Predicting discontinuation of docetaxel treatment for metastatic castration-resistant prostate cancer (mCRPC) with random forest

Abstract

Prostate cancer is the most common cancer among men in developed countries. Androgen deprivation therapy (ADT) is the standard treatment for prostate cancer. However, approximately one third of all patients with metastatic disease treated with ADT develop resistance to ADT. This condition is called metastatic castrate-resistant prostate cancer (mCRPC). Patients who do not respond to hormone therapy are often treated with a chemotherapy drug called docetaxel. Sub-challenge 2 of the Prostate Cancer DREAM Challenge aims to improve the prediction of whether a patient with mCRPC would discontinue docetaxel treatment due to adverse effects. Specifically, a dataset containing three distinct clinical studies of patients with mCRPC treated with docetaxel was provided. We applied the k-nearest neighbor method for missing data imputation, the hill climbing algorithm and random forest importance for feature selection, and the random forest algorithm for classification. We also empirically studied the performance of many classification algorithms, including support vector machines and neural networks. Additionally, we found using random forest importance for feature selection provided slightly better results than the more computationally expensive method of hill climbing.

Treatment Stratification of Patients with Metastatic Castration-Resistant Prostate Cancer by Machine Learning

“…Prostate cancer is the most common cancer in men and the second leading cause of cancer-related mortality in the Western world (Gupta et al, 2014). Androgen deprivation therapy (ADT) is widely used to treat prostate cancer, but one-third of the patients will develop resistance to ADT, which is called castrationresistant prostate cancer (CRPC) (Kristiyanto et al, 2016). In general, patients with prostate cancer progress into CRPC in 18-48 months, and most deaths result from metastatic CRPC (mCRPC) with a median survival time of fewer than 2 years (Cookson et al, 2015).…”