Insights into Imaging最新文献

{"title":"Development and validation of clinical-radiomics deep learning model based on MRI for endometrial cancer molecular subtypes classification.","authors":"Wenyi Yue, Ruxue Han, Haijie Wang, Xiaoyun Liang, He Zhang, Hua Li, Qi Yang","doi":"10.1186/s13244-025-01966-y","DOIUrl":"10.1186/s13244-025-01966-y","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to develop and validate a clinical-radiomics deep learning (DL) model based on MRI for endometrial cancer (EC) molecular subtypes classification.</p><p><strong>Methods: </strong>This multicenter retrospective study included EC patients undergoing surgery, MRI, and molecular pathology diagnosis across three institutions from January 2020 to March 2024. Patients were divided into training, internal, and external validation cohorts. A total of 386 handcrafted radiomics features were extracted from each MR sequence, and MoCo-v2 was employed for contrastive self-supervised learning to extract 2048 DL features per patient. Feature selection integrated selected features into 12 machine learning methods. Model performance was evaluated with the AUC.</p><p><strong>Results: </strong>A total of 526 patients were included (mean age, 55.01 ± 11.07). The radiomics model and clinical model demonstrated comparable performance across the internal and external validation cohorts, with macro-average AUCs of 0.70 vs 0.69 and 0.70 vs 0.67 (p = 0.51), respectively. The radiomics DL model, compared to the radiomics model, improved AUCs for POLEmut (0.68 vs 0.79), NSMP (0.71 vs 0.74), and p53abn (0.76 vs 0.78) in the internal validation (p = 0.08). The clinical-radiomics DL Model outperformed both the clinical model and radiomics DL model (macro-average AUC = 0.79 vs 0.69 and 0.73, in the internal validation [p = 0.02], 0.74 vs 0.67 and 0.69 in the external validation [p = 0.04]).</p><p><strong>Conclusions: </strong>The clinical-radiomics DL model based on MRI effectively distinguished EC molecular subtypes and demonstrated strong potential, with robust validation across multiple centers. Future research should explore larger datasets to further uncover DL's potential.</p><p><strong>Critical relevance statement: </strong>Our clinical-radiomics DL model based on MRI has the potential to distinguish EC molecular subtypes. This insight aids in guiding clinicians in tailoring individualized treatments for EC patients.</p><p><strong>Key points: </strong>Accurate classification of EC molecular subtypes is crucial for prognostic risk assessment. The clinical-radiomics DL model outperformed both the clinical model and the radiomics DL model. The MRI features exhibited better diagnostic performance for POLEmut and p53abn.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"107"},"PeriodicalIF":4.1,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12084453/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144077859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Guidelines and recommendations for radiologist staffing, education and training.","authors":"Adrian P Brady, Christian Loewe, Boris Brkljacic, Graciano Paulo, Martina Szucsich, Monika Hierath","doi":"10.1186/s13244-025-01982-y","DOIUrl":"10.1186/s13244-025-01982-y","url":null,"abstract":"","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"105"},"PeriodicalIF":4.1,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12081806/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144077853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Contrast-enhanced MRI-based intratumoral heterogeneity assessment for predicting lymph node metastasis in resectable pancreatic ductal adenocarcinoma.","authors":"Junjian Shen, Qing Li, Lei Li, Tianyu Lu, Jun Han, Zongyu Xie, Peng Wang, Zirui Cao, Mengsu Zeng, Jianjun Zhou, Tianzhu Yu, Yaolin Xu, Haitao Sun","doi":"10.1186/s13244-025-01973-z","DOIUrl":"10.1186/s13244-025-01973-z","url":null,"abstract":"","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"103"},"PeriodicalIF":4.1,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12081796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144077852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Workplace equity in radiology: a nationwide survey by the Radiological Society of Finland.","authors":"Suvi Marjasuo, Milja Holstila, Jussi Hirvonen","doi":"10.1186/s13244-025-01975-x","DOIUrl":"10.1186/s13244-025-01975-x","url":null,"abstract":"<p><strong>Objectives: </strong>The issue of equity among medical professionals has been extensively discussed in recent literature. Gender inequity, in particular, is a well-documented phenomenon within scientific communities. The Radiological Society of Finland undertook a national survey to assess equity among radiologists in Finland, with the primary hypothesis of equity prevailing in the radiological community.