from the region of interest (ROI) on preoperative Gd-EOB-DTPA-enhanced MR images, and consistency analysis was performed to select stable radiomics features. Significant variables in the univariate and multivariate logistic regression analysis were included in clinical-radiologic model. Nomograms were constructed by combining the best performing radiologic and clinical-radiologic characteristics. Recurrence-free survival (RFS) comparisons were conducted using the log-rank test based on high versus low model-derived scores. RESULTS The radiomics model based on multiple phases MR outperformed all other radiomics models and had the best discrimination for early recurrence, with AUC of 0.799 and 0.743 in the training and validation sets, respectively. In the entire cohort, high-risk patients exhibited significantly lower RFS compared to low-risk patients. CONCLUSION The nomogram integrating Gd-EOB-DTPA enhanced MRI radiomics features and clinical-radiologic characteristics demonstrate superior predictive performance with postoperative early recurrence in patients with HCC. The model can identify patients at high risk and provide support for individualized treatment planning.
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Affiliation(s)
- Hanqiu Zeng
- Department of Radiology, the Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai, China
| | - Zichang Ma
- Department of Radiology, the Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai, China
| | - Yuxi Tao
- Department of Radiology, the Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai, China
| | - Ci Cheng
- Department of Radiology, the Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai, China
| | - Junyu Lin
- Department of Radiology, the Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai, China
| | - Jiayu Fang
- Department of Radiology, the Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai, China
| | - Yuhan Wei
- Department of Radiology, the Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai, China
| | - Huajin Liu
- Department of Radiology, the Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai, China
| | - Feixiang Zou
- Department of Radiology, People's Hospital of Wuchuan Gelao and Miao Autonomous County, Zunyi 5643000 Guizhou, China
| | - Enming Cui
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, China.
| | - Yaqin Zhang
- Department of Radiology, the Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai, China.
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Shang LQ, Guo HX, Wang P, Sun XH, You JQ, Ma JT, Wang LK, Liu JX, Wang ZQ, Shao HB. Global scientific trends on hepatocellular carcinoma research from 2004 to 2023: A bibliometric and visualized analysis. World J Gastrointest Oncol 2025; 17:105781. [DOI: 10.4251/wjgo.v17.i6.105781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2025] [Revised: 03/22/2025] [Accepted: 04/17/2025] [Indexed: 06/13/2025] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is a major cause of cancer-related mortality worldwide, and the research landscape has rapidly evolved over the past two decades. Despite significant progress, an in-depth analysis of global research trends, collaborative networks, and emerging themes in HCC remains limited. This study aimed to fill this gap by conducting a bibliometric analysis to map the research output, identify key contributors, and highlight future directions in HCC research. We hypothesized that the analysis would reveal a growing focus on molecular mechanisms and immunotherapy, with increasing contributions from specific countries and institutions.
AIM To investigate global research trends, collaborative networks, and emerging themes in HCC from 2004 to 2023.
METHODS A bibliometric analysis was performed using 93987 publications from the Science Citation Index Expanded Database of the Web of Science Core Collection. Data were analyzed using the VOSviewer software to identify publication trends, leading contributors, and research themes. Key metrics included annual publication output, country and institutional contributions, journal impact, and thematic clusters. Statistical analysis was carried out to quantify trends and collaborations.
RESULTS The number of annual publications increased from 2341 in 2004 to 8756 in 2023, with 65583 papers (69.78%) published between 2014 and 2023. China, the United States, and Japan were the top contributors, constituting 58.3% of total publications. PLOS One published the most studies (n = 2145), while Gastroenterology had the highest average number of citations (78.4 citations per paper). Fudan University was the most prolific institution (n = 1872). Thematic analysis identified five main clusters, namely molecular mechanisms, therapeutic strategies, prognosis and immunology, risk factors, and diagnostic approaches.
CONCLUSION This study highlights the growing focus on HCC research, particularly in immunotherapy and molecular mechanisms, underscoring the significance of international collaboration to advance diagnosis and treatment strategies.
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Affiliation(s)
- Li-Qi Shang
- Department of Interventional Radiology, The First Hospital of China Medical University, Shenyang 110000, Liaoning Province, China
| | - Hao-Xin Guo
- Department of Information Center, The First Hospital of China Medical University, Shenyang 110000, Liaoning Province, China
| | - Peng Wang
- Department of Interventional Radiology, The First Hospital of China Medical University, Shenyang 110000, Liaoning Province, China
| | - Xiao-Han Sun
- Department of Interventional Radiology, The First Hospital of China Medical University, Shenyang 110000, Liaoning Province, China
| | - Jia-Qi You
- Department of Interventional Radiology, The First Hospital of China Medical University, Shenyang 110000, Liaoning Province, China
| | - Jun-Ting Ma
- Department of Interventional Radiology, The First Hospital of China Medical University, Shenyang 110000, Liaoning Province, China
| | - Lu-Ke Wang
- Department of Interventional Radiology, The First Hospital of China Medical University, Shenyang 110000, Liaoning Province, China
| | - Jia-Xi Liu
- Department of Interventional Radiology, The First Hospital of China Medical University, Shenyang 110000, Liaoning Province, China
| | - Zhong-Qing Wang
- Department of Information Center, The First Hospital of China Medical University, Shenyang 110000, Liaoning Province, China
| | - Hai-Bo Shao
- Department of Interventional Radiology, The First Hospital of China Medical University, Shenyang 110000, Liaoning Province, China
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Huang S, Liu D, Deng K, Shu C, Wu Y, Zhou Z. A computed tomography angiography-based radiomics model for prognostic prediction of endovascular abdominal aortic repair. Int J Cardiol 2025; 429:133138. [PMID: 40090490 DOI: 10.1016/j.ijcard.2025.133138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2025] [Revised: 02/25/2025] [Accepted: 03/06/2025] [Indexed: 03/18/2025]
Abstract
OBJECTIVE This study aims to develop a radiomics machine learning (ML) model that uses preoperative computed tomography angiography (CTA) data to predict the prognosis of endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA) patients. METHODS In this retrospective study, 164 AAA patients underwent EVAR and were categorized into shrinkage (good prognosis) or stable (poor prognosis) groups based on post-EVAR sac regression. From preoperative AAA and perivascular adipose tissue (PVAT) image, radiomics features (RFs) were extracted for model creation. Patients were split into 80 % training and 20 % test sets. A support vector machine model was constructed for prediction. Accuracy is evaluated via the area under the receiver operating characteristic curve (AUC). RESULTS Demographics and comorbidities showed no significant differences between shrinkage and stable groups. The model containing 5 AAA RFs (which are original_firstorder_InterquartileRange, log-sigma-3-0-mm-3D_glrlm_GrayLevelNonUniformityNormalized, log-sigma-3-0-mm-3D_glrlm_RunPercentage, log-sigma-4-0-mm-3D_glrlm_ShortRunLowGrayLevelEmphasis, wavelet-LLH_glcm_SumEntropy) had AUCs of 0.86 (training) and 0.77 (test). The model containing 7 PVAT RFs (which are log-sigma-3-0-mm-3D_firstorder_InterquartileRange, log-sigma-3-0-mm-3D_glcm_Correlation, wavelet-LHL_firstorder_Energy, wavelet-LHL_firstorder_TotalEnergy, wavelet-LHH_firstorder_Mean, wavelet-LHH_glcm_Idmn, wavelet-LHH_glszm_GrayLevelNonUniformityNormalized) had AUCs of 0.76 (training) and 0.78 (test). Combining AAA and PVAT RFs yielded the highest accuracy: AUCs of 0.93 (training) and 0.87 (test). CONCLUSIONS Radiomics-based CTA model predicts aneurysm sac regression post-EVAR in AAA patients. PVAT RFs from preoperative CTA images were closely related to AAA prognosis after EVAR, enhancing accuracy when combined with AAA RFs. This preliminary study explores a predictive model designed to assist clinicians in optimizing therapeutic strategies during clinical decision-making processes.
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Affiliation(s)
- Shanya Huang
- National Clinical Research Center for Metabolic Diseases, Metabolic Syndrome Research Center, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha, 410011, Hunan, China; Department of Ultrasound, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Dingxiao Liu
- Department of Vascular Surgery, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Kai Deng
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Chang Shu
- Department of Vascular Surgery, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China; Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Yan Wu
- National Clinical Research Center for Metabolic Diseases, Metabolic Syndrome Research Center, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha, 410011, Hunan, China.
| | - Zhiguang Zhou
- National Clinical Research Center for Metabolic Diseases, Metabolic Syndrome Research Center, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha, 410011, Hunan, China.
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Wei Z, Yu R, Zhang Y, Wang Y, Wang J, Xie C, Chen X. A CT-based radiomic model for predicting vertebral fractures in older patients with type 2 diabetes mellitus: A longitudinal study. J Endocrinol Invest 2025:10.1007/s40618-025-02627-z. [PMID: 40493166 DOI: 10.1007/s40618-025-02627-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2025] [Accepted: 06/05/2025] [Indexed: 06/12/2025]
Affiliation(s)
- Zicheng Wei
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China
| | - Rui Yu
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China
| | - Yiping Zhang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China
| | - Yu Wang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China
| | - Jianhua Wang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China
| | - Cao Xie
- Center for Musculoskeletal Research, School of Medicine and Dentistry, University of Rochester, Rochester, NY, 14642, USA
| | - Xiao Chen
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China.
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Ra S, Kim J, Na I, Ko ES, Park H. Enhancing radiomics features via a large language model for classifying benign and malignant breast tumors in mammography. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2025; 265:108765. [PMID: 40203779 DOI: 10.1016/j.cmpb.2025.108765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2025] [Revised: 03/27/2025] [Accepted: 04/03/2025] [Indexed: 04/11/2025]
Abstract
BACKGROUND AND OBJECTIVES Radiomics is widely used to assist in clinical decision-making, disease diagnosis, and treatment planning for various target organs, including the breast. Recent advances in large language models (LLMs) have helped enhance radiomics analysis. MATERIALS AND METHODS Herein, we sought to improve radiomics analysis by incorporating LLM-learned clinical knowledge, to classify benign and malignant tumors in breast mammography. We extracted radiomics features from the mammograms based on the region of interest and retained the features related to the target task. Using prompt engineering, we devised an input sequence that reflected the selected features and the target task. The input sequence was fed to the chosen LLM (LLaMA variant), which was fine-tuned using low-rank adaptation to enhance radiomics features. This was then evaluated on two mammogram datasets (VinDr-Mammo and INbreast) against conventional baselines. RESULTS The enhanced radiomics-based method performed better than baselines using conventional radiomics features tested on two mammogram datasets, achieving accuracies of 0.671 for the VinDr-Mammo dataset and 0.839 for the INbreast dataset. Conventional radiomics models require retraining from scratch for an unseen dataset using a new set of features. In contrast, the model developed in this study effectively reused the common features between the training and unseen datasets by explicitly linking feature names with feature values, leading to extensible learning across datasets. Our method performed better than the baseline method in this retraining setting using an unseen dataset. CONCLUSIONS Our method, one of the first to incorporate LLM into radiomics, has the potential to improve radiomics analysis.
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Affiliation(s)
- Sinyoung Ra
- Department of Artificial Intelligence, Sungkyunkwan University, Suwon, Republic of Korea
| | - Jonghun Kim
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Inye Na
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Eun Sook Ko
- Samsung Medical Center, Department of Radiology, School of Medicine, Sungkyunkwan University, Seoul, Republic of Korea
| | - Hyunjin Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea.
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Hertel A, Froelich MF, Overhoff D, Nestler T, Faby S, Jürgens M, Schmidt B, Vellala A, Hesse A, Nörenberg D, Stoll R, Schmelz H, Schoenberg SO, Waldeck S. Radiomics-driven spectral profiling of six kidney stone types with monoenergetic CT reconstructions in photon-counting CT. Eur Radiol 2025; 35:3120-3130. [PMID: 39665989 PMCID: PMC12081576 DOI: 10.1007/s00330-024-11262-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Revised: 09/25/2024] [Accepted: 11/04/2024] [Indexed: 12/13/2024]
Abstract
OBJECTIVES Urolithiasis, a common and painful urological condition, is influenced by factors such as lifestyle, genetics, and medication. Differentiating between different types of kidney stones is crucial for personalized therapy. The purpose of this study is to investigate the use of photon-counting computed tomography (PCCT) in combination with radiomics and machine learning to develop a method for automated and detailed characterization of kidney stones. This approach aims to enhance the accuracy and detail of stone classification beyond what is achievable with conventional computed tomography (CT) and dual-energy CT (DECT). MATERIALS AND METHODS In this ex vivo study, 135 kidney stones were first classified using infrared spectroscopy. All stones were then scanned in a PCCT embedded in a phantom. Various monoenergetic reconstructions were generated, and radiomics features were extracted. Statistical analysis was performed using Random Forest (RF) classifiers for both individual reconstructions and a combined model. RESULTS The combined model, using radiomics features from all monoenergetic reconstructions, significantly outperformed individual reconstructions and SPP parameters, with an AUC of 0.95 and test accuracy of 0.81 for differentiating all six stone types. Feature importance analysis identified key parameters, including NGTDM_Strength and wavelet-LLH_firstorder_Variance. CONCLUSION This ex vivo study demonstrates that radiomics-driven PCCT analysis can improve differentiation between kidney stone subtypes. The combined model outperformed individual monoenergetic levels, highlighting the potential of spectral profiling in PCCT to optimize treatment through image-based strategies. KEY POINTS Question How can photon-counting computed tomography (PCCT) combined with radiomics improve the differentiation of kidney stone types beyond conventional CT and dual-energy CT, enhancing personalized therapy? Findings Our ex vivo study demonstrates that a combined spectral-driven radiomics model achieved 95% AUC and 81% test accuracy in differentiating six kidney stone types. Clinical relevance Implementing PCCT-based spectral-driven radiomics allows for precise non-invasive differentiation of kidney stone types, leading to improved diagnostic accuracy and more personalized, effective treatment strategies, potentially reducing the need for invasive procedures and recurrence.
