Machine learning prediction of overall survival in patients with cT1b renal cell carcinoma after surgical resection using the SEER database.

ABSTRACT: Accurate survival prediction is essential for guiding follow-up strategies in patients with cT1b renal cell carcinoma (RCC). Traditional AJCC TNM staging systems provide limited prognostic accuracy. Data from the SEER database were used, which included 22,426 patients with cT1b RCC who underwent surgical resection. The data were randomized into training and validation sets in a 7:3 ratioen, suring comparability using standardized mean differences (SMD < 0.1). A random survival forest (RSF) model was developed and compared with support vector machine (SVM) and extreme gradient boosting accelerated failure time (XGB-AFT) models. Model performance was assessed using AUC, sensitivity, specificity, and calibration, with 1000 bootstrap resamples. Shapley additive explanation (SHAP) values were c

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC12583456 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature