Evaluating trends in treatment and outcomes of stage I renal cell carcinoma with machine learning: The RECLAIM-L (REnal cancer landscape and artificial intelligence with machine learning) database

Abstract

Treatment selection for localized renal cell carcinoma (RCC) has progressed significantly in recent years. We aim to describe clinicopathological characteristics and treatment outcomes of clinical stage I RCC patients, and construct machine learning (ML) models for personalized prediction of survival outcomes at various epochs pre- and post-operatively. This is a single-center retrospective study of patients treated for clinical stage I RCC from July 1990 to December 2023. Exclusion criteria included patients with stage II-IV RCC at diagnosis, patients who were not treated (e.g. active surveillance, basic supportive care), and incomplete data. ML models using extreme gradient boosting were created to predict overall survival (OS), recurrence-free survival (RFS), cancer-specific survival (CSS), and postoperative complications in clinical stage I RCC, with evaluation via concordance-index (C-index) for survival and accuracy metrics for complications. We analyzed 1511 patients. Survival outcomes are in line with contemporary data. Using propensity score matching, radical and partial nephrectomy demonstrated similar perioperative and survival outcomes for clinical stage I tumors. C-indexes for ML model predictions were 0.722 for preoperative OS, 0.745 for postoperative OS, 0.682 for preoperative RFS, 0.763 for preoperative CSS, and 0.847 for postoperative CSS. For postoperative complications, sensitivity was 79.0% and positive predictive value was 75.4%. Drawing data from three decades of RCC management in a tertiary center, ML models offer reasonable predictive power, which can aid preoperative counselling, shared decision-making, and management of patient expectations before and after surgical treatment.