</p><p><strong>Methods: </strong>A cross-sectional study in the form of an online questionnaire was developed to investigate occupational equity and demographic variables. This survey was disseminated to the heads of radiological departments in all Finnish public healthcare units and the largest radiological units within the private sector, with instructions to distribute to their medical staff. The questionnaire was accessible for responses from May 1 to June 16, 2024.</p><p><strong>Results: </strong>A total of 259 answers were received, representing 31% of all radiologists and residents working in Finland. Among the respondents, 137/259 (52.9%) identified as female, 118/259 (45.6%) male, and 1/259 (0.4%) other, with three choosing not to answer. A significant proportion, 63/259 (24.3%), reported having witnessed discriminatory behavior, while 41/259 (15.8%) had personally experienced discrimination. The prevalence of respondents having witnessed workplace discrimination was notably higher in female respondents (42/131, 32.1%) than in males (18/113, 15.9%) or others (0%) (p = 0.012). The most cited bases for discrimination included gender, opinion, age, and cultural background.</p><p><strong>Conclusions: </strong>Perceived discrimination is prevalent within the Finnish radiological community. Gender was reported as the most common suspected grounds of perceived discriminatory behavior.</p><p><strong>Critical relevance statement: </strong>This study is the first to explore equity and diversity among radiologists in Finland. This broader approach offers a more comprehensive perspective, and the findings aim to support efforts toward greater inclusivity and equity within the field.</p><p><strong>Key points: </strong>One-quarter of radiologists in Finland reported witnessing and one-sixth reported personally experiencing discrimination in the workplace. Gender was suspected to be the most common basis for discrimination, followed by differences in opinion, age, and cultural background. Respondents were largely unaware of whether the reported incidents had been addressed. Increasing transparency and communication may help reduce perceived discrimination.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"106"},"PeriodicalIF":4.1,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12081815/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144077864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: The role of imaging in defining cardiovascular risk to help cancer patient management: a scoping review.","authors":"Roberto Farì, Giulia Besutti, Pierpaolo Pattacini, Guido Ligabue, Francesco Piroli, Francesca Mantovani, Alessandro Navazio, Mario Larocca, Carmine Pinto, Paolo Giorgi Rossi, Luigi Tarantini","doi":"10.1186/s13244-025-01981-z","DOIUrl":"10.1186/s13244-025-01981-z","url":null,"abstract":"","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"104"},"PeriodicalIF":4.1,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12081778/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144077855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT-based AI framework leveraging multi-scale features for predicting pathological grade and Ki67 index in clear cell renal cell carcinoma: a multicenter study.","authors":"Huancheng Yang, Yueyue Zhang, Fan Li, Weihao Liu, Haoyang Zeng, Haoyuan Yuan, Zixi Ye, Zexin Huang, Yangguang Yuan, Ye Xiang, Kai Wu, Hanlin Liu","doi":"10.1186/s13244-025-01980-0","DOIUrl":"https://doi.org/10.1186/s13244-025-01980-0","url":null,"abstract":"<p><strong>Purpose: </strong>To explore whether a CT-based AI framework, leveraging multi-scale features, can offer a non-invasive approach to accurately predict pathological grade and Ki67 index in clear cell renal cell carcinoma (ccRCC).</p><p><strong>Methods: </strong>In this multicenter retrospective study, a total of 1073 pathologically confirmed ccRCC patients from seven cohorts were split into internal cohorts (training and validation sets) and an external test set. The AI framework comprised an image processor, a 3D-kidney and tumor segmentation model by 3D-UNet, a multi-scale features extractor built upon unsupervised learning, and a multi-task classifier utilizing XGBoost. A quantitative model interpretation technique, known as SHapley Additive exPlanations (SHAP), was employed to explore the contribution of multi-scale features.