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Affiliation(s)
- Alexander Hertel
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, University of Heidelberg, Mannheim, Germany.
| | - Matthias F Froelich
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, University of Heidelberg, Mannheim, Germany
| | - Daniel Overhoff
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, University of Heidelberg, Mannheim, Germany
- Department of Diagnostic and Interventional Radiology, Federal Armed Services Hospital Koblenz, Koblenz, Germany
| | - Tim Nestler
- Department of Urology, Federal Armed Services Hospital Koblenz, Koblenz, Germany
- Department of Urology, University Hospital Cologne, Cologne, Germany
| | | | | | | | - Abhinay Vellala
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, University of Heidelberg, Mannheim, Germany
| | | | - Dominik Nörenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, University of Heidelberg, Mannheim, Germany
| | - Rico Stoll
- Department of Urology, Federal Armed Services Hospital Koblenz, Koblenz, Germany
| | - Hans Schmelz
- Department of Urology, Federal Armed Services Hospital Koblenz, Koblenz, Germany
| | - Stefan O Schoenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, University of Heidelberg, Mannheim, Germany
| | - Stephan Waldeck
- Department of Diagnostic and Interventional Radiology, Federal Armed Services Hospital Koblenz, Koblenz, Germany
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12
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Peng M, Wang M, An W, Wu T, Zhang Y, Ge F, Cheng L, Liu W, Wang K. Predictive classification of lung cancer pathological based on PET/CT radiomics. Jpn J Radiol 2025; 43:1007-1024. [PMID: 39998736 DOI: 10.1007/s11604-025-01742-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2024] [Accepted: 01/17/2025] [Indexed: 02/27/2025]
Abstract
OBJECTIVES To develop and validate a combined clinical and radiomics model for non-invasive prediction of lung cancer (LC) pathological types (lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung cancer) based on patients' pre-treatment FDG PET/CT images and clinical data, as a complementary tool to aid in the diagnosis of LC pathological histological classification. METHODS In total, 896 patients with pathological confirmation of lung cancer were part of this retrospective study. The training and test groups included 819 patients who underwent scanning using scanner 1. The independent validation group included 77 patients who using scanner 2. The optimal features were retained by least absolute shrinkage and selection operator algorithm dimensionality reduction screening of the collected radiomics features, clinical parameters, and PET metabolic parameters. Five models were established to predict the lung cancer pathological types by the k-nearest neighbor classification (KNN) algorithm. The performance of the prediction model was assessed by calculating the area under the curve (AUC) from the receiver operator characteristic curve (ROC). RESULTS Of all five predictive models (the PET-only radiomics model, the CT-only radiomics model, the PET/CT radiomics model, the clinical-only model and the combined clinical and PET/CT radiomics model), the clinical combined PET/CT radiomics model exhibited best performance. The macro-AUC for the training, test and independent validation groups were 0.974, 0.931, 0.960, the micro-AUC were 0.976, 0.940, 0.970, and the accuracy were 0.963, 0.914, and 0.961, respectively. CONCLUSIONS Our model combined radiomics and clinical data and showed higher performance in non-invasively predicting the LC pathological types, which suggesting that PET/CT radiomics may be a promising technique for predicting LC histopathology.
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Affiliation(s)
- Mengye Peng
- PET-CT/MRI Department, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, China
| | - Menglu Wang
- PET-CT/MRI Department, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, China
| | - Wenxin An
- Department of Urology, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, China
| | - Tingting Wu
- PET-CT/MRI Department, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, China
| | - Ying Zhang
- PET-CT/MRI Department, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, China
| | - Fan Ge
- PET-CT/MRI Department, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, China
| | - Liang Cheng
- PET-CT/MRI Department, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, China
| | - Wei Liu
- PET-CT/MRI Department, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, China
| | - Kezheng Wang
- PET-CT/MRI Department, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, China.
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Jannatdoust P, Valizadeh P, Saeedi N, Valizadeh G, Salari HM, Saligheh Rad H, Gity M. Computer-Aided Detection (CADe) and Segmentation Methods for Breast Cancer Using Magnetic Resonance Imaging (MRI). J Magn Reson Imaging 2025; 61:2376-2390. [PMID: 39781684 DOI: 10.1002/jmri.29687] [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: 03/24/2024] [Revised: 11/30/2024] [Accepted: 12/02/2024] [Indexed: 01/12/2025] Open
Abstract
Breast cancer continues to be a major health concern, and early detection is vital for enhancing survival rates. Magnetic resonance imaging (MRI) is a key tool due to its substantial sensitivity for invasive breast cancers. Computer-aided detection (CADe) systems enhance the effectiveness of MRI by identifying potential lesions, aiding radiologists in focusing on areas of interest, extracting quantitative features, and integrating with computer-aided diagnosis (CADx) pipelines. This review aims to provide a comprehensive overview of the current state of CADe systems in breast MRI, focusing on the technical details of pipelines and segmentation models including classical intensity-based methods, supervised and unsupervised machine learning (ML) approaches, and the latest deep learning (DL) architectures. It highlights recent advancements from traditional algorithms to sophisticated DL models such as U-Nets, emphasizing CADe implementation of multi-parametric MRI acquisitions. Despite these advancements, CADe systems face challenges like variable false-positive and negative rates, complexity in interpreting extensive imaging data, variability in system performance, and lack of large-scale studies and multicentric models, limiting the generalizability and suitability for clinical implementation. Technical issues, including image artefacts and the need for reproducible and explainable detection algorithms, remain significant hurdles. Future directions emphasize developing more robust and generalizable algorithms, integrating explainable AI to improve transparency and trust among clinicians, developing multi-purpose AI systems, and incorporating large language models to enhance diagnostic reporting and patient management. Additionally, efforts to standardize and streamline MRI protocols aim to increase accessibility and reduce costs, optimizing the use of CADe systems in clinical practice. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Payam Jannatdoust
- School of Medicine, Tehran University of Medical Science, Tehran, Iran
| | - Parya Valizadeh
- School of Medicine, Tehran University of Medical Science, Tehran, Iran
| | - Nikoo Saeedi
- Student Research Committee, Islamic Azad University, Mashhad Branch, Mashhad, Iran
| | - Gelareh Valizadeh
- Quantitative MR Imaging and Spectroscopy Group (QMISG), Tehran University of Medical Sciences, Tehran, Iran
| | - Hanieh Mobarak Salari
- Quantitative MR Imaging and Spectroscopy Group (QMISG), Tehran University of Medical Sciences, Tehran, Iran
| | - Hamidreza Saligheh Rad
- Quantitative MR Imaging and Spectroscopy Group (QMISG), Tehran University of Medical Sciences, Tehran, Iran
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
| | - Masoumeh Gity
- Advanced Diagnostic and Interventional Radiology Research Center, Tehran University of Medical Sciences, Tehran, Iran
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14
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Fang Y, Zhang Q, Yan J, Yu S. Application of radiomics in acute and severe non-neoplastic diseases: A literature review. J Crit Care 2025; 87:155027. [PMID: 39848114 DOI: 10.1016/j.jcrc.2025.155027] [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: 11/01/2024] [Accepted: 01/10/2025] [Indexed: 01/25/2025]
Abstract
Radiomics involves the integration of computer technology, big data analysis, and clinical medicine. Currently, there have been initial advancements in the fields of acute cerebrovascular disease and cardiovascular disease. The objective of radiomics is to extract quantitative features from medical images for analysis to predict the risk or treatment outcome, help in differential diagnosis, and guide clinical decisions and management. Radiomics applied research has reached a more advanced stage yet encounters several obstacles, including the need for standardization of radiomics features and alignment with treatment requirements for acute and severe illnesses. Future research should aim to seamlessly incorporate radiomics with various disciplines, leverage big data and artificial intelligence advancements, cater to the requirements of acute and critical medicine, and enhance the effectiveness of technological innovation and application in diagnosing and treating acute and critical illnesses.
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Affiliation(s)
- Yu Fang
- Department of Emergency Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China; Department of Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
| | - Qiannan Zhang
- Department of Emergency Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
| | - Jingjun Yan
- Department of Emergency Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China; Department of Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
| | - Shanshan Yu
- Department of Emergency Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China; Department of Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China.
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15
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Long B, Li R, Wang R, Yin A, Zhuang Z, Jing Y, E L. A computed tomography-based deep learning radiomics model for predicting the gender-age-physiology stage of patients with connective tissue disease-associated interstitial lung disease. Comput Biol Med 2025; 191:110128. [PMID: 40209580 DOI: 10.1016/j.compbiomed.2025.110128] [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: 04/19/2024] [Revised: 03/29/2025] [Accepted: 04/01/2025] [Indexed: 04/12/2025]
Abstract
OBJECTIVES To explore the feasibility of using a diagnostic model constructed with deep learning-radiomics (DLR) features extracted from chest computed tomography (CT) images to predict the gender-age-physiology (GAP) stage of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD). MATERIALS AND METHODS The data of 264 CTD-ILD patients were retrospectively collected. GAP Stage I, II, III patients are 195, 56, 13 cases respectively. The latter two stages were combined into one group. The patients were randomized into a training set and a validation set. Single-input models were separately constructed using the selected radiomics and DL features, while DLR model was constructed from both sets of features. For all models, the support vector machine (SVM) and logistic regression (LR) algorithms were used for construction. The nomogram models were generated by integrating age, gender, and DLR features. RESULTS The DLR model outperformed the radiomics and DL models in both the training set and the validation set. The predictive performance of the DLR model based on the LR algorithm was the best among all the feature-based models (AUC = 0.923). The comprehensive models had even greater performance in predicting the GAP stage of CTD-ILD patients. The comprehensive model using the SVM algorithm had the best performance of the two models (AUC = 0.951). CONCLUSION The DLR model extracted from CT images can assist in the clinical prediction of the GAP stage of CTD-ILD patients. A nomogram showed even greater performance in predicting the GAP stage of CTD-ILD patients.
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Affiliation(s)
- Bingqing Long
- Department of Radiology, People's Hospital of Longhua, Shenzhen, 518109, China.
| | - Rui Li
- Department of Radiology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan, 030032, China.
| | - Ronghua Wang
- Department of Radiology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan, 030032, China.
| | - Anyu Yin
- Department of Radiology, People's Hospital of Longhua, Shenzhen, 518109, China.
| | - Ziyi Zhuang
- Department of Radiology, People's Hospital of Longhua, Shenzhen, 518109, China.
| | - Yang Jing
- Huiying Medical Technology Co., Ltd., Room A206, B2, Dongsheng Science and Technology Park, Haidian District, Beijing, 100192, China.
| | - Linning E
- Department of Radiology, People's Hospital of Longhua, Shenzhen, 518109, China.
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16
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Tartari C, Porões F, Schmidt S, Abler D, Vetterli T, Depeursinge A, Dromain C, Violi NV, Jreige M. MRI and CT radiomics for the diagnosis of acute pancreatitis. Eur J Radiol Open 2025; 14:100636. [PMID: 39967811 PMCID: PMC11833635 DOI: 10.1016/j.ejro.2025.100636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2024] [Revised: 01/17/2025] [Accepted: 01/22/2025] [Indexed: 02/20/2025] Open
Abstract
Purpose To evaluate the single and combined diagnostic performances of CT and MRI radiomics for diagnosis of acute pancreatitis (AP). Materials and methods We prospectively enrolled 78 patients (mean age 55.7 ± 17 years, 48.7 % male) diagnosed with AP between 2020 and 2022. Patients underwent contrast-enhanced CT (CECT) within 48-72 h of symptoms and MRI ≤ 24 h after CECT. The entire pancreas was manually segmented tridimensionally by two operators on portal venous phase (PVP) CECT images, T2-weighted imaging (WI) MR sequence and non-enhanced and PVP T1-WI MR sequences. A matched control group (n = 77) with normal pancreas was used. Dataset was randomly split into training and test, and various machine learning algorithms were compared. Receiver operating curve analysis was performed. Results The T2WI model exhibited significantly better diagnostic performance than CECT and non-enhanced and venous T1WI, with sensitivity, specificity and AUC of 73.3 % (95 % CI: 71.5-74.7), 80.1 % (78.2-83.2), and 0.834 (0.819-0.844) for T2WI (p = 0.001), 74.4 % (71.5-76.4), 58.7 % (56.3-61.1), and 0.654 (0.630-0.677) for non-enhanced T1WI, 62.1 % (60.1-64.2), 78.7 % (77.1-81), and 0.787 (0.771-0.810) for venous T1WI, and 66.4 % (64.8-50.9), 48.4 % (46-50.9), and 0.610 (0.586-0.626) for CECT, respectively.The combination of T2WI with CECT enhanced diagnostic performance compared to T2WI, achieving sensitivity, specificity and AUC of 81.4 % (80-80.3), 78.1 % (75.9-80.2), and 0.911 (0.902-0.920) (p = 0.001). Conclusion The MRI radiomics outperformed the CT radiomics model to detect diagnosis of AP and the combination of MRI with CECT showed better performance than single models. The translation of radiomics into clinical practice may improve detection of AP, particularly MRI radiomics.
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Affiliation(s)
- Caterina Tartari
- Department of Radiology and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Fabio Porões
- Department of Radiology and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Sabine Schmidt
- Department of Radiology and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Daniel Abler
- Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Department of Oncology, Geneva University Hospitals, Geneva, Switzerland
| | - Thomas Vetterli
- Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
| | - Adrien Depeursinge
- Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Department of Oncology, Geneva University Hospitals, Geneva, Switzerland
| | - Clarisse Dromain
- Department of Radiology and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Naïk Vietti Violi
- Department of Radiology and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Mario Jreige
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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17
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Liu H, Meng X, Wang G, Yao S, Wang Y, Wang R, Wang T. Differentiating second primary lung cancer from pulmonary metastasis in patients of single solitary pulmonary lesion with extrapulmonary tumor using multiparametric analysis of FDG PET/CT. Ann Nucl Med 2025; 39:567-575. [PMID: 40042775 PMCID: PMC12095392 DOI: 10.1007/s12149-025-02034-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2024] [Accepted: 02/24/2025] [Indexed: 05/22/2025]
Abstract
OBJECTIVE Using fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT), a multiparametric analysis will be performed in the differential diagnosis of patients with single solitary pulmonary lesion and extrapulmonary malignant tumor to discriminate between a second primary lung cancer (SPLC) and pulmonary metastasis (PM). METHODS This study retrospectively studied 84 patients with preoperative exams utilizing 18F-FDG PET/CT. Using complementing PET/CT parameters, a composite model was developed. A receiver operating characteristic (ROC) analysis assessed the combined model and each independent parameter's differential diagnostic efficacies. Furthermore, this study investigated the improvement in diagnostic efficacy using other metrics, such as integrated discriminatory improvement (IDI) and net reclassification improvement (NRI). RESULTS The highest discriminative diagnostic value was obtained by the independent parameters energy (1,039,358.1 [95126.2-1,965,032.2] vs. 92,011.0 [45916.3-365,322.9], P = 0.001). In comparison to peak standardized uptake value (SUVpeak), total lesion glycolysis (TLG), energy, lobulation, and spiculation alone, the combined model (addition of these factors) significantly improved the differential diagnostic efficacy of SPLCs and PMs (sensitivity = 76.2%, specificity = 83.8%, area under the curve [AUC] = 0.826) and permitted reclassification using IDI = 0.176 (P < 0.001), 0.169 (P < 0.001), 0.127 (P < 0.001), and categorical NRI = 0.678 (P < 0.001), 0.637 (P < 0.001), and 0.592 (P < 0.001) compared to SUVpeak, TLG and energy separately. DeLong's test revealed a statistically significant enhancement in ROC when compared to SUVpeak (Z = 2.372, P = 0.018), TLG (Z = 2.095, P = 0.036), and energy (Z = 2.318, P = 0.020). CONCLUSION Combining multiple parameters using 18F-FDG PET/CT may further improve distinguishing between SPLCs and PMs in patients with single solitary pulmonary lesion and extrapulmonary malignant tumor.
