Establishment and validation of a model for predicting the SSIGN score and prognosis of patients with clear cell renal cell carcinoma based on CT radiomic features and clinical indices - ZHI Kaiyue, YANG Zhitao, ZHANG Shuo, ZHU He, WANG Yanmei, ZHAO Lianzi, WANG Ning, ZHAO Xia, LI Xianjun, CHENG Nan, WANG Yicong, CHEN Chengcheng, WANG Nan, NIE Pei (精准医学杂志)
Objective To establish and validate a model for predicting the SSIGN score and prognosis of patients with clear cell renal cell carcinoma (ccRCC) based on CT radiomic features and clinical indices. Methods A total of 845 patients with pathologically confirmed ccRCC from June 2013 to July 2022 were enrolled, with 592 patients in the training set and 253 in the test set, and according to the SSIGN score, the patients were divided into intermediate-to-high recurrence risk group (≥4 points) and low recurrence risk group (0-3 points). The clinical indices and CT radiomic features of ccRCC were collected from all patients, and univariate and multivariate logistic regression analyses were used to identify the influencing factors for SSIGN score. A predictive model for SSIGN score was established based on CT radiomic features and clinical indices. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) were used to evaluate the performance of the model in both the training set and the test set. The decision curve analysis was used to assess the net clinical benefit of the model in the test set. The Kaplan-Meier (K-M) survival analysis was used to assess the correlation of SSIGN score predicted by the model with recurrence-free survival (RFS) in both the training set and the test set, and index of concordance (C-index) and hazard ratio (HR) were used to eva-luate the performance of the model in predicting RFS. Results In the training set, this model had an AUC of 0.93 in predicting SSIGN score in ccRCC patients, while it had an AUC of 0.92 in the test set. The calibration curves showed that the combined model had high calibration ability for predicting SSIGN score in both the training set and the test set, and the DCA results showed that the combined model had a relatively high net clinical benefit in the test set. The K-M survival curve analysis showed a significant difference in RFS between the intermediate-to-high recurrence risk group and the low recurrence risk group based on SSIGN score predicted by the combined model (χ2=104.718,82.229,P<0.001). The mo-del had a C-index of 0.76 and an HR of 40.51 in the training set, as well as an C-index of 0.77 and an HR of 185.27 in the training set. Conclusion The combined model based on CT radiomic features and clinical indices shows relatively high predictive efficacy for the SSIGN score and prognosis of ccRCC patients. As a novel noninvasive assessment tool, this model has a certain potential for clinical application in the prognostic evaluation of ccRCC patients.