</p><p><strong>Results: </strong>The 3D-UNet model showed excellent performance in segmenting both the kidney and tumor regions, with Dice coefficients exceeding 0.92. The proposed multi-scale features model exhibited strong predictive capability for pathological grading and Ki67 index, with AUROC values of 0.84 and 0.87, respectively, in the internal validation set, and 0.82 and 0.82, respectively, in the external test set. The SHAP results demonstrated that features from radiomics, the 3D Auto-Encoder, and dimensionality reduction all made significant contributions to both prediction tasks.</p><p><strong>Conclusions: </strong>The proposed AI framework, leveraging multi-scale features, accurately predicts the pathological grade and Ki67 index of ccRCC.</p><p><strong>Critical relevance statement: </strong>The CT-based AI framework leveraging multi-scale features offers a promising avenue for accurately predicting the pathological grade and Ki67 index of ccRCC preoperatively, indicating a direction for non-invasive assessment.</p><p><strong>Key points: </strong>Non-invasively determining pathological grade and Ki67 index in ccRCC could guide treatment decisions. The AI framework integrates segmentation, classification, and model interpretation, enabling fully automated analysis. The AI framework enables non-invasive preoperative detection of high-risk tumors, assisting clinical decision-making.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"102"},"PeriodicalIF":4.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12078187/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144077857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRI-based quantification of intratumoral heterogeneity for intrahepatic mass-forming cholangiocarcinoma grading: a multicenter study.","authors":"Liyong Zhuo, Wenjing Chen, Lihong Xing, Xiaomeng Li, Zijun Song, Jinghui Dong, Yanyan Zhang, Hongjun Li, Jingjing Cui, Yuxiao Han, Jiawei Hao, Jianing Wang, Xiaoping Yin, Caiying Li","doi":"10.1186/s13244-025-01985-9","DOIUrl":"https://doi.org/10.1186/s13244-025-01985-9","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to develop a quantitative approach to measure intratumor heterogeneity (ITH) using MRI scans and predict the pathological grading of intrahepatic mass-forming cholangiocarcinoma (IMCC).</p><p><strong>Methods: </strong>Preoperative MRI scans from IMCC patients were retrospectively obtained from five academic medical centers, covering the period from March 2018 to April 2024. Radiomic features were extracted from the whole tumor and its subregions, which were segmented using K-means clustering. An ITH index was derived from a habitat model integrating output probabilities of the subregions-based models. Significant variables from clinical laboratory-imaging features, radiomics, and the habitat model were integrated into a predictive model, and its performance was evaluated using the area under the receiver operating characteristic curve (AUC).</p><p><strong>Results: </strong>The final training and internal validation datasets included 197 patients (median age, 59 years [IQR, 52-65 years]); the external validation dataset included 43 patients (median age, 58.5 years [IQR, 52.25-69.75 years]). The habitat model achieved AUCs of 0.847 (95% CI: 0.783, 0.911) in the training set and 0.753 (95% CI: 0.595, 0.911) in the internal validation set. Furthermore, the combined model, integrating imaging variables, the habitat model, and radiomics model, demonstrated improved predictive performance, with AUCs of 0.895 (95% CI: 0.845, 0.944) in the training dataset, 0.790 (95% CI: 0.65, 0.931) in the internal validation dataset, and 0.815 (95% CI: 0.68, 0.951) in the external validation dataset.</p><p><strong>Conclusion: </strong>The combined model based on MRI-derived quantification of ITH, along with clinical, laboratory, radiological, and radiomic features, showed good performance in predicting IMCC grading.</p><p><strong>Critical relevance statement: </strong>This model, integrating MRI-derived intrahepatic mass-forming cholangiocarcinoma (IMCC) classification metrics with quantitative radiomic analysis of intratumor heterogeneity (ITH), demonstrates enhanced accuracy in tumor grade prediction, advancing risk stratification for clinical decision-making in IMCC management.</p><p><strong>Key points: </strong>Grading of intrahepatic mass-forming cholangiocarcinoma (IMCC) is important for risk stratification, clinical decision-making, and personalized therapeutic optimization. Quantitative intratumor heterogeneity can accurately predict the pathological grading of IMCC. This combined model provides higher diagnostic accuracy.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"101"},"PeriodicalIF":4.