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Affiliation(s)
- Honghong Liu
- Departments of Nuclear Medicine, the First Medical Center, Chinese PLA General Hospital, No.28 Fuxing Road, Beijing, 100853, China
| | - Xiaolin Meng
- Departments of Nuclear Medicine, the First Medical Center, Chinese PLA General Hospital, No.28 Fuxing Road, Beijing, 100853, China
| | - Guanyun Wang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Shulin Yao
- Departments of Nuclear Medicine, the First Medical Center, Chinese PLA General Hospital, No.28 Fuxing Road, Beijing, 100853, China
| | - Yanmei Wang
- GE Healthcare China, Pudong New Town, Shanghai, 200125, China
| | - Ruimin Wang
- Departments of Nuclear Medicine, the First Medical Center, Chinese PLA General Hospital, No.28 Fuxing Road, Beijing, 100853, China.
| | - Tao Wang
- Departments of Thoracic Surgery, the First Medical Center, Chinese PLA General Hospital, No.28 Fuxing Road, Beijing, 100853, China.
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Ammari S, Quillent A, Elvira V, Bidault F, Garcia GCTE, Hartl DM, Balleyguier C, Lassau N, Chouzenoux É. Using Machine Learning on MRI Radiomics to Diagnose Parotid Tumours Before Comparing Performance with Radiologists: A Pilot Study. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2025; 38:1496-1508. [PMID: 39390287 DOI: 10.1007/s10278-024-01255-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 07/31/2024] [Accepted: 08/19/2024] [Indexed: 10/12/2024]
Abstract
The parotid glands are the largest of the major salivary glands. They can harbour both benign and malignant tumours. Preoperative work-up relies on MR images and fine needle aspiration biopsy, but these diagnostic tools have low sensitivity and specificity, often leading to surgery for diagnostic purposes. The aim of this paper is (1) to develop a machine learning algorithm based on MR images characteristics to automatically classify parotid gland tumours and (2) compare its results with the diagnoses of junior and senior radiologists in order to evaluate its utility in routine practice. While automatic algorithms applied to parotid tumours classification have been developed in the past, we believe that our study is one of the first to leverage four different MRI sequences and propose a comparison with clinicians. In this study, we leverage data coming from a cohort of 134 patients treated for benign or malignant parotid tumours. Using radiomics extracted from the MR images of the gland, we train a random forest and a logistic regression to predict the corresponding histopathological subtypes. On the test set, the best results are given by the random forest: we obtain a 0.720 accuracy, a 0.860 specificity, and a 0.720 sensitivity over all histopathological subtypes, with an average AUC of 0.838. When considering the discrimination between benign and malignant tumours, the algorithm results in a 0.760 accuracy and a 0.769 AUC, both on test set. Moreover, the clinical experiment shows that our model helps to improve diagnostic abilities of junior radiologists as their sensitivity and accuracy raised by 6 % when using our proposed method. This algorithm may be useful for training of physicians. Radiomics with a machine learning algorithm may help improve discrimination between benign and malignant parotid tumours, decreasing the need for diagnostic surgery. Further studies are warranted to validate our algorithm for routine use.
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Affiliation(s)
- Samy Ammari
- Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, 94805, Villejuif, France
- Department of Imaging, Gustave Roussy Cancer Campus, Université Paris Saclay, 94805, Villejuif, France
| | - Arnaud Quillent
- Centre de Vision Numérique, OPIS, CentraleSupélec, Inria, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Víctor Elvira
- School of Mathematics, University of Edinburgh, Edinburgh, EH9 3FD, UK
| | - François Bidault
- Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, 94805, Villejuif, France
- Department of Imaging, Gustave Roussy Cancer Campus, Université Paris Saclay, 94805, Villejuif, France
| | - Gabriel C T E Garcia
- Department of Imaging, Gustave Roussy Cancer Campus, Université Paris Saclay, 94805, Villejuif, France
| | - Dana M Hartl
- Department of Otolaryngology Head and Neck Surgery, Gustave Roussy Cancer Campus, Université Paris Saclay, 94805, Villejuif, France
| | - Corinne Balleyguier
- Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, 94805, Villejuif, France
- Department of Imaging, Gustave Roussy Cancer Campus, Université Paris Saclay, 94805, Villejuif, France
| | - Nathalie Lassau
- Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, 94805, Villejuif, France
- Department of Imaging, Gustave Roussy Cancer Campus, Université Paris Saclay, 94805, Villejuif, France
| | - Émilie Chouzenoux
- Centre de Vision Numérique, OPIS, CentraleSupélec, Inria, Université Paris-Saclay, 91190, Gif-sur-Yvette, France.
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Peng M, Yang X, Wang Y, Zhou L, Ge F, Liu S, Liu W, Cheng L, Wang K. Clinical combined PET/CT radiomics model prediction of benefit from platinum-based chemotherapy and chemoradiotherapy in patients with small cell lung cancer. Nucl Med Commun 2025; 46:558-569. [PMID: 40084524 DOI: 10.1097/mnm.0000000000001971] [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] [Indexed: 03/16/2025]
Abstract
OBJECTIVE To develop and validate a clinical combined radiomics model for predicting the treatment response and long-term survival prognosis of small cell lung cancer (SCLC) patients receiving platinum-based chemotherapy, as well as survival outcomes following chemoradiotherapy. METHODS A total of 98 SCLC patients treated with platinum-based first-line chemotherapy were included in this study. Five prediction models for assessing the short-term efficacy of platinum-based first-line chemotherapy were developed using a logistic regression algorithm. The performance of the models was assessed by calculating the area under the curve of the receiver operating characteristic curves. For predicting progression-free survival (PFS) and overall survival in the platinum-based chemotherapy group and the chemoradiotherapy group, the optimal cutoff value was determined using X-tile software. Kaplan-Meier survival curves were plotted, and the log-rank test was used to compare survival outcomes. RESULTS Among the five models for predicting short-term efficacy, the clinical combined positron emission tomography/computed tomography (PET/CT) radiomics model performed the best, achieving areas under the curve of 0.832 and 0.833 for the training and test sets, respectively. The Kaplan-Meier survival analysis indicated that both the high-scoring Combine group and high-scoring PET/CT group were significantly associated with worse PFS and worse overall survival in the platinum-only chemotherapy group. Additionally, the high-scoring CT group was significantly associated with worse PFS in the chemoradiotherapy group. CONCLUSION The clinical combined PET/CT radiomics model can noninvasively and accurately predict the response to platinum-based treatments in SCLC as well as long-term survival prognosis, which can contribute to personalized treatment strategies and guide precision therapy for SCLC patients.
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Affiliation(s)
- Mengye Peng
- PET-CT/MRI Department, Harbin Medical University Cancer Hospital, Harbin
| | - Xinyue Yang
- PET-CT/MRI Department, Harbin Medical University Cancer Hospital, Harbin
| | - Yanmei Wang
- Scientific Research Center Department, Beijing General Electric Company, Beijing
| | - Liangqin Zhou
- Imaging Center Department, Harbin Medical University Cancer Hospital, Harbin, China
| | - Fan Ge
- PET-CT/MRI Department, Harbin Medical University Cancer Hospital, Harbin
| | - Shijia Liu
- PET-CT/MRI Department, Harbin Medical University Cancer Hospital, Harbin
| | - Wei Liu
- PET-CT/MRI Department, Harbin Medical University Cancer Hospital, Harbin
| | - Liang Cheng
- PET-CT/MRI Department, Harbin Medical University Cancer Hospital, Harbin
| | - Kezheng Wang
- PET-CT/MRI Department, Harbin Medical University Cancer Hospital, Harbin
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20
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Gong T, Gao Y, Li H, Wang J, Li Z, Yuan Q. Research progress in multimodal radiomics of rectal cancer tumors and peritumoral regions in MRI. Abdom Radiol (NY) 2025:10.1007/s00261-025-04965-1. [PMID: 40448847 DOI: 10.1007/s00261-025-04965-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2025] [Revised: 04/18/2025] [Accepted: 04/20/2025] [Indexed: 06/02/2025]
Abstract
Rectal cancer (RC) is one of the most common malignant tumors of the digestive system and has an alarmingly high incidence and mortality rate globally. Compared to conventional imaging examinations, radiomics can extract quantitative features that reflect tumor heterogeneity and mine data from medical images. In this review, we discuss the potential value of multimodal MRI-based radiomics in the diagnosis and treatment of RC, with a special emphasis on the role of peritumoral tissue characteristics in clinical decision-making. Existing studies have shown that a radiomics model integrating intratumoral and peritumoral characteristics has good application prospects in RC staging evaluation, efficacy prediction, metastasis monitoring, recurrence early warning, and prognosis judgment. At the same time, this paper also objectively analyzes the existing methodological limitations in this field, including insufficient data standardization, inadequate model validation, limited sample size and poor reproducibility of results. By combining existing evidence, this review aimed to enhance the attention of clinicians and radiologists on the characteristics of peritumoral tissues and promote the translational application of radiomics technology in the individualized treatment of RC.
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Affiliation(s)
- Tingting Gong
- The Second Affiliated Hospital of Jilin University, Jilin Province, China
| | - Ying Gao
- The Second Affiliated Hospital of Jilin University, Jilin Province, China
| | - He Li
- The Second Affiliated Hospital of Jilin University, Jilin Province, China
| | - Jianqiu Wang
- The Second Affiliated Hospital of Jilin University, Jilin Province, China
| | - Zili Li
- Jilin Province Cancer Hospital, Jilin Province, China.
| | - Qinghai Yuan
- The Second Affiliated Hospital of Jilin University, Jilin Province, China.
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21
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Pareto D, Naval-Baudin P, Pons-Escoda A, Bargalló N, Garcia-Gil M, Majós C, Rovira À. Image analysis research in neuroradiology: bridging clinical and technical domains. Neuroradiology 2025:10.1007/s00234-025-03633-x. [PMID: 40434412 DOI: 10.1007/s00234-025-03633-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Accepted: 04/20/2025] [Indexed: 05/29/2025]
Abstract
PURPOSE Advancements in magnetic resonance imaging (MRI) analysis over the past decades have significantly reshaped the field of neuroradiology. The ability to extract multiple quantitative measures from each MRI scan, alongside the development of extensive data repositories, has been fundamental to the emergence of advanced methodologies such as radiomics and artificial intelligence (AI). This educational review aims to delineate the importance of image analysis, highlight key paradigm shifts, examine their implications, and identify existing constraints that must be addressed to facilitate integration into clinical practice. Particular attention is given to aiding junior neuroradiologists in navigating this complex and evolving landscape. METHODS A comprehensive review of the available analysis toolboxes was conducted, focusing on major technological advancements in MRI analysis, the evolution of data repositories, and the rise of AI and radiomics in neuroradiology. Stakeholders within the field were identified and their roles examined. Additionally, current challenges and barriers to clinical implementation were analyzed. RESULTS The analysis revealed several pivotal shifts, including the transition from qualitative to quantitative imaging, the central role of large datasets in developing AI tools, and the growing importance of interdisciplinary collaboration. Key stakeholders-including academic institutions, industry partners, regulatory bodies, and clinical practitioners-were identified, each playing a distinct role in advancing the field. However, significant barriers remain, particularly regarding standardization, data sharing, regulatory approval, and integration into clinical workflows. CONCLUSIONS While advancements in MRI analysis offer tremendous potential to enhance neuroradiology practice, realizing this potential requires overcoming technical, regulatory, and practical barriers. Education and structured support for junior neuroradiologists are essential to ensure they are well-equipped to participate in and drive future developments. A coordinated effort among stakeholders is crucial to facilitate the seamless translation of these technological innovations into everyday clinical practice.
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Affiliation(s)
- Deborah Pareto
- Neuroradiology Section, Radiology Department (IDI), Vall Hebron University Hospital, Psg Vall Hebron 119-129, 08035, Barcelona, Spain.
- Neuroradiology Group, Vall Hebron Research Institute, Barcelona, Spain.
| | - Pablo Naval-Baudin
- Neuroradiology Section, Department of Radiology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Spain
- Institut Diagnòstic Per La Imatge (IDI), Centre Bellvitge, L'Hospitalet de Llobregat, Spain
- Diagnostic Imaging and Nuclear Medicine Research Group, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain
| | - Albert Pons-Escoda
- Neuroradiology Section, Department of Radiology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Spain
- Institut Diagnòstic Per La Imatge (IDI), Centre Bellvitge, L'Hospitalet de Llobregat, Spain
- Diagnostic Imaging and Nuclear Medicine Research Group, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain
| | - Núria Bargalló
- Neuroradiology Section, Radiology Department, Diagnostic Image Center, Hospital Clinic of Barcelona, University of Barcelona, Barcelona, Spain
| | - María Garcia-Gil
- Institut Diagnòstic Per La Imatge (IDI), Serveis Corporatius, Parc Sanitaria Pere Virgili, Barcelona, Spain
| | - Carlos Majós
- Neuroradiology Section, Department of Radiology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Spain
- Institut Diagnòstic Per La Imatge (IDI), Centre Bellvitge, L'Hospitalet de Llobregat, Spain
- Diagnostic Imaging and Nuclear Medicine Research Group, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain
| | - Àlex Rovira
- Neuroradiology Section, Radiology Department (IDI), Vall Hebron University Hospital, Psg Vall Hebron 119-129, 08035, Barcelona, Spain
- Neuroradiology Group, Vall Hebron Research Institute, Barcelona, Spain
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22
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Shi L, Wang X, Li C, Bai Y, Zhang Y, Li H. Radiomics applications in the modern management of esophageal squamous cell carcinoma. Med Oncol 2025; 42:221. [PMID: 40425893 DOI: 10.1007/s12032-025-02775-5] [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: 02/19/2025] [Accepted: 05/11/2025] [Indexed: 05/29/2025]
Abstract
Esophageal cancer ranks among the most lethal malignancies globally, with China accounting for more than half of worldwide esophageal squamous cell carcinoma (ESCC) cases. Late-stage diagnosis frequently precludes surgical intervention, contributing to poor outcomes. While precise clinical assessment is essential for treatment planning, therapeutic responses and prognosis exhibit substantial inter-patient heterogeneity, underscoring the urgent need for reliable biomarkers to enhance prognostic accuracy and guide personalized therapeutic strategies. Radiomics, an emerging field that extracts high-dimensional features from medical images, provides non-invasive approaches to improve diagnostic accuracy, predict survival, monitor adverse events, detect recurrence, and optimize treatment strategies. Radiomics has shown promising potential in the modern management of ESCC. Here, we review the critical contributions of radiomics to ESCC research and clinical practice, examining its workflow, applications, strengths, and limitations. Radiomics represents a compelling frontier with substantial potential to advance precision medicine for ESCC patients.
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Affiliation(s)
- Liqiang Shi
- Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China
| | - Xipeng Wang
- Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China
| | - Chengqiang Li
- Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China
| | - Yaya Bai
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yajie Zhang
- Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China.
| | - Hecheng Li
- Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China.
