Predicting Recurrence Following Surgical Resection for High-risk Localized Renal Cell Carcinoma: A Radiomics-Clinical Integration Approach.

Abstract

PURPOSE Adjuvant immunotherapy for clear cell renal cell carcinoma (ccRCC) is controversial due to the absence of reliable biomarkers for identifying patients most likely to benefit. This study aimed to develop and validate a quantitative radiomic signature (RS) and a radiomics-clinical model to identify patients at increased risk of recurrence following surgery among those eligible for adjuvant immunotherapy. METHODS This retrospective study included patients with ccRCC who are at intermediate-to-high or high risk of recurrence after nephrectomy. Inclusion criteria were patients with baseline characteristics matching the KEYNOTE-564 criteria. Radiomic texture-features were extracted from preoperative CT scans. Affinity-propagation clustering and random survival forest algorithms were applied to construct the RS. A radiomics-clinical-model was developed using multivariable Cox regression. The primary endpoint was disease-free survival (DFS). Model performance was assessed using time-dependent and integrated AUCs (iAUCs) and compared to conventional prognostic models via decision curve analysis (DCA). RESULTS A total of 309 patients were included, split into training (247) and test (62) sets. From each patient, 1,316 radiomic features were extracted. The RS achieved an iAUC of 0.78 in the training set and 0.72 in the test set. Multivariable analysis identified node status, vascular invasion, hemoglobin, and the RS as predictors of DFS (all p<0.05). These factors formed the radiomics-clinical-model, which achieved an iAUC of 0.81(95%CI,0.76-0.85) in the training set and 0.78(95%CI,0.69-0.88) in the test set. DCA demonstrated its superior clinical utility compared to conventional prognostic models. CONCLUSIONS Integrating radiomics with clinical factors improves DFS prediction in intermediate-to-high or high risk ccRCC. This model offers a tool for individualized risk assessment, potentially optimizing patient selection for adjuvant therapy.