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12078897/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144077861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large language models for efficient whole-organ MRI score-based reports and categorization in knee osteoarthritis.","authors":"Yuxue Xie, Zhonghua Hu, Hongyue Tao, Yiwen Hu, Haoyu Liang, Xinmin Lu, Lei Wang, Xiangwen Li, Shuang Chen","doi":"10.1186/s13244-025-01976-w","DOIUrl":"10.1186/s13244-025-01976-w","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the performance of large language models (LLMs) in automatically generating whole-organ MRI score (WORMS)-based structured MRI reports and predicting osteoarthritis (OA) severity for the knee.</p><p><strong>Methods: </strong>A total of 160 consecutive patients suspected of OA were included. Knee MRI reports were reviewed by three radiologists to establish the WORMS reference standard for 39 key features. GPT-4o and GPT-4o-mini were prompted using in-context knowledge (ICK) and chain-of-thought (COT) to generate WORMS-based structured reports from original reports and to automatically predict the OA severity. Four Orthopedic surgeons reviewed original and LLM-generated reports to conduct pairwise preference and difficulty tests, and their review times were recorded.</p><p><strong>Results: </strong>GPT-4o demonstrated perfect performance in extracting the laterality of the knee (accuracy = 100%). GPT-4o outperformed GPT-4o mini in generating WORMS reports (Accuracy: 93.9% vs 76.2%, respectively). GPT-4o achieved higher recall (87.3% s 46.7%, p < 0.001), while maintaining higher precision compared to GPT-4o mini (94.2% vs 71.2%, p < 0.001). For predicting OA severity, GPT-4o outperformed GPT-4o mini across all prompt strategies (best accuracy: 98.1% vs 68.7%). Surgeons found it easier to extract information and gave more preference to LLM-generated reports over the original reports (both p < 0.001) while spending less time on each report (51.27 ± 9.41 vs 87.42 ± 20.26 s, p < 0.001).</p><p><strong>Conclusion: </strong>GPT-4o generated expert multi-feature, WORMS-based reports from original free-text knee MRI reports. GPT-4o with COT achieved high accuracy in categorizing OA severity. Surgeons reported greater preference and higher efficiency when using LLM-generated reports.</p><p><strong>Critical relevance statement: </strong>The perfect performance of generating WORMS-based reports and the high efficiency and ease of use suggest that integrating LLMs into clinical workflows could greatly enhance productivity and alleviate the documentation burden faced by clinicians in knee OA.</p><p><strong>Key points: </strong>GPT-4o successfully generated WORMS-based knee MRI reports. GPT-4o with COT prompting achieved impressive accuracy in categorizing knee OA severity. Greater preference and higher efficiency were reported for LLM-generated reports.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"100"},"PeriodicalIF":4.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12078906/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144018594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT-based quantification of intratumoral heterogeneity for predicting distant metastasis in retroperitoneal sarcoma.","authors":"Jun Xu, Jian-Guo Miao, Chen-Xi Wang, Yu-Peng Zhu, Ke Liu, Si-Yuan Qin, Hai-Song Chen, Ning Lang","doi":"10.1186/s13244-025-01977-9","DOIUrl":"https://doi.org/10.1186/s13244-025-01977-9","url":null,"abstract":"<p><strong>Objectives: </strong>Retroperitoneal sarcoma (RPS) is highly heterogeneous, leading to different risks of distant metastasis (DM) among patients with the same clinical stage. This study aims to develop a quantitative method for assessing intratumoral heterogeneity (ITH) using preoperative contrast-enhanced CT (CECT) scans and evaluate its ability to predict DM risk.</p><p><strong>Methods: </strong>We conducted a retrospective analysis of 274 PRS patients who underwent complete surgical resection and were monitored for ≥ 36 months at two centers. Conventional radiomics (C-radiomics), ITH radiomics, and deep-learning (DL) features were extracted from the preoperative CECT scans and developed single-modality models. Clinical indicators and high-throughput CECT features were integrated to develop a combined model for predicting DM. The performance of the models was evaluated by measuring the receiver operating characteristic curve and Harrell's concordance index (C-index). Distant metastasis-free survival (DMFS) was also predicted to further assess survival benefits.