A Machine Learning Approach to Predict the Probability of Brain Metastasis in Renal Cell Carcinoma Patients

Abstract

Patients with brain metastasis (BM) have a better prognosis when it is detected early. However, current guidelines recommend brain imaging only when there are central nervous system symptoms or abnormal experimental values. Therefore, metastases are discovered later in asymptomatic patients. As a result, there is a need for an algorithm that predicts the possibility of BM using clinical data and machine learning (ML). Data from 3153 patients with renal cell carcinoma (RCC) were collected from the 11-institution Korean Renal Cancer Study group (KRoCS) database. To predict BM, clinical information of 1282 patients was extracted from the database and used to compare the performance of six ML algorithms. The final model selection was based on the area under the receiver operating characteristic (AUROC) curve. After optimizing the hyperparameters for each model, the adaptive boosting (AdaBoost) model outperformed the others, with an AUROC of 0.716. We developed an algorithm to predict the probability of BM in patients with RCC. Using the developed predictive model, it is possible to avoid detection delays by performing computed tomography scans on potentially asymptomatic patients.