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Wang C, Wang C, Zhang J, Ding M, Ge Y, He X. Development and validation of a radiogenomics prognostic model integrating PET/CT radiomics and glucose metabolism-related gene signatures for non-small cell lung cancer. Eur J Nucl Med Mol Imaging 2025:10.1007/s00259-025-07354-4. [PMID: 40423774 DOI: 10.1007/s00259-025-07354-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2025] [Accepted: 05/13/2025] [Indexed: 05/28/2025]
Abstract
BACKGROUND Non-small cell lung cancer (NSCLC) is a highly heterogeneous malignancy characterized by altered glucose metabolism. Integration of PET/CT radiomics with glucose metabolism-related genomic signatures could provide a more comprehensive approach for prognosis and treatment guidance. METHODS Radiomics features were extracted from PET/CT images of 156 NSCLC patients from The Cancer Imaging Archive (TCIA) database, and glucose metabolism-related gene signatures were obtained from TCGA and GEO databases. We developed a multimodal radiogenomics prognostic model (RGC-score) using least absolute shrinkage and selection operator (LASSO) regression, combining PET/CT radiomics, glucose metabolism-related genes (GMR-genes). Functional enrichment analysis, immune infiltration assessment, and drug sensitivity analysis were performed to investigate the biological significance of glucose metabolism-related genes (GMR-genes). RESULTS The RGC-score model effectively stratified NSCLC patients into distinct high- and low-risk groups with significant differences in survival outcomes (P < 0.001), demonstrating excellent predictive performance (1-year AUC = 0.907, 5-year AUC = 0.968).GMR-genes are mainly involved in the process of metabolic remodeling of tumors, which is closely related to the immune microenvironment (especially CD8+ T cell infiltration) and immune checkpoint molecule expression. Additionally, significant differences in drug sensitivity were identified between glucose metabolism subtypes. CONCLUSION The RGC-score robustly predicts NSCLC prognosis and informs metabolic-immune interactions for personalized therapy. Limitations include the retrospective design and modest validation cohort size, necessitating prospective multicenter trials.
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Affiliation(s)
- Chunsheng Wang
- Department of Radiation Oncology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 210009, Jiangsu, China
- Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, 211116, Jiangsu, China
| | - Congjie Wang
- Department of Pulmonary and Critical Care Medicine, Yantai Yuhuangding Hospital, Yantai, 264000, Shandong, China
| | - Jianguo Zhang
- Department of Pulmonary Oncology, Hubei Key Laboratory of Tumor Biological Behavior, Hubei Cancer Clinical Study Center, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Mingjun Ding
- Department of Radiation Oncology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 210009, Jiangsu, China
- Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, 211116, Jiangsu, China
| | - Yizhi Ge
- Department of Radiation Oncology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 210009, Jiangsu, China.
- Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, 211116, Jiangsu, China.
| | - Xia He
- Department of Radiation Oncology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 210009, Jiangsu, China.
- Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, 211116, Jiangsu, China.
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Zhang Y, Zheng F. ASO Author Reflections: Clinical-Radiomic Machine Learning Model Predicts Pheochromocytomas and Paragangliomas Surgical Difficulty: A Retrospective Study. Ann Surg Oncol 2025:10.1245/s10434-025-17491-7. [PMID: 40419717 DOI: 10.1245/s10434-025-17491-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2025] [Accepted: 04/25/2025] [Indexed: 05/28/2025]
Abstract
This study developed a machine learning (ML) model combining clinical and radiomic features to predict surgical difficulty in pheochromocytomas and paragangliomas (PPGLs), aiming to optimize preoperative planning and reduce perioperative complications. Retrospective clinical and imaging data from PPGLs patients were analyzed to construct two sets of models: clinical parameter models and clinical-radiomic models. Seven ML algorithms were tested, with the SVM-based clinical-radiomic model achieving the highest performance (training area under the curve [AUC]: 0.96, validation AUC: 0.85), significantly surpassing the clinical parameter model. SHAP analysis highlighted radiomic signature (Rad-score) as the strongest predictor, followed by body mass index, age, tumor size, and preoperative heart rate. The model enables objective stratification of surgical difficulty, aiding tailored preoperative strategies. Future directions include integrating multi-omics data, refining surgical difficulty criteria through multicenter studies, developing real-time intraoperative predictive tools, and automating radiomic workflows via deep learning. This research advances personalized surgical management for PPGLs, demonstrating significant clinical translation potential.
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Affiliation(s)
- Yubing Zhang
- Department of Urology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Fufu Zheng
- Department of Urology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China.
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Gao Y, Liang F, Tian X, Zhang G, Zhang H. Preoperative risk assessment of invasive endometrial cancer using MRI-based radiomics: a systematic review and meta-analysis. Abdom Radiol (NY) 2025:10.1007/s00261-025-05005-8. [PMID: 40411548 DOI: 10.1007/s00261-025-05005-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2025] [Revised: 05/08/2025] [Accepted: 05/13/2025] [Indexed: 05/26/2025]
Abstract
OBJECTIVE Image-derived machine learning (ML) is a robust and growing field in diagnostic imaging systems for both clinicians and radiologists. Accurate preoperative radiological evaluation of the invasive ability of endometrial cancer (EC) can increase the degree of clinical benefit. The present study aimed to investigate the diagnostic performance of magnetic resonance imaging (MRI)-derived artificial intelligence for accurate preoperative assessment of the invasive risk. METHODS The PubMed, Embase, Cochrane Library and Web of Science databases were searched, and pertinent English-language papers were collected. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), and positive and negative likelihood ratios (PLR and NLR, respectively) of all the papers were calculated using Stata software. The results were plotted on a summary receiver operating characteristic (SROC) curve, publication bias and threshold effects were evaluated, and meta-regression and subgroup analyses were conducted to explore the possible causes of intratumoral heterogeneity. RESULTS MRI-based radiomics revealed pooled sensitivity (SEN) and specificity (SPE) values of 0.85 and 0.82 for the prediction of high-grade EC; 0.80 and 0.85 for deep myometrial invasion (DMI); 0.85 and 0.73 for lymphovascular space invasion (LVSI); 0.79 and 0.85 for microsatellite instability (MSI); and 0.90 and 0.72 for lymph node metastasis (LNM), respectively. For LVSI prediction and high-grade histological analysis, meta-regression revealed that the image segmentation and MRI-based radiomics modeling contributed to heterogeneity (p = 0.003 and 0.04). CONCLUSION Through a systematic review and meta-analysis of the reported literature, preoperative MRI-derived ML could help clinicians accurately evaluate EC risk factors, potentially guiding individual treatment thereafter.
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Affiliation(s)
- Yao Gao
- Department of Radiology, Obstetrics & Gynecology Hospital of Fudan University, Yangtze River Delta Integration Demonstration Zone (QingPu), Shanghai, China
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China
| | - Fan Liang
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China
| | - Xiaomei Tian
- Department of Radiology, Obstetrics & Gynecology Hospital of Fudan University, Yangtze River Delta Integration Demonstration Zone (QingPu), Shanghai, China
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China
| | - Guofu Zhang
- Department of Radiology, Obstetrics & Gynecology Hospital of Fudan University, Yangtze River Delta Integration Demonstration Zone (QingPu), Shanghai, China
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China
| | - He Zhang
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China.
- Department of Radiology, Obstetrics & Gynecology Hospital of Fudan University, Yangtze River Delta Integration Demonstration Zone (QingPu), Shanghai, China.
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Delgado-López PD, Cárdenas Montes M, Troya García J, Ocaña-Tienda B, Cepeda S, Martínez Martínez R, Corrales-García EM. Artificial intelligence in neuro-oncology: methodological bases, practical applications and ethical and regulatory issues. Clin Transl Oncol 2025:10.1007/s12094-025-03948-4. [PMID: 40402414 DOI: 10.1007/s12094-025-03948-4] [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: 03/12/2025] [Accepted: 04/26/2025] [Indexed: 05/23/2025]
Abstract
Artificial Intelligence (AI) is transforming neuro-oncology by enhancing diagnosis, treatment planning, and prognosis prediction. AI-driven approaches-such as CNNs and deep learning-have improved the detection and classification of brain tumors through advanced imaging techniques and genomic analysis. Explainable AI methods mitigate the "black box" problem, promoting model transparency and clinical trust. Mechanistic models complement AI by integrating biological principles, enabling precise tumor growth predictions and treatment response assessments. AI applications also include the creation of digital twins for personalized therapy optimization, virtual clinical trials, and predictive modeling for estimation of tumor resection and pattern of recurrence. However, challenges such as data bias, ethical concerns, and regulatory compliance persist. The European Artificial Intelligence Act and the Health Data Space Regulation impose strict data protection and transparency requirements. This review explores AI's methodological foundations, clinical applications, and ethical challenges in neuro-oncology, emphasizing the need for interdisciplinary collaboration and regulatory adaptation.
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Affiliation(s)
- Pedro David Delgado-López
- Servicio de Neurocirugía, Hospital Universitario de Burgos, Avda Islas Baleares 3, 09006, Burgos, Spain.
| | - Miguel Cárdenas Montes
- Departamento de Investigación Básica, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain
| | - Jesús Troya García
- Servicio de Medicina Interna, Hospital Universitario Infanta Leonor, Madrid, Spain
| | - Beatriz Ocaña-Tienda
- Centro Nacional de Investigaciones Oncológicas (CNIO), Unidad de Bioinformática, Madrid, Spain
| | - Santiago Cepeda
- Servicio de Neurocirugía, Hospital Universitario Rio Hortega, Valladolid, Spain
- Grupo Especializado en Imagen Biomédica y Análisis Computacional (GEIBAC), Instituto de Investigación Biosanitaria de Valladolid (IBioVall), Valladolid, Spain
| | - Ricard Martínez Martínez
- Facultad de Derecho, Cátedra de Privacidad y Transformación Digital de la Universidad de Valencia, Valencia, Spain
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Leng Y, Zhou J, Liu W, Luo F, Peng F, Gong L. A CT-based radiomics model for predicting progression-free survival in patients with epithelial ovarian cancer. BMC Cancer 2025; 25:899. [PMID: 40394512 PMCID: PMC12090431 DOI: 10.1186/s12885-025-14265-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2025] [Accepted: 05/05/2025] [Indexed: 05/22/2025] Open
Abstract
PURPOSE This study aimed to develop and validate a CT-based radiomics nomogram for predicting the progression-free survival (PFS) of epithelial ovarian cancer (EOC). MATERIALS AND METHODS A total of 144 EOC patients were retrospectively enrolled from two hospitals and The Cancer Genome Atlas and The Cancer Imaging Archive, divided into a training set (n = 101) and a test set (n = 43) using a 7:3 ratio. Radiomic features were extracted from contrast enhanced CT images. The radiomics score (rad-score) was developed using the least absolute shrinkage and selection operator (LASSO) Cox regression. Clinical semantic features with P < 0.05 in multivariate Cox regression were combined with rad-score to develop radiomics nomogram. The predictive performance of the nomogram was assessed using the concordance index (C-index) and calibration curves. RESULTS Multivariate Cox regression analysis revealed that the International Federation of Obstetrics and Gynecology stage and residual tumor are significant predictors of PFS. Twelve radiomic features were selected by LASSO Cox regression. The combined model demonstrated superior predictive performance, with a C-index of 0.78 (95% CI: 0.689-0.889) in the training set, and 0.73 (95% CI: 0.572-0.886) in the test set. The combined model outperformed the clinical and radiomics models in predicting 1-, 3-, and 5-year PFS, with area under curves of 0.850 (95% CI: 0.722-0.943), 0.828 (95% CI: 0.722-0.901), and 0.845 (95% CI: 0.722-0.943), respectively. Calibration curves of the radiomic nomogram for prediction of 1-year, 3-year, 5-year PFS showed excellent calibrations in both training and test sets. CONCLUSION The combined model integrating rad-score and clinical semantic features effectively evaluates PFS in EOC patients. The radiomics nomogram provides a non-invasive, simple, and feasible method to predict PFS in EOC patients, which may facilitate clinical decision-making.
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Affiliation(s)
- Yinping Leng
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Minde Road No. 1, Nanchang, Jiangxi Province, 330006, China
- Jiangxi Provincial Key Laboratory of Intelligent Medical Imaging, Nanchang, China
| | - Jingjing Zhou
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Minde Road No. 1, Nanchang, Jiangxi Province, 330006, China
- Jiangxi Provincial Key Laboratory of Intelligent Medical Imaging, Nanchang, China
| | - Wenjie Liu
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Minde Road No. 1, Nanchang, Jiangxi Province, 330006, China
- Jiangxi Provincial Key Laboratory of Intelligent Medical Imaging, Nanchang, China
| | - Fengyuan Luo
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Minde Road No. 1, Nanchang, Jiangxi Province, 330006, China
- Jiangxi Provincial Key Laboratory of Intelligent Medical Imaging, Nanchang, China
| | - Fei Peng
- Department of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Lianggeng Gong
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Minde Road No. 1, Nanchang, Jiangxi Province, 330006, China.
- Jiangxi Provincial Key Laboratory of Intelligent Medical Imaging, Nanchang, China.
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Xiao S, Zeng S, Kou Y. MRI radiomics in diagnosing high and low grade meningiomas through systematic review and meta analysis. Sci Rep 2025; 15:17521. [PMID: 40394344 PMCID: PMC12092648 DOI: 10.1038/s41598-025-88315-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 01/28/2025] [Indexed: 05/22/2025] Open
Abstract
To evaluate the diagnostic value of magnetic resonance imaging (MRI) radiomics in distinguishing high-grade meningiomas (HGM) from low-grade meningiomas (LGM). A systematic search was conducted in PubMed, EMbase, Web of Science, and The Cochrane Library databases up to December 31, 2023. Two researchers independently screened studies, extracted data, and assessed risk of bias and quality of included studies as well. Meta-analysis was performed using Stata 14 software to calculate pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC). A total of 21 studies with 2253 patients were included (607 HGM, 1646 LGM). Meta-analysis showed an overall SEN of 0.82 (95% CI 0.74-0.88) and SPE of 0.85 (95% CI 0.81-0.89). The PLR and NLR were 5.64 (95% CI 4.17-7.64) and 0.21 (95% CI 0.14-0.31), respectively, with a pooled DOR of 26.66 (95% CI 14.42-49.27) and an AUC of 0.91 (95% CI 0.88-0.93), indicating high diagnostic accuracy. Although additional research is required to validate suitable techniques, MRI radiomics shows strong potential as an accurate tool for meningioma grading. Standardizing radiomics application could enhance diagnostic precision and clinical decision-making for meningioma grading in the future.Trial Registration: CRD42024500086.
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Affiliation(s)
- Simin Xiao
- Radiology Department, The First Affiliated Hospital of Traditional Chinese Medicine of Chengdu Medical College, XinDu Hospital of Traditional Chinese Medicine, Chengdu, China
| | - Siyuan Zeng
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Yangbin Kou
- Radiology Department, The First Affiliated Hospital of Traditional Chinese Medicine of Chengdu Medical College, XinDu Hospital of Traditional Chinese Medicine, Chengdu, China.
