Using Decision Tree to Predict Cancer-Specific Mortality in Patients with Clear Cell Renal Cancer Treated with Nephrectomy

Background/Objectives: Accurate prognostic stratification after nephrectomy for clear cell renal carcinoma (ccRCC) remains challenging. Traditional models often lack the intuitive clinical application or the ability to handle non-linear interactions between variables. We aimed to develop and internally validate a decision tree-based model to predict cancer-specific survival in patients with ccRCC following nephrectomy. Methods: We analyzed 79,526 patients with ccRCC who underwent nephrectomy from the SEER database (2012–2018). Patients were randomized into development (2/3) and validation (1/3) cohorts. A conditional inference tree was constructed to predict cancer-specific survival. Multiple imputation by chained equations was used to handle missing data. Discriminatory ability was assessed using the C-index. Net clinical benefit was evaluated with decision curve analysis. The model was evaluated using CHARMS and PROBAST. Results: A decision tree with 15 risk groups is presented, further classified into high-, intermediate-, and low-risk categories according to observed median survival. The final predictors were tumor localization, tumor grade, TNM stage, age, and sarcomatoid differentiation. The model demonstrated excellent discriminatory performance, with a C-index of 0.846 (95% CI: 0.834–0.847). PROBAST assessment showed low risk of bias and low concern regarding applicability. Conclusions: The use of decision trees provides an interpretable alternative to conventional regression-based models. Three main risk categories and 15 subgroups are proposed based on tumor localization, tumor grade, TNM stage, age, and sarcomatoid differentiation. Our model demonstrates good applicability and a low risk of bias according to PROBAST guidelines; however, external validation in independent cohorts is required prior to clinical implementation.