Skip to main content
Article
Predicting Discontinuation of Docetaxel Treatment for Metastatic Castration-Resistant Prostate Cancer (mCRPC) With Random Forest
F1000Research
  • Daniel Kristiyanto
  • Kevin E. Anderson
  • Ling-Hong Hung
  • Ka Yee Yeung, University of Washington Tacoma
Publication Date
11-16-2016
Document Type
Article
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.

DOI
10.12688/f1000research.8353.1
Version
open access
Citation Information
Daniel Kristiyanto, Kevin E. Anderson, Ling-Hong Hung and Ka Yee Yeung. "Predicting Discontinuation of Docetaxel Treatment for Metastatic Castration-Resistant Prostate Cancer (mCRPC) With Random Forest" F1000Research Vol. 5 (2016)
Available at: http://works.bepress.com/ky-yeung/18/