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Troise S, Ugga L, Esposito M, Positano M, Elefante A, Capasso S, Cuocolo R, Merola R, Committeri U, Abbate V, Bonavolontà P, Nocini R, Dell'Aversana Orabona G. The Role of Machine Learning to Detect Occult Neck Lymph Node Metastases in Early-Stage (T1-T2/N0) Oral Cavity Carcinomas. Head Neck 2025. [PMID: 40390252 DOI: 10.1002/hed.28189] [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: 11/29/2024] [Revised: 04/04/2025] [Accepted: 05/05/2025] [Indexed: 05/21/2025] Open
Abstract
OBJECTIVE Oral cavity carcinomas (OCCs) represent roughly 50% of all head and neck cancers. The risk of occult neck metastases for early-stage OCCs ranges from 15% to 35%, hence the need to develop tools that can support the diagnosis of detecting these neck metastases. Machine learning and radiomic features are emerging as effective tools in this field. Thus, the aim of this study is to demonstrate the effectiveness of radiomic features to predict the risk of occult neck metastases in early-stage (T1-T2/N0) OCCs. STUDY DESIGN Retrospective study. SETTING A single-institution analysis (Maxillo-facial Surgery Unit, University of Naples Federico II). METHODS A retrospective analysis was conducted on 75 patients surgically treated for early-stage OCC. For all patients, data regarding TNM, in particular pN status after the histopathological examination, have been obtained and the analysis of radiomic features from MRI has been extrapolated. RESULTS 56 patients confirmed N0 status after surgery, while 19 resulted in pN+. The radiomic features, extracted by a machine-learning algorithm, exhibited the ability to preoperatively discriminate occult neck metastases with a sensitivity of 78%, specificity of 83%, an AUC of 86%, accuracy of 80%, and a positive predictive value (PPV) of 63%. CONCLUSIONS Our results seem to confirm that radiomic features, extracted by machine learning methods, are effective tools in detecting occult neck metastases in early-stage OCCs. The clinical relevance of this study is that radiomics could be used routinely as a preoperative tool to support diagnosis and to help surgeons in the surgical decision-making process, particularly regarding surgical indications for neck lymph node treatment.
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Affiliation(s)
- Stefania Troise
- Maxillofacial Surgery Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Maria Esposito
- Maxillofacial Surgery Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy
| | - Maria Positano
- Maxillofacial Surgery Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy
| | - Andrea Elefante
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Serena Capasso
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Raffaele Merola
- Anesthesia and Intensive Care Medicine, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy
| | | | - Vincenzo Abbate
- Maxillofacial Surgery Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy
| | - Paola Bonavolontà
- Maxillofacial Surgery Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy
| | - Riccardo Nocini
- Otolaryngology-Head and Neck Surgery Department, University and Hospital Trust of Verona, Verona, Italy
| | - Giovanni Dell'Aversana Orabona
- Maxillofacial Surgery Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy
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Li J, Mao N, Aodeng S, Zhang H, Zhu Z, Wang L, Liu Y, Qi H, Qiao H, Lin Y, Qiu Z, Yang T, Zha Y, Wang X, Wang W, Song X, Lv W. Development and Validation an Integrated Deep Learning Model to Assist Eosinophilic Chronic Rhinosinusitis Diagnosis: A Multicenter Study. Int Forum Allergy Rhinol 2025:e23595. [PMID: 40387008 DOI: 10.1002/alr.23595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2024] [Revised: 03/12/2025] [Accepted: 04/03/2025] [Indexed: 05/20/2025]
Abstract
BACKGROUND The assessment of eosinophilic chronic rhinosinusitis (eCRS) lacks accurate non-invasive preoperative prediction methods, relying primarily on invasive histopathological sections. This study aims to use computed tomography (CT) images and clinical parameters to develop an integrated deep learning model for the preoperative identification of eCRS and further explore the biological basis of its predictions. METHODS A total of 1098 patients with sinus CT images were included from two hospitals and were divided into training, internal, and external test sets. The region of interest of sinus lesions was manually outlined by an experienced radiologist. We utilized three deep learning models (3D-ResNet, 3D-Xception, and HR-Net) to extract features from CT images and calculate deep learning scores. The clinical signature and deep learning score were inputted into a support vector machine for classification. The receiver operating characteristic curve, sensitivity, specificity, and accuracy were used to evaluate the integrated deep learning model. Additionally, proteomic analysis was performed on 34 patients to explore the biological basis of the model's predictions. RESULTS The area under the curve of the integrated deep learning model to predict eCRS was 0.851 (95% confidence interval [CI]: 0.77-0.93) and 0.821 (95% CI: 0.78-0.86) in the internal and external test sets. Proteomic analysis revealed that in patients predicted to be eCRS, 594 genes were dysregulated, and some of them were associated with pathways and biological processes such as chemokine signaling pathway. CONCLUSIONS The proposed integrated deep learning model could effectively predict eCRS patients. This study provided a non-invasive way of identifying eCRS to facilitate personalized therapy, which will pave the way toward precision medicine for CRS.
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Affiliation(s)
- Jingjing Li
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Ning Mao
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Surita Aodeng
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Haicheng Zhang
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Zhenzhen Zhu
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Lei Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Yuzhuo Liu
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Hang Qi
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Hong Qiao
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Yuxi Lin
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Zijun Qiu
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Tengyu Yang
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Yang Zha
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xiaowei Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Weiqing Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xicheng Song
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Wei Lv
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
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Chen T, Liu F. Neoadjuvant immunotherapy in early-stage NSCLC: navigating biomarker dilemmas and special population challenges. Lung Cancer 2025; 204:108588. [PMID: 40409027 DOI: 10.1016/j.lungcan.2025.108588] [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: 05/13/2025] [Accepted: 05/17/2025] [Indexed: 05/25/2025]
Abstract
Neoadjuvant immunotherapy has shown impressive outcomes in treating non-small cell lung cancer (NSCLC) recently due to advancements in immune checkpoint inhibitors (ICIs) research. Neoadjuvant immunotherapy can lower the tumor load, raise the complete surgical (R0) resection rate, and improve clinical outcomes by alleviating the immune system repression caused by tumor cells. This review provides a comprehensive evaluation of neoadjuvant immunotherapy in NSCLC, focusing on: (1) its safety and efficacy profiles, (2) the most recent clinical trial evidence, and (3) critical unresolved challenges including predictive biomarker development, management of driver mutation-positive patients, chronic obstructive pulmonary disease (COPD) comorbidity considerations, and its application in stage III-IVA (oligometastatic) disease. Furthermore, we explore future research directions to optimize neoadjuvant immunotherapy approaches for resectable NSCLC, aiming to guide clinical practice and investigation.
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Affiliation(s)
- Tong Chen
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Fang Liu
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China.
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Liu F, Chen L, Wu Q, Li L, Li J, Su T, Li J, Liang S, Qing L. Radiomics of Dynamic Contrast-Enhanced MRI for Predicting Radiation-Induced Hepatic Toxicity After Intensity Modulated Radiotherapy for Hepatocellular Carcinoma: A Machine Learning Predictive Model Based on the SHAP Methodology. J Hepatocell Carcinoma 2025; 12:999-1015. [PMID: 40406666 PMCID: PMC12095435 DOI: 10.2147/jhc.s523448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2025] [Accepted: 05/03/2025] [Indexed: 05/26/2025] Open
Abstract
Objective To develop an interpretable machine learning (ML) model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic data, dosimetric parameters, and clinical data for predicting radiation-induced hepatic toxicity (RIHT) in patients with hepatocellular carcinoma (HCC) following intensity-modulated radiation therapy (IMRT). Methods A retrospective analysis of 150 HCC patients was performed, with a 7:3 ratio used to divide the data into training and validation cohorts. Radiomic features from the original MRI sequences and Delta-radiomic features were extracted. Seven ML models based on radiomics were developed: logistic regression (LR), random forest (RF), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), adaptive boosting (AdaBoost), decision tree (DT), and artificial neural network (ANN). The predictive performance of the models was evaluated using receiver operating characteristic (ROC) curve analysis and calibration curves. Shapley additive explanations (SHAP) were employed to interpret the contribution of each variable and its risk threshold. Results Original radiomic features and Delta-radiomic features were extracted from DCE-MRI images and filtered to generate Radiomics-scores and Delta-Radiomics-scores. These were then combined with independent risk factors (Body Mass Index (BMI), V5, and pre-Child-Pugh score(pre-CP)) identified through univariate and multivariate logistic regression and Spearman correlation analysis to construct the ML models. In the training cohort, the AUC values were 0.8651 for LR, 0.7004 for RF, 0.6349 for SVM, 0.6706 for XGBoost, 0.7341 for AdaBoost, 0.6806 for Decision Tree, and 0.6786 for ANN. The corresponding accuracies were 84.4%, 65.6%, 75.0%, 65.6%, 71.9%, 68.8%, and 71.9%, respectively. The validation cohort further confirmed the superiority of the LR model, which was selected as the optimal model. SHAP analysis revealed that Delta-radiomics made a substantial positive contribution to the model. Conclusion The interpretable ML model based on radiomics provides a non-invasive tool for predicting RIHT in patients with HCC, demonstrating satisfactory discriminative performance.
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Affiliation(s)
- Fushuang Liu
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530001, People’s Republic of China
| | - Lijun Chen
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530001, People’s Republic of China
| | - Qiaoyuan Wu
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530001, People’s Republic of China
| | - Liqing Li
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530001, People’s Republic of China
| | - Jizhou Li
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530001, People’s Republic of China
| | - Tingshi Su
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530001, People’s Republic of China
| | - Jianxu Li
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530001, People’s Republic of China
| | - Shixiong Liang
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530001, People’s Republic of China
| | - Liping Qing
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530001, People’s Republic of China
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Fish JE, Eleti S, Power N, Nandra G. Imaging of young-onset colorectal cancer: what the radiologist needs to know. Abdom Radiol (NY) 2025:10.1007/s00261-025-04976-y. [PMID: 40382481 DOI: 10.1007/s00261-025-04976-y] [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: 03/07/2025] [Revised: 04/19/2025] [Accepted: 04/27/2025] [Indexed: 05/20/2025]
Abstract
Young-onset colorectal cancer (YOCRC) refers to colorectal cancer diagnosed in individuals under the age of 50. Whilst the overall incidence of colorectal cancer is decreasing, YOCRC cases are increasing and now accounts for up to 10% of all colorectal cancers. YOCRC more frequently presents with acute symptoms, where radiologists play an important role in identifying malignancy and distinguishing it from benign colonic pathologies. Risk factors associated with YOCRC, such as inflammatory bowel disease and hereditary syndromes, may exhibit specific imaging manifestations. In addition, YOCRC is frequently associated with a mucinous histopathological subtype which may be identifiable based on the presence of specific imaging features. Given their younger age, these patients are more likely to undergo aggressive treatment and complex surgical interventions. Specific considerations such as fertility preserving surgical techniques must be factored in when managing these patients. As the incidence of YOCRC increases, guidance for colonoscopy screening protocols may need revision. This includes evaluating the role of ionising imaging techniques in both diagnosing and follow-up to balance early detection and minimising radiation exposure in this younger patient population.
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Affiliation(s)
| | - Saigeet Eleti
- Imaging Department, Royal London Hospital, London, UK
| | - Niall Power
- Imaging Department, Royal London Hospital, London, UK
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Broomand Lomer N, Ghasemi A, Ahmadzadeh AM, A Torigian D. MRI-based radiomics for differentiating high-grade from low-grade clear cell renal cell carcinoma: a systematic review and meta-analysis. Abdom Radiol (NY) 2025:10.1007/s00261-025-04982-0. [PMID: 40382483 DOI: 10.1007/s00261-025-04982-0] [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: 02/20/2025] [Revised: 04/15/2025] [Accepted: 04/30/2025] [Indexed: 05/20/2025]
Abstract
PURPOSE High-grade clear cell renal cell carcinoma (ccRCC) is linked to lower survival rates and more aggressive disease progression. This study aims to assess the diagnostic performance of MRI-derived radiomics as a non-invasive approach for pre-operative differentiation of high-grade from low-grade ccRCC. METHODS A systematic search was conducted across PubMed, Scopus, and Embase. 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 estimated using a bivariate model. Separate meta-analyses were conducted for radiomics models and combined models, where the latter integrated clinical and radiological features with radiomics. Subgroup analysis was performed to identify potential sources of heterogeneity. Sensitivity analysis was conducted to identify potential outliers. RESULTS A total of 15 studies comprising 2,265 patients were included, with seven and six studies contributing to the meta-analysis of radiomics and combined models, respectively. The pooled estimates of the radiomics model were as follows: sensitivity, 0.78; specificity, 0.84; PLR, 4.17; NLR, 0.28; DOR, 17.34; and AUC, 0.84. For the combined model, the pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.87, 0.81, 3.78, 0.21, 28.57, and 0.90, respectively. Radiomics models trained on smaller cohorts exhibited a significantly higher pooled specificity and PLR than those trained on larger cohorts. Also, radiomics models based on single-user segmentation demonstrated a significantly higher pooled specificity compared to multi-user segmentation. CONCLUSION Radiomics has demonstrated potential as a non-invasive tool for grading ccRCC, with combined models achieving superior performance.
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Affiliation(s)
- Nima Broomand Lomer
- Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, United States
| | - Amirhosein Ghasemi
- Faculty of Medicine, Guilan University of Medical Sciences, Rasht, Islamic Republic of Iran
| | - Amir Mahmoud Ahmadzadeh
- Department of Radiology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Islamic Republic of Iran
| | - Drew A Torigian
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, United States.
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Lu D, Zhou L, Zuo Z, Zhang Z, Zheng X, Weng J, Yu Z, Ji J, Xia J. MRI Radiomics to Predict Early Treatment Response to TACE Combined with Lenvatinib Plus a PD-1 Inhibitor for Hepatocellular Carcinoma with Portal Vein Tumor Thrombus. J Hepatocell Carcinoma 2025; 12:985-998. [PMID: 40406667 PMCID: PMC12094907 DOI: 10.2147/jhc.s513696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2025] [Accepted: 05/08/2025] [Indexed: 05/26/2025] Open
Abstract
Purpose To develop and validate a predictor for early treatment response in hepatocellular carcinoma (HCC) patients accompanied by portal vein tumor thrombus (PVTT) undergoing transarterial chemoembolization (TACE), lenvatinib and a programmed cell death protein 1 (PD-1) inhibitor (TLP) therapy. Patients and Methods In this retrospective study, patients with HCC and PVTT from two institutions receiving triple TLP therapy were enrolled. Radiomics features derived from pretreatment contrast-enhanced MRI were curated using intraclass correlation coefficient (ICC), Student's t-test, least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE) to ensure robust selection. Various machine learning (ML) algorithms were then used to construct the models. The meaningful clinical indicators were obtained via logistic regression analysis and ultimately integrated with radiomics features to develop a combined model. In addition, we used Shapley Additive exPlanation (SHAP) to clarify the model's operational dynamics. Results Our study ultimately included 115 patients (7:3 randomization, 80 and 35 in the training and test cohorts, respectively) in total. No patients achieved complete remission, 47 achieved partial remission, 29 achieved stable disease, and 39 experienced disease progression. Among objective response rates (ORRs) and disease control rates (DCRs), 40.9% and 66.1% were reported. One of the four ML classifiers with optimal performance, namely random forest, was adopted as the radiomics model after testing. Regarding the performance assessment, the radiomics model's area under the curve (AUC) values reached 0.92 (95% CI: 0.86-0.97) and 0.79 (95% CI: 0.61-0.95), inferior to the combined model's AUCs of 0.95 (95% CI: 0.68-0.98) and 0.84 (95% CI: 0.91-0.99). Moreover, the SHAP plots illustrate the importance of global variables and the prediction process for individual samples. Conclusion The model based on machine learning and radiomics showed favorable performance, and the operating mode was visualized through SHAP.
