The interpretable CT-based vision transformer model for preoperative prediction of clear cell renal cell carcinoma SSIGN score and outcome

作者：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 · 发表于：Insights into Imaging · 年份：2025 · DOI：10.1186/s13244-025-01972-0 · 被引用次数：6 · 研究领域：Renal cell carcinoma treatment、Radiomics and Machine Learning in Medical Imaging、Advanced X-ray and CT Imaging

OBJECTIVES: 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. METHODS: 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. RESULTS: 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 pre...