A preoperative CT-based deep learning radiomics model in predicting the stage, size, grade and necrosis score and outcome in localized clear cell renal cell carcinoma: A multicenter study
Pei Nie, Shihe Liu, Ruizhi Zhou, Xiaoli Li, Kaiyue Zhi, Yanmei Wang, Zhengjun Dai, Lianzi Zhao, Ning Wang, Xia Zhao, Xianjun Li, Nan Cheng, Yicong Wang, Chengcheng Chen, Yuchao Xu, Guangjie Yang
Eur J Radiol . 2023 Sep;166:111018. doi: 10.1016/j.ejrad.2023.111018. Epub 2023 Jul 29.
Background and purpose: The Stage, Size, Grade and Necrosis (SSIGN) score is the most commonly used prognostic model in clear cell renal cell carcinoma (ccRCC) patients. It is a great challenge to preoperatively predict SSIGN score and outcome of ccRCC patients. The aim of this study was to develop and validate a CT-based deep learning radiomics model (DLRM) for predicting SSIGN score and outcome in localized ccRCC.
Methods: A multicenter 784 (training cohort/ test 1 cohort / test 2 cohort, 475/204/105) localized ccRCC patients were enrolled. Radiomics signature (RS), deep learning signature (DLS), and DLRM incorporating radiomics and deep learning features were developed for predicting SSIGN score. Model performance was evaluated with area under the receiver operating characteristic curve (AUC). Kaplan-Meier survival analysis was used to assess the association of the model-predicted SSIGN with cancer-specific survival (CSS). Harrell's concordance index (C-index) was calculated to assess the CSS predictive accuracy of these models.
Results: The DLRM achieved higher micro-average/macro-average AUCs (0.913/0.850, and 0.969/0.942, respectively in test 1 cohort and test 2 cohort) than the RS and DLS did for the prediction of SSIGN score. The CSS showed significant differences among the DLRM-predicted risk groups. The DLRM achieved higher C-indices (0.827 and 0.824, respectively in test 1 cohort and test 2 cohort) than the RS and DLS did in predicting CSS for localized ccRCC patients.
Conclusion: The DLRM can accurately predict the SSIGN score and outcome in localized ccRCC.