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Affiliation(s)
- Deyu Lu
- Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035, People’s Republic of China
| | - Lingling Zhou
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, People’s Republic of China
| | - Ziyi Zuo
- Division of Pulmonary Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou Key Laboratory of Interdiscipline and Translational Medicine, Wenzhou Key Laboratory of Heart and Lung, Wenzhou, Zhejiang, 325000, People’s Republic of China
| | - Zhao Zhang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China
| | - Xiangwu Zheng
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China
| | - Jialu Weng
- Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035, People’s Republic of China
| | - Zhijie Yu
- Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035, People’s Republic of China
| | - Jiansong Ji
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, People’s Republic of China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, People’s Republic of China
| | - Jinglin Xia
- Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035, People’s Republic of China
- Liver Cancer Institute, Zhongshan Hospital of Fudan University, Shanghai, 200032, People’s Republic of China
- National Clinical Research Center for Interventional Medicine, Zhongshan Hospital of Fudan University, Shanghai, 200032, People’s Republic of China
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Yin P, Xu J, Liu Y, Wang S, Liu T, Tang X, Hong N. T2-weighted magnetic resonance imaging radiogenomic features for the prediction of neoadjuvant chemotherapy response in patients with osteosarcoma. Acta Radiol 2025:2841851251337849. [PMID: 40375792 DOI: 10.1177/02841851251337849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2025]
Abstract
BackgroundOsteosarcoma (OS) is the most common primary malignant bone tumor. Exploring quantitative parameters that reflect the outcome of neoadjuvant chemotherapy (NACT) in patients with OS can help advance the treatment of patients.PurposeTo explore the role of T2-weighted (T2W) magnetic resonance imaging (MRI) radiogenomic features in characterizing changes in patients with OS and on NACT.Material and MethodsA total of 21 patients with OS were examined retrospectively and divided into a poor-response group (n = 13) and a good-response group (n = 8). A total of 98 radiomic features and 31 gene expression profiles were analyzed for each patient. Age, sex, alkaline phosphatase, pathologic type, tumor size, and tumor location were also analyzed. Comparisons between the good- and poor-response groups were made using the t-test, Mann-Whitney U test, or Fisher's exact test. The relationships between radiomic features and gene expression profiles were conducted using Spearman's correlative analyses.ResultsStatistical differences in 19 radiomics features and glutathione-s-transferase 1 were found between the good- and poor-response groups (P < 0.05). The receiver operating characteristic curve showed that four NGTDM busyness features had the best performance in predicting the NACT of patients with OS, with an area under the curve of 0.788, sensitivity of 0.750, and specificity of 0.923. Correlation analysis showed that the HLA_I, CD274, GSTP1, and CCND3 were significantly correlated with one or more radiomics features (P < 0.05).ConclusionThe T2W MRI radiogenomic features can be used as biomarkers for the early response evaluation of NACT in OS. This is the first study to analyze the association of T2 radiogenomic features with NACT in patients with OS to assist in the assessment of NACT.
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Affiliation(s)
- Ping Yin
- Department of Radiology, Peking University People's Hospital, Beijing, PR China
| | - Jie Xu
- Musculoskeletal Tumor Center, Peking University People's Hospital, Beijing, PR China
| | - Ying Liu
- Department of Radiology, Peking University People's Hospital, Beijing, PR China
| | - Sicong Wang
- MR Research China, GE Healthcare, Beijing, PR China
| | - Tao Liu
- Department of Radiology, Peking University People's Hospital, Beijing, PR China
| | - Xiaodong Tang
- Musculoskeletal Tumor Center, Peking University People's Hospital, Beijing, PR China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, Beijing, PR China
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Zhou C, Zhang YF, Yang ZJ, Huang YQ, Da MX. Computed tomography-based deep learning radiomics model for preoperative prediction of tumor immune microenvironment in colorectal cancer. World J Gastrointest Oncol 2025; 17:106103. [DOI: 10.4251/wjgo.v17.i5.106103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2025] [Revised: 03/08/2025] [Accepted: 03/31/2025] [Indexed: 05/15/2025] Open
Abstract
BACKGROUND Colorectal cancer (CRC) is a leading cause of cancer-related death globally, with the tumor immune microenvironment (TIME) influencing prognosis and immunotherapy response. Current TIME evaluation relies on invasive biopsies, limiting its clinical application. This study hypothesized that computed tomography (CT)-based deep learning (DL) radiomics models can non-invasively predict key TIME biomarkers: Tumor-stroma ratio (TSR), tumor-infiltrating lymphocytes (TILs), and immune score (IS).
AIM To develop a non-invasive DL approach using preoperative CT radiomics to evaluate TIME components in CRC patients.
METHODS In this retrospective study, preoperative CT images of 315 pathologically confirmed CRC patients (220 in training cohort and 95 in validation cohort) were analyzed. Manually delineated regions of interest were used to extract DL features. Predictive models (DenseNet-121/169) for TSR, TILs, IS, and TIME classification were constructed. Performance was evaluated via receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA).
RESULTS The DL-DenseNet-169 model achieved area under the curve (AUC) values of 0.892 [95% confidence interval (CI): 0.828-0.957] for TSR and 0.772 (95%CI: 0.674-0.870) for TIME score. The DenseNet-121 model yielded AUC values of 0.851 (95%CI: 0.768-0.933) for TILs and 0.852 (95%CI: 0.775-0.928) for IS. Calibration curves demonstrated strong prediction-observation agreement, and DCA confirmed clinical utility across threshold probabilities (P < 0.05 for all models).
CONCLUSION CT-based DL radiomics provides a reliable non-invasive method for preoperative TIME evaluation, enabling personalized immunotherapy strategies in CRC management.
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Affiliation(s)
- Chuan Zhou
- The First Clinical Medical College of Lanzhou University, Lanzhou University, Lanzhou 730000, Gansu Province, China
- NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou 730000, Gansu Province, China
- Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, Gansu Provincial Hospital, Lanzhou 730000, Gansu Province, China
| | - Yun-Feng Zhang
- The First Clinical Medical College of Lanzhou University, Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Zhi-Jun Yang
- The First Clinical Medical College of Lanzhou University, Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Yu-Qian Huang
- Center of Medical Cosmetology, Chengdu Second People’s Hospital, Chengdu 610017, Sichuan Province, China
| | - Ming-Xu Da
- The First Clinical Medical College of Lanzhou University, Lanzhou University, Lanzhou 730000, Gansu Province, China
- NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou 730000, Gansu Province, China
- Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, Gansu Provincial Hospital, Lanzhou 730000, Gansu Province, China
- Department of Surgical Oncology, Gansu Provincial Hospital, Lanzhou 730000, Gansu Province, China
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Wang M, Chen W, Ren R, Lin Y, Tang J, Wu M. Comparative analysis of multi-zone peritumoral radiomics in breast cancer for predicting NAC response using ABVS-based deep learning models. Front Oncol 2025; 15:1586715. [PMID: 40438687 PMCID: PMC12116539 DOI: 10.3389/fonc.2025.1586715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2025] [Accepted: 04/28/2025] [Indexed: 06/01/2025] Open
Abstract
Background Peritumoral characteristics demonstrate significant predictive value for neoadjuvant chemotherapy (NAC) response in breast cancer (BC) through tumor-stromal interactions. Radiomics analysis of peritumoral regions has shown robust capability in predicting treatment outcomes; however, the optimal peritumoral thickness for maximizing predictive accuracy remains undefined. Objective To establish a clinically implementable framework for early identification of NAC non-responders through standardized prediction modeling. This study aims to determine the optimal peritumoral thickness for NAC response prediction by training and systematically comparing artificial intelligence (AI)-driven radiomics models across multiple peritumoral zones using Automated Breast Volume Scanning (ABVS). Methods A total of 402 BC patients who received NAC were retrospectively analyzed. Pre-treatment ABVS images were processed to extract radiomic features from five regions of interest (ROIs): the intratumoral region (R0) and four consecutive peritumoral zones (R2-R8) extending outward at 2-mm intervals. The study cohort was divided into training and testing cohorts. ROI-specific TabNet models were developed using the training cohort data. Comparative analysis was performed in the testing cohort through comprehensive performance evaluation, including discrimination, calibration, clinical utility assessment, and classification metrics, to identify the optimal peritumoral zone. The radiomics features of the best-performing model were ranked by importance, with subsequent ablation studies validating the predictive contribution of high-ranking features. Results Among the study population, 138 patients (34.3%) were classified as NAC non-responders. Model evaluation demonstrated progressively improved predictive performance from R0 to R6, with area under the ROC curves increasing from 0.681 to 0.845. The R6 model demonstrated optimal performance with accuracy of 0.810 and precision of 0.765. The combined model integrating R0 and R6 features enhanced predictive capability, achieving accuracy of 0.909, precision of 0.841, and recall of 0.902. Feature importance analysis identified textural heterogeneity and volumetric characteristics as the most influential variables, with the top features derived predominantly from the 6-mm peritumoral region. Conclusion The 6-mm peritumoral zone demonstrated optimal predictive value for NAC response, with the AI-driven combined intratumoral-peritumoral model achieving superior performance. This standardized ABVS-based radiomics approach enables early identification of potential NAC non-responders, facilitating timely therapeutic modifications.
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Affiliation(s)
- Minfang Wang
- Department of Ultrasound, The Affiliated People’s Hospital of Ningbo University, Ningbo, Zhejiang, China
| | - Wanjun Chen
- Department of Ultrasound, The Affiliated People’s Hospital of Ningbo University, Ningbo, Zhejiang, China
| | - Ruiping Ren
- Department of Radiotherapy and Chemotherapy, The Affiliated People’s Hospital of Ningbo University, Ningbo, Zhejiang, China
| | - Yuanwei Lin
- Department of Radiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China
| | - Jiawen Tang
- Department of Pathology, The Affiliated People’s Hospital of Ningbo University, Ningbo, Zhejiang, China
| | - Meng Wu
- Department of Ultrasound, The Affiliated People’s Hospital of Ningbo University, Ningbo, Zhejiang, China
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Zhang J, Li Q, Liang H, Wang Y, Sun L, Zhang Q, Gao C. Preoperative prediction of lymph node metastasis in patients with ovarian cancer using contrast-enhanced computed tomography-based intratumoral and peritumoral radiomics features. Front Oncol 2025; 15:1543873. [PMID: 40438691 PMCID: PMC12116341 DOI: 10.3389/fonc.2025.1543873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2024] [Accepted: 04/23/2025] [Indexed: 06/01/2025] Open
Abstract
Purpose To develop and validate computed tomography (CT)-based intratumoral and peritumoral radiomics signatures for preoperative prediction of lymph node metastasis (LNM) in patients with ovarian cancer (OC). Methods Patients with pathological diagnosis of OC were retrospectively included. Intratumoral and peritumoral radiomics features were extracted from contrast-enhanced CT images. Intratumoral and peritumoral radiomics features were extracted from contrast-enhanced CT images. Intratumoral, peritumoral, and combined radiomics signatures were constructed, and their radiomics scores were calculated. Univariate and multivariate logistic regression analyses were performed to identify predictors of clinical outcomes. A radiomics nomogram was developed by incorporating the combined radiomics signature with clinical risk factors. The prediction efficiency of the various models was evaluated using the accuracy value, the area under the receiver-operating characteristic curve (AUC) and decision curve analysis (DCA). Results Two hundred and seventy-three patients with OC were enrolled and randomly divided into a training cohort (n=190) and a test cohort (n=83) in a 7:3 ratio. The intratumoral, peritumoral, and combined radiomics signatures were constructed using 18, 11, and 17 radiomics features, respectively. The combined radiomics signature showed the best prediction ability, with accuracy of 0.783 and an AUC of 0.860 (95% confidence interval 0.779-0.941). The DCA results showed that the combined radiomics signature had better clinical application than the clinical model and the radiomics nomogram. Conclusions A CT-based combined radiomics signature incorporating intratumoral and peritumoral radiomics features can predict LNM in patients with OC before surgery.
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Affiliation(s)
- Jing Zhang
- Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Qiyuan Li
- Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Haoyu Liang
- Huashan Hospital, Fudan University, Shanghai, China
| | - Yao Wang
- Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Li Sun
- Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Qingyuan Zhang
- Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Chuanping Gao
- Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, China
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Zhang P, Wei L, Nie Z, Hu P, Zheng J, Lv J, Cui T, Liu C, Lan X. Research on the developments of artificial intelligence in radiomics for oncology over the past decade: a bibliometric and visualized analysis. Discov Oncol 2025; 16:763. [PMID: 40366503 PMCID: PMC12078899 DOI: 10.1007/s12672-025-02590-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2025] [Accepted: 05/06/2025] [Indexed: 05/15/2025] Open
Abstract
OBJECTIVE To assess the publications' bibliographic features and look into how the advancement of artificial intelligence (AI) and its subfields in radiomics has affected the growth of oncology. METHODS The researchers conducted a search in the Web of Science (WoS) for scientific publications in cancer pertaining to AI and radiomics, published in English from 1 January 2015 to 31 December 2024.The research included a scientometric methodology and comprehensive data analysis utilising scientific visualization tools, including the Bibliometrix R software package, VOSviewer, and CiteSpace. Bibliometric techniques utilised were co-authorship, co-citation, co-occurrence, citation burst, and performance Analysis. RESULTS The final study encompassed 4,127 publications authored by 5,026 individuals and published across 597 journals. China (2087;50.57%) and USA (850;20.6%) were the two most productive countries. The authors with the highest publication counts were Tian Jie (60) and Cuocolo Renato (30). Fudan University (169;4.09%) and Sun Yat-sen University (162;3.93%) were the most active institutions. The foremost journals were Frontiers in Oncology and Cancer. The predominant author keywords were radiomics, artificial intelligence, and oncology research. CONCLUSION Investigations into the integration of AI with radiomics in oncology remain nascent, with numerous studies concentrating on biology, diagnosis, treatment, and cancer risk evaluation.
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Affiliation(s)
- Pengyu Zhang
- Department of Urology, Qingdao Central Hospital, University of Health and Rehabilitation Sciences, Qingdao, 266042, People's Republic of China
- School of Qingdao Medical College, Qingdao University, 308 Ningxia Road, Qingdao, 266071, China
| | - Lili Wei
- Department of Urology, Qingdao Central Hospital, University of Health and Rehabilitation Sciences, Qingdao, 266042, People's Republic of China
| | - Zonglong Nie
- Department of Urology, Qingdao Central Hospital, University of Health and Rehabilitation Sciences, Qingdao, 266042, People's Republic of China
| | - Pengcheng Hu
- Department of Urology, Qingdao Central Hospital, University of Health and Rehabilitation Sciences, Qingdao, 266042, People's Republic of China
| | - Jilu Zheng
- Department of Urology, Qingdao Central Hospital, University of Health and Rehabilitation Sciences, Qingdao, 266042, People's Republic of China
| | - Ji Lv
- Department of Urology, Qingdao Central Hospital, University of Health and Rehabilitation Sciences, Qingdao, 266042, People's Republic of China.
| | - Tao Cui
- Department of Urology, Qingdao Central Hospital, University of Health and Rehabilitation Sciences, Qingdao, 266042, People's Republic of China.
| | - Chunlei Liu
- Department of Urology, Qingdao Central Hospital, University of Health and Rehabilitation Sciences, Qingdao, 266042, People's Republic of China.
| | - Xiaopeng Lan
- Department of Urology, Qingdao Central Hospital, University of Health and Rehabilitation Sciences, Qingdao, 266042, People's Republic of China.
