RCC-Supporter: supporting renal cell carcinoma treatment decision-making using machine learning

The population diagnosed with renal cell carcinoma, especially in Asia, represents 36.6% of global cases, with the incidence rate of renal cell carcinoma in Korea steadily increasing annually. However, treatment options for renal cell carcinoma are diverse, depending on clinical stage and histologic characteristics. Hence, this study aims to develop a machine learning based clinical decision-support system that recommends personalized treatment tailored to the individual health condition of each patient. We reviewed the real-world medical data of 1,867 participants diagnosed with renal cell carcinoma between November 2008 and June 2021 at the Pusan National University Yangsan Hospital in South Korea. Data were manually divided into a follow-up group where the patients did not undergo surgery or chemotherapy (Surveillance), a group where the patients underwent surgery (Surgery), and a group where the patients received chemotherapy before or after surgery (Chemotherapy). Feature selection was conducted to identify the significant clinical factors influencing renal cell carcinoma treatment decisions from 2,058 features. These features included subsets of 20, 50, 75, 100, and 150, as well as the complete set and an additional 50 expert-selected features. We applied representative machine learning algorithms, namely Decision Tree, Random Forest, and Gradient Boosting Machine (GBM). We analyzed the performance of three applied machine learning algorithms, among which the GBM algorithm achieved an accuracy score of 95% (95% CI, 92–98%) for the 100 and 150 feature sets. The GBM algorithm using 100 and 150 features achieved better performance than the algorithm using features selected by clinical experts (93%, 95% CI 89–97%). We developed a preliminary personalized treatment decision-support system (TDSS) called “RCC-Supporter” by applying machine learning (ML) algorithms to determine personalized treatment for the various clinical situations of RCC patients. Our results demonstrate the feasibility of using machine learning-based clinical decision support systems for treatment decisions in real clinical settings.