Application of Machine Learning for Predicting Progression-Free and Overall Survival in Patients With Renal Cell Carcinoma.

Abstract

Patient outcomes in advanced renal cell carcinoma (RCC) remain poor, with five-year survival rates ranging from ~10% to 30%. Early projections of therapeutic outcomes could optimize precision medicine and accelerate drug development. While machine learning (ML) models integrating tumor growth inhibition (TGI) metrics have improved survival predictions over traditional models, their application in RCC remains unexplored. Herein, we used TGI metrics and baseline data to evaluate parametric (PM) and semi-parametric (SPM) survival models alongside ML approaches for predicting progression-free (PFS) and overall survival (OS) in 1839 RCC patients from four trials (evaluating sunitinib, axitinib, sorafenib, interferon-alpha, and avelumab + axitinib). Data were split into training (70%) and testing (30%), and feature selection was used to determine parsimonious and robust models. Bootstrap resampling (n = 100) was employed for models' validation, and performance was assessed using C-index and Integrated Brier Score. In brief, training data results demonstrated that tree-based ML models (random survival forest (RSF) and XGBoost) outperformed PM and SPM models in predicting PFS (C-index: 0.783-0.785 vs. 0.725-0.738 for PM and SPM; p < 0.05) and OS (C-index: 0.77-0.867 vs. 0.750-0.758 for PM and SPM; p < 0.05), with RSF achieving better prediction of PFS and OS using only 3-5 covariates, compared to 9-35 with other tested methods. Tree-based methods were also superior in the testing data. SHapley Additive exPlanations revealed nonlinear relationships among top predictors, including TGI metrics, underscoring the ability of tree-based methods to capture complex prognostic interactions. Further validation is required to confirm models' generalizability to additional therapies and patients with differing tumor severity.