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Song G, Li K, Wang Z, Liu W, Xue Q, Liang J, Zhou Y, Geng H, Liu D. A fully automatic radiomics pipeline for postoperative facial nerve function prediction of vestibular schwannoma. Neuroscience 2025; 574:124-137. [PMID: 40210197 DOI: 10.1016/j.neuroscience.2025.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2025] [Revised: 03/28/2025] [Accepted: 04/05/2025] [Indexed: 04/12/2025]
Abstract
Vestibular schwannoma (VS) is the most prevalent intracranial schwannoma. Surgery is one of the options for the treatment of VS, with the preservation of facial nerve (FN) function being the primary objective. Therefore, postoperative FN function prediction is essential. However, achieving automation for such a method remains a challenge. In this study, we proposed a fully automatic deep learning approach based on multi-sequence magnetic resonance imaging (MRI) to predict FN function after surgery in VS patients. We first developed a segmentation network 2.5D Trans-UNet, which combined Transformer and U-Net to optimize contour segmentation for radiomic feature extraction. Next, we built a deep learning network based on the integration of 1DConvolutional Neural Network (1DCNN) and Gated Recurrent Unit (GRU) to predict postoperative FN function using the extracted features. We trained and tested the 2.5D Trans-UNet segmentation network on public and private datasets, achieving accuracies of 89.51% and 90.66%, respectively, confirming the model's strong performance. Then Feature extraction and selection were performed on the private dataset's segmentation results using 2.5D Trans-UNet. The selected features were used to train the 1DCNN-GRU network for classification. The results showed that our proposed fully automatic radiomics pipeline outperformed the traditional radiomics pipeline on the test set, achieving an accuracy of 88.64%, demonstrating its effectiveness in predicting the postoperative FN function in VS patients. Our proposed automatic method has the potential to become a valuable decision-making tool in neurosurgery, assisting neurosurgeons in making more informed decisions regarding surgical interventions and improving the treatment of VS patients.
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Affiliation(s)
- Gang Song
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Keyuan Li
- School of Information Science and Technology, Beijing University of Technology, Beijing, China
| | - Zhuozheng Wang
- School of Information Science and Technology, Beijing University of Technology, Beijing, China
| | - Wei Liu
- School of Information Science and Technology, Beijing University of Technology, Beijing, China.
| | - Qi Xue
- School of Information Science and Technology, Beijing University of Technology, Beijing, China
| | - Jiantao Liang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yiqiang Zhou
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Haoming Geng
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Dong Liu
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
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Li N, Liu X, Xia X, Liu X, Wang G, Duan C. An MRI-based deep transfer learning radiomics nomogram for predicting meningioma grade. Sci Rep 2025; 15:16614. [PMID: 40360672 PMCID: PMC12075611 DOI: 10.1038/s41598-025-01665-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2024] [Accepted: 05/07/2025] [Indexed: 05/15/2025] Open
Abstract
The aim of this study was to establish a nomogram based on clinical, radiomics, and deep transfer learning (DTL) features to predict meningioma grade. Three hundred forty meningiomas from one hospital composed the training set, and 102 meningiomas from another hospital composed the test set. The enhanced T1 WI images were used for analysis. The clinical, radiomics and DTL features were selected to construct the model. Radiomics and DTL scores were calculated. The deep transfer learning radiomics (DTLR) nomogram was developed on the basis of selected clinical features, radiomics scores and DTL scores. Receiver operating characteristic (ROC) curves and decision curve analysis (DCA) curves were drawn. The clinical features of sex, shape, indistinct margin and peritumoral edema were selected and used to construct the clinical model. Thirty-two radiomics features and 28 DTL features were selected for model construction. The clinical model had an AUC of 0.788. (95% CI: 0.6996-0.8756), with an accuracy of 0.745, a sensitivity of 0.941, and a specificity of 0.549 in the test set. The DTLR nomogram had the highest AUC of 0.866 (95% CI: 0.7984-0.9340), with an accuracy of 0.804, a sensitivity of 0.745, and a specificity of 0.863 in the test set. Compared with the other models, the DTLR nomogram had the greatest net benefit according to the DCA. There was a significant difference between the DTLR nomogram and the clinical model, no significant difference between the rest models in DeLong test.The DTLR nomogram has superior predictive value in DCA and could be a valuable method in clinical decision-making. Given the results of DeLong test, only the radiomics model is sufficient and there is no need to add DTL features. As a new attempt, the DTLR nomogram needs to be improved in the future study.
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Affiliation(s)
- Nan Li
- Department of Information Management, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xuejun Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiang Su Road, Shinan District, Qingdao, Shandong Province, China
| | - Xiaona Xia
- Department of Radiology, Cheeloo College of Medicine, Qilu Hospital (Qingdao), Shandong University, Qingdao, China
| | - Xushun Liu
- Laizhou People's Hospital, Yantai, China
| | - Gang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiang Su Road, Shinan District, Qingdao, Shandong Province, China
| | - Chongfeng Duan
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiang Su Road, Shinan District, Qingdao, Shandong Province, China.
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Yu B, Huang C, Fan X, Liu D, Zhang Y, Ding J. Differentiation Between Parotid Adenolymphoma and Malignant Tumor Based on Multimodal Functional MRI of Radiomics and Intratumoral Vascular ITSS Classification. Ann Surg Oncol 2025:10.1245/s10434-025-17399-2. [PMID: 40358780 DOI: 10.1245/s10434-025-17399-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2025] [Accepted: 04/13/2025] [Indexed: 05/15/2025]
Abstract
BACKGROUND Differentiating between parotid adenolymphoma and malignant tumors remains challenging. PURPOSE This study aims to improve preoperative diagnosis accuracy by evaluating the role of multimodal functional magnetic resonance imaging (MRI) and advanced radiomics analysis. METHODS We retrospectively analyzed 124 patients with adenolymphoma and malignant parotid tumors, divided into primary (n = 84) and test (n = 40) cohorts. Tumor regions were manually labeled on susceptibility-weighted imaging (SWI), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (CE-T1WI). Seven radiomics models were constructed using logistic regression. We also incorporated intratumoral susceptibility signal (ITSS) grading and performed histogram analysis of apparent diffusion coefficient (ADC) maps. RESULTS The united radiomics model combining SWI, DWI, and CE-T1WI showed the highest diagnostic performance (area under the curve (AUC) = 0.95, accuracy = 0.93, specificity = 0.93) in the primary cohort, outperforming single-sequence and double-sequence models. The test set validated the model's good diagnostic performance (AUC = 0.9). ITSS grading significantly differed between adenolymphomas and malignant tumors (p < 0.001). ADC histogram analysis revealed significant differences in mean, 10th percentile, and kurtosis values between the two groups. CONCLUSIONS The multisequence radiomics model combining DWI, SWI, and CE-T1WI provides a comprehensive and accurate noninvasive approach for differentiating parotid adenolymphoma from malignant tumors. This method helps avoid the risks associated with invasive procedures, such as tumor cell implantation and metastasis, while guiding personalized surgical decision-making. By offering a novel diagnostic tool, this study enhances the precision of preoperative tumor characterization and supports more effective treatment planning and prognosis assessment for patients with parotid gland tumors.
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Affiliation(s)
- Baoting Yu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise and League of PHD Technology Co., Ltd, Beijing, China
| | - Xiaofei Fan
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Dongyao Liu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Yuting Zhang
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Jun Ding
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, China.
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Hou X, Chen K, Luo H, Xu W, Li X. Identification of HER2-over-expression, HER2-low-expression, and HER2-zero-expression statuses in breast cancer based on 18F-FDG PET/CT radiomics. Cancer Imaging 2025; 25:62. [PMID: 40355910 PMCID: PMC12070556 DOI: 10.1186/s40644-025-00880-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2024] [Accepted: 04/30/2025] [Indexed: 05/15/2025] Open
Abstract
PURPOSE According to the updated classification system, human epidermal growth factor receptor 2 (HER2) expression statuses are divided into the following three groups: HER2-over-expression, HER2-low-expression, and HER2-zero-expression. HER2-negative expression was reclassified into HER2-low-expression and HER2-zero-expression. This study aimed to identify three different HER2 expression statuses for breast cancer (BC) patients using PET/CT radiomics and clinicopathological characteristics. METHODS AND MATERIALS A total of 315 BC patients who met the inclusion and exclusion criteria from two institutions were retrospectively included. The patients in institution 1 were divided into the training set and the independent validation set according to the ratio of 7:3, and institution 2 was used as the external validation set. According to the results of pathological examination, all BC patients were divided into HER2-over-expression, HER2-low-expression, and HER2-zero-expression. First, PET/CT radiomic features and clinicopathological features based on each patient were extracted and collected. Second, multiple methods were used to perform feature screening and feature selection. Then, four machine learning classifiers, including logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF), were constructed to identify HER2-over-expression vs. others, HER2-low-expression vs. others, and HER2-zero-expression vs. others. The receiver operator characteristic (ROC) curve was plotted to measure the model's predictive power. RESULTS According to the feature screening process, 8, 10, and 2 radiomics features and 2 clinicopathological features were finally selected to construct three prediction models (HER2-over-expression vs. others, HER2-low-expression vs. others, and HER2-zero-expression vs. others). For HER2-over-expression vs. others, the RF model outperformed other models with an AUC value of 0.843 (95%CI: 0.774-0.897), 0.785 (95%CI: 0.665-0.877), and 0.788 (95%CI: 0.708-0.868) in the training set, independent validation set, and external validation set. Concerning HER2-low-expression vs. others, the outperformance of the LR model over other models was identified with an AUC value of 0.783 (95%CI: 0.708-0.846), 0.756 (95%CI: 0.634-0.854), and 0.779 (95%CI: 0.698-0.860) in the training set, independent validation set, and external validation set. Whereas, the KNN model was confirmed as the optimal model to distinguish HER2-zero-expression from others, with an AUC value of 0.929 (95%CI: 0.890-0.958), 0.847 (95%CI: 0.764-0.910), and 0.835 (95%CI: 0.762-0.908) in the training set, independent validation set, and external validation set. CONCLUSION Combined PET/CT radiomic models integrating with clinicopathological characteristics are non-invasively predictive of different HER2 statuses of BC patients.
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Affiliation(s)
- Xuefeng Hou
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin, 300060, China
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, 300060, China
| | - Kun Chen
- Department of Nuclear Medicine, Hangzhou Institute of Medicine (HIM), Zhejiang Cancer Hospital, Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Huiwen Luo
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin, 300060, China
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, 300060, China
| | - Wengui Xu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin, 300060, China.
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China.
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, 300060, China.
| | - Xiaofeng Li
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin, 300060, China.
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China.
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, 300060, China.
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Sakamoto K, Okabayashi K, Seishima R, Shigeta K, Kiyohara H, Mikami Y, Kanai T, Kitagawa Y. Radiomics prediction of surgery in ulcerative colitis refractory to medical treatment. Tech Coloproctol 2025; 29:113. [PMID: 40347388 PMCID: PMC12065716 DOI: 10.1007/s10151-025-03139-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2024] [Accepted: 03/08/2025] [Indexed: 05/12/2025]
Abstract
BACKGROUND The surgeries in drug-resistant ulcerative colitis are determined by complex factors. This study evaluated the predictive performance of radiomics analysis on the basis of whether patients with ulcerative colitis in hospital were in the surgical or medical treatment group by discharge from hospital. METHODS This single-center retrospective cohort study used CT at admission of patients with US admitted from 2015 to 2022. The target of prediction was whether the patient would undergo surgery by the time of discharge. Radiomics features were extracted using the rectal wall at the level of the tailbone tip of the CT as the region of interest. CT data were randomly classified into a training cohort and a validation cohort, and LASSO regression was performed using the training cohort to create a formula for calculating the radiomics score. RESULTS A total of 147 patients were selected, and data from 184 CT scans were collected. Data from 157 CT scans matched the selection criteria and were included. Five features were used for the radiomics score. Univariate logistic regression analysis of clinical information detected a significant influence of severity (p < 0.001), number of drugs used until surgery (p < 0.001), Lichtiger score (p = 0.024), and hemoglobin (p = 0.010). Using a nomogram combining these items, we found that the discriminatory power in the surgery and medical treatment groups was AUC 0.822 (95% confidence interval (CI) 0.841-0.951) for the training cohort and AUC 0.868 (95% CI 0.729-1.000) for the validation cohort, indicating a good ability to discriminate the outcomes. CONCLUSIONS Radiomics analysis of CT images of patients with US at the time of admission, combined with clinical data, showed high predictive ability regarding a treatment strategy of surgery or medical treatment.
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Affiliation(s)
- K Sakamoto
- Department of Surgery, Keio University School of Medicine, 35 Shinano-Machi Shinjuku-Ku, Tokyo, 1608582, Japan
| | - K Okabayashi
- Department of Surgery, Keio University School of Medicine, 35 Shinano-Machi Shinjuku-Ku, Tokyo, 1608582, Japan.
| | - R Seishima
- Department of Surgery, Keio University School of Medicine, 35 Shinano-Machi Shinjuku-Ku, Tokyo, 1608582, Japan
| | - K Shigeta
- Department of Surgery, Keio University School of Medicine, 35 Shinano-Machi Shinjuku-Ku, Tokyo, 1608582, Japan
| | - H Kiyohara
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Y Mikami
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - T Kanai
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Y Kitagawa
- Department of Surgery, Keio University School of Medicine, 35 Shinano-Machi Shinjuku-Ku, Tokyo, 1608582, Japan
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Liu H, He M, Gao E, Zhang Y, Cheng J, Zhao G. Multiparametric MRI-Based Radiomics for Identifying Primary Central Nervous System Diffuse Large B-cell Lymphomas' Pathological Subtypes. Acad Radiol 2025:S1076-6332(25)00396-4. [PMID: 40348709 DOI: 10.1016/j.acra.2025.04.046] [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: 12/10/2024] [Revised: 04/18/2025] [Accepted: 04/18/2025] [Indexed: 05/14/2025]
Abstract
RATIONALE AND OBJECTIVES To explore the predictive potential of radiomics features extracted from preoperative multiparametric magnetic resonance imaging (MRI) for identifying pathological subtypes of primary central nervous system diffuse large B-cell lymphomas (PCNS-DLBCL). METHODS This study recruited 186 patients with PCNS-DLBCL, including 55 with germinal center B-cell-like (GCB) subtype and 131 with non-GCB subtype. The largest abnormal signal regions of the tumor were automatically segmented in T1-weighted images (T1WI), T2-weighted images, T2 fluid-attenuated inversion recovery, contrast-enhanced T1-weighted (CE-T1WI), and apparent diffusion coefficient (ADC) maps, respectively. Radiomics features were extracted from preprocessed multiparameter preoperative MRI images. To identify GCB and non-GCB subtypes, radiomics models were constructed based on each MRI sequence and combinations of sequences. Clinical models and models combining radiomics and clinical features were also constructed to compare performance. RESULTS Radiomics models combining multiple sequences generally outperformed single-sequence radiomics models. The ADC+CE-T1WI model exhibited superior discriminative power, with an area under the curve of 0.867 (95% CI, 0.745-0.988). Models incorporating more sequences (3-5 sequences) did not demonstrate better performance. The performance of the model combining radiomics features with clinical features showed no improvement. CONCLUSION Radiomics based on multiparametric MRI have independent value in predicting the pathological subtypes of PCNS-DLBCL patients.