</p><p><strong>Results: </strong>The ITH model demonstrated satisfactory predictive capability for DM in internal and external validation cohorts (AUC: 0.735, 0.765; C-index: 0.691, 0.729). The combined model that combined clinicoradiological variables, ITH-score, and DL-score achieved the best predictive performance in internal and external validation cohorts (AUC: 0.864, 0.801; C-index: 0.770, 0.752), successfully stratified patients into high- and low-risk groups for DM (p < 0.05).</p><p><strong>Conclusions: </strong>The combined model demonstrated promising potential for accurately predicting the DM risk and stratifying the DMFS risk in RPS patients undergoing complete surgical resection, providing a valuable tool for guiding treatment decisions and follow-up strategies.</p><p><strong>Critical relevance statement: </strong>The intratumoral heterogeneity analysis facilitates the identification of high-risk retroperitoneal sarcoma patients prone to distant metastasis and poor prognoses, enabling the selection of candidates for more aggressive surgical and post-surgical interventions.</p><p><strong>Key points: </strong>Preoperative identification of retroperitoneal sarcoma (RPS) with a high potential for distant metastasis (DM) is crucial for targeted interventional strategies. Quantitative assessment of intratumoral heterogeneity achieved reasonable performance for predicting DM. The integrated model combining clinicoradiological variables, ITH radiomics, and deep-learning features effectively predicted distant metastasis-free survival.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"99"},"PeriodicalIF":4.1,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12064543/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144014296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The interpretable CT-based vision transformer model for preoperative prediction of clear cell renal cell carcinoma SSIGN score and outcome.","authors":"Kaiyue Zhi, Yanmei Wang, Lei Yan, Feng Hou, Jie Wu, Shuo Zhang, He Zhu, Lianzi Zhao, Ning Wang, Xia Zhao, Xianjun Li, Yicong Wang, Chengcheng Chen, Nan Wang, Yuchao Xu, Guangjie Yang, Pei Nie","doi":"10.1186/s13244-025-01972-0","DOIUrl":"https://doi.org/10.1186/s13244-025-01972-0","url":null,"abstract":"<p><strong>Objectives: </strong>To develop and validate an interpretable CT-based vision transformer (ViT) model for preoperative prediction of the stage, size, grade, and necrosis (SSIGN) and outcome in clear cell renal cell carcinoma (ccRCC) patients.</p><p><strong>Methods: </strong>Eight hundred forty-five ccRCC patients from multiple centers were retrospectively enrolled. For each patient, 768 ViT features were extracted in the cortical medullary phase (CMP) and renal parenchymal phase (RPP) images, respectively. The CMP ViT model (CVM), RPP ViT model (RVM), and CMP-RPP combined ViT model (CRVM) were constructed to predict the SSIGN in ccRCC patients. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each model. Decision curve analysis (DCA) was used to evaluate the net clinical benefit. The endpoint was the progression-free survival (PFS). Kaplan-Meier survival analysis was used to assess the association between model-predicted SSIGN and PFS. The SHAP approach was applied to determine the prediction process of the CRVM.</p><p><strong>Results: </strong>The CVM, RVM, and CRVM demonstrated good performance in predicting SSIGN, with a high AUC of 0.859, 0.883, and 0.895, respectively, in the test cohort. DCA demonstrated the CRVM performed best in clinical net benefit. In predicting PFS, CRVM achieved a higher Harrell's concordance index (C-index, 0.840) than the CVM (0.719) and RVM (0.773) in the test cohort. The SHAP helped us understand the impact of ViT features on CRVM's SSIGN prediction from a global and individual perspective.</p><p><strong>Conclusion: </strong>The interpretable CT-based CRVM may serve as a non-invasive biomarker in predicting the SSIGN and outcome of ccRCC.</p><p><strong>Critical relevance statement: </strong>Our findings outline the potential of an interpretable CT-based ViT biomarker for predicting the SSIGN score and outcome of ccRCC, which might facilitate patient counseling and assist clinicians in therapy decision-making for individual cases.</p><p><strong>Key points: </strong>Current first-line imaging lacks preoperative prediction of the SSIGN score for ccRCC patients. The ViT model could predict the SSIGN score and outcome of ccRCC patients. This study can facilitate the development of personalized treatment for ccRCC patients.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"98"},"PeriodicalIF":4.1,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12064486/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143970392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}