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
Khi Yung Fong, Valerie Huei Li Gan, Edwin Jonathan Aslim, Kae Jack Tay, Kenneth Chen, Yu Guang Tan, Alvin Yuanming Lee, Jenna Yin Ting Loh, Shao Xian Chew, Christopher Wai Sam Cheng, John Shyi Peng Yuen, Henry Sun Sien Ho, Ee Jean LimBackground
Treatment selection for localized renal cell carcinoma (RCC) has progressed significantly in recent years.
Objectives
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.
Design
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.
Methods
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.
Results
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%.
Conclusion
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.