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Affiliation(s)
- Hao Liu
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China (H.L., E.G., Y.Z., J.C., G.Z.)
| | - Mengyang He
- School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450001, China (M.H.)
| | - Eryuan Gao
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China (H.L., E.G., Y.Z., J.C., G.Z.)
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China (H.L., E.G., Y.Z., J.C., G.Z.)
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China (H.L., E.G., Y.Z., J.C., G.Z.); Tianjian Laboratory of Advanced Biomedical Sciences, Zhengzhou 450007, China (J.C., G.Z.)
| | - Guohua Zhao
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China (H.L., E.G., Y.Z., J.C., G.Z.); Tianjian Laboratory of Advanced Biomedical Sciences, Zhengzhou 450007, China (J.C., G.Z.).
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Urso L, Badrane I, Manco L, Castello A, Lancia F, Collavino J, Crestani A, Castellani M, Cittanti C, Bartolomei M, Giannarini G. The Role of Radiomics and Artificial Intelligence Applied to Staging PSMA PET in Assessing Prostate Cancer Aggressiveness. J Clin Med 2025; 14:3318. [PMID: 40429314 PMCID: PMC12112297 DOI: 10.3390/jcm14103318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2025] [Revised: 04/24/2025] [Accepted: 05/03/2025] [Indexed: 05/29/2025] Open
Abstract
Background: PSMA PET is essential tool in the management of prostate cancer (PCa) patients in various clinical settings of disease. The tremendous growth of the implementation of radiomics and artificial intelligence (AI) in medical imaging techniques has led to an increasing interest in their application in prostate-specific membrane antigen (PSMA) PET. The aim of this article is to systemically review the current literature that explores radiomics and AI analyses of staging PSMA PET towards its potential application in clinical practice. Methods: A systematic research of the literature on three international databases (PubMed, Scopus, and Web of Science) identified a total of 166 studies. An initial screening excluded 68 duplicates and 72 articles relevant to other topics. Finally, 21 studies met the inclusion criteria. Conclusions: The literature suggests that radiomic analysis could improve the characterization of tumor aggressiveness, the prediction of extra-capsular extension, and seminal vesicles involvement. Moreover, AI models could contribute to predicting BCR after radical treatment. Limitations regarding heterogeneous objectives of investigation, and methodological standardization of radiomics analysis still represent the main obstacle to overcome in order to see these technology break through into daily clinical practice.
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Affiliation(s)
- Luca Urso
- Department of Translational Medicine, University of Ferrara, Via Aldo Moro 8, 44124 Ferrara, Italy; (L.U.); (I.B.); (C.C.)
- Nuclear Medicine Unit, Onco-Hematology Department, University Hospital of Ferrara, 44124 Ferrara, Italy;
| | - Ilham Badrane
- Department of Translational Medicine, University of Ferrara, Via Aldo Moro 8, 44124 Ferrara, Italy; (L.U.); (I.B.); (C.C.)
- Nuclear Medicine Unit, Onco-Hematology Department, University Hospital of Ferrara, 44124 Ferrara, Italy;
| | - Luigi Manco
- Medical Physics Unit, University Hospital of Ferrara, 44124 Ferrara, Italy;
| | - Angelo Castello
- Nuclear Medicine Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy;
| | - Federica Lancia
- Oncology Unit, University Hospital of Ferrara, 44124 Ferrara, Italy;
| | - Jeanlou Collavino
- Urology Unit, Santa Maria della Misericordia University Hospital, 33100 Udine, Italy; (J.C.); (A.C.); (G.G.)
| | - Alessandro Crestani
- Urology Unit, Santa Maria della Misericordia University Hospital, 33100 Udine, Italy; (J.C.); (A.C.); (G.G.)
- Department of Medicine, University of Udine, 33100 Udine, Italy
| | - Massimo Castellani
- Nuclear Medicine Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy;
| | - Corrado Cittanti
- Department of Translational Medicine, University of Ferrara, Via Aldo Moro 8, 44124 Ferrara, Italy; (L.U.); (I.B.); (C.C.)
- Nuclear Medicine Unit, Onco-Hematology Department, University Hospital of Ferrara, 44124 Ferrara, Italy;
| | - Mirco Bartolomei
- Nuclear Medicine Unit, Onco-Hematology Department, University Hospital of Ferrara, 44124 Ferrara, Italy;
| | - Gianluca Giannarini
- Urology Unit, Santa Maria della Misericordia University Hospital, 33100 Udine, Italy; (J.C.); (A.C.); (G.G.)
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Mali SA, Rad NM, Woodruff HC, Depeursinge A, Andrearczyk V, Lambin P. Harmonizing CT scanner acquisition variability in an anthropomorphic phantom: A comparative study of image-level and feature-level harmonization using GAN, ComBat, and their combination. PLoS One 2025; 20:e0322365. [PMID: 40344028 PMCID: PMC12063804 DOI: 10.1371/journal.pone.0322365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2024] [Accepted: 03/20/2025] [Indexed: 05/11/2025] Open
Abstract
PURPOSE Radiomics allows for the quantification of medical images and facilitates precision medicine. Many radiomic features derived from computed tomography (CT) are sensitive to variations across scanners, reconstruction settings, and acquisition protocols. In this phantom study, eight different CT reconstruction parameters were varied to explore image- and feature-level harmonization approaches to improve tissue classification. METHODS Varying reconstructions of an anthropomorphic radiopaque phantom containing three lesion categories (metastasis, hemangioma, and benign cyst) and normal liver tissue were used for evaluating two harmonization methods and their combination: (i) generative adversarial networks (GANs) at the image level; (ii) ComBat at the feature level, and (iii) a combination of (i) and (ii). A total of 93 texture and intensity features were extracted from each tissue class before and after image-level harmonization and were also harmonized at the feature level. Reproducibility and stability were assessed via the Concordance Correlation Coefficient (CCC) and pairwise comparisons using paired stability tests. The ability of features to discriminate between tissue classes was assessed by measuring the area under the receiver operating characteristic curve. The global reproducibility and discriminative power were assessed by averaging over the entire dataset and across all tissue types. RESULTS ComBat improved reproducibility by 31.58% and stability by 5.24%, while GAN increased reproducibility by 8% it reduced stability by 4.33%. Classification analysis revealed that ComBat increased average AUC by 15.19%, whereas GAN decreased AUC by 2.56%. CONCLUSION While GAN qualitatively enhances image harmonization, ComBat provides superior statistical improvements in feature stability and classification performance, highlighting the importance of robust feature-level harmonization in radiomics.
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Affiliation(s)
- Shruti Atul Mali
- The D-Lab, Department of Precision Medicine, GROW- Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
| | - Nastaran Mohammadian Rad
- The D-Lab, Department of Precision Medicine, GROW- Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
| | - Henry C. Woodruff
- The D-Lab, Department of Precision Medicine, GROW- Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
- Department of Radiology and Nuclear Medicine, GROW- Research Institute School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Adrien Depeursinge
- Institute of Information Systems, University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Vincent Andrearczyk
- Institute of Information Systems, University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW- Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
- Department of Radiology and Nuclear Medicine, GROW- Research Institute School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, Netherlands
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Cvachovec P, Bicu AS, Schmidt R, Siefert V, Eckl M, Willam M, Clausen S, Froelich MF, Schoenberg SO, Ehmann M, Buergy D, Fleckenstein J, Giordano FA, Boda-Heggemann J, Dreher C. Longitudinal stability of HyperSight TM-CBCT based radiomic features in patients with CT guided adaptive SBRT for prostate cancer. Sci Rep 2025; 15:15863. [PMID: 40335645 PMCID: PMC12059028 DOI: 10.1038/s41598-025-99920-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2024] [Accepted: 04/23/2025] [Indexed: 05/09/2025] Open
Abstract
CT-guided adaptive radiotherapy (aRT) based on HyperSightTM-CBCT provides high-quality imaging, allowing quantitative radiomic feature analysis as a monitoring tool. This study comprehensively evaluates the stability of radiomic features, as potential imaging biomarkers, in pelvic structures of prostate cancer patients treated with adaptive stereotactic body radiation therapy (SBRT). Between December 2023 and July 2024, 32 patients with localized prostate cancer underwent adaptive SBRT at the Ethos® linear accelerator (Varian, Siemens Healthineers) with HyperSight-CBCT imaging. Longitudinal stability was assessed by intraclass correlation coefficient (ICC) over five fractions of aRT for target structures and non-hollow organs at risk. In pooled organs at risk, 93.0% of features showed very high stability (ICC > 0.9) compared to 67.4% in pooled target structures, indicating significantly lower stability for target structures (p = 0.00009129). Second-order features demonstrated greater stability than conventional and shape-based features (p = 0.0433, p = 0.0252). Fraction number significantly affected longitudinal prostate feature variability (p = 0.0135). This study comprehensively analyzed HyperSight-CBCT imaging to evaluate longitudinal stability of radiomic features during adaptive SBRT for prostate cancer. The trends observed will provide a framework for future CT-guided aRT studies, facilitating quantitative imaging analysis of radiological biomarkers for clinical translation and improving personalized treatment.
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Affiliation(s)
- Paula Cvachovec
- Department of Radiation Oncology, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68167, Mannheim, Germany
| | - Alicia S Bicu
- Department of Radiation Oncology, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68167, Mannheim, Germany
- DKFZ-Hector Cancer Institute, University Medical Centre Mannheim, Mannheim, Germany
| | - Ralf Schmidt
- Department of Radiation Oncology, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68167, Mannheim, Germany
- DKFZ-Hector Cancer Institute, University Medical Centre Mannheim, Mannheim, Germany
| | - Victor Siefert
- Department of Radiation Oncology, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68167, Mannheim, Germany
- DKFZ-Hector Cancer Institute, University Medical Centre Mannheim, Mannheim, Germany
| | - Miriam Eckl
- Department of Radiation Oncology, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68167, Mannheim, Germany
| | - Marvin Willam
- Department of Radiation Oncology, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68167, Mannheim, Germany
| | - Sven Clausen
- Department of Radiation Oncology, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68167, Mannheim, Germany
| | - Matthias F Froelich
- Department of Radiology and Nuclear Medicine, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Stefan O Schoenberg
- Department of Radiology and Nuclear Medicine, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Michael Ehmann
- Department of Radiation Oncology, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68167, Mannheim, Germany
- DKFZ-Hector Cancer Institute, University Medical Centre Mannheim, Mannheim, Germany
| | - Daniel Buergy
- Department of Radiation Oncology, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68167, Mannheim, Germany
- DKFZ-Hector Cancer Institute, University Medical Centre Mannheim, Mannheim, Germany
| | - Jens Fleckenstein
- Department of Radiation Oncology, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68167, Mannheim, Germany
| | - Frank A Giordano
- Department of Radiation Oncology, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68167, Mannheim, Germany
- DKFZ-Hector Cancer Institute, University Medical Centre Mannheim, Mannheim, Germany
- Mannheim Institute for Intelligent Systems in Medicine (MIiSM), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Judit Boda-Heggemann
- Department of Radiation Oncology, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68167, Mannheim, Germany
- DKFZ-Hector Cancer Institute, University Medical Centre Mannheim, Mannheim, Germany
| | - Constantin Dreher
- Department of Radiation Oncology, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68167, Mannheim, Germany.
- DKFZ-Hector Cancer Institute, University Medical Centre Mannheim, Mannheim, Germany.
- Mannheim Institute for Intelligent Systems in Medicine (MIiSM), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
- Junior Research Group "Intelligent Imaging for adaptive Radiotherapy", Mannheim Institute for Intelligent Systems in Medicine (MIiSM), University of Heidelberg, Mannheim, Germany.
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Fu M, Qi H, Zhu S, Gao Y, Li Y, Wu J, Zhu D. Computed tomography based radiomics signature for predicting the expression of vascular endothelial growth factor in pediatric patients with nephroblastoma. Sci Rep 2025; 15:15844. [PMID: 40328996 PMCID: PMC12056030 DOI: 10.1038/s41598-025-99610-8] [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: 09/25/2024] [Accepted: 04/21/2025] [Indexed: 05/08/2025] Open
Abstract
To construct a computed tomography (CT) based radiomics signature and assess its performance in predicting vascular endothelial growth factor (VEGF) expression in pediatric patients with nephroblastoma. A total of 73 pediatric nephroblastomaL patients were enrolled (51 in the training cohort and 22 in the test cohort). The region of interest manually marked on the CT images served as the basis for the automatic extraction of radiomics features. A radiomics score was generated utilizing the radiomics signature based formula after retaining a subset of radiomics features to create a radiomics signature. Clinical elements, such as clinicopathological information and CT imaging characteristics, were used to create a clinical model. With the inclusion of a radiomics signature and clinical characteristics, a composite nomogram was created. Decision curve analysis (DCA) was used to evaluate the prediction performance. 5 carefully chosen radiomics features were used to create the radiomics signature. Next, the radiomics score was determined. In the training cohort and the test cohort, the logistic regression model's area under the curve was 0.761 and 0.791, respectively. Based on the radiomics signature and clinical variables, the clinical radiomics nomogram demonstrated its ability to accurately predict the level of VEGF expression. DCA verified the clinical value of the clinical radiomics nomogram. In pediatric patients with nephroblastoma, the radiomics model based on the CT radiomics signature may accurately predict the level of VEGF expression.
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Affiliation(s)
- Ma Fu
- Department of neonatology, Lianyungang Maternal and Child Health Care Hospital, Lianyungang, China
| | - Han Qi
- Department of Emergency Surgery, The Second People's Hospital of Lianyungang, Affiliated to Kangda College of Nanjing Medical University, Lianyungang, China
| | - Suyue Zhu
- Department of Pediatric, Suqian Hospital Affiliated to Xuzhou Medical University, Suqian, China
| | - Yan Gao
- Department of neonatology, Lianyungang Maternal and Child Health Care Hospital, Lianyungang, China
| | - Yanlin Li
- Department of Pediatric, Lianyungang Maternal and Child Health Care Hospital, Lianyungang, 222000, China.
| | - Jian Wu
- Department of Pediatric, Xiangcheng District People's Hospital, Suzhou, 215000, China.
| | - Dongsheng Zhu
- Department of Pediatric Surgery, The First People's Hospital of Lianyungang, Affiliated to Kangda College of Nanjing Medical University, Lianyungang, 222000, China.
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