Title
Machine-Learning Models For Multicenter Prostate Cancer Treatment Plans
Abstract
Clinical factors, including T-stage, Gleason score, and baseline prostate-specific antigen, are used to stratify patients with prostate cancer (PCa) into risk groups. This provides prognostic information for a heterogeneous disease such as PCa and guides treatment selection. In this article, we hypothesize that nonclinical factors may also impact treatment selection and their adherence to treatment guidelines. A total of 552 patients with intermediate- and high-risk PCa treated with definitive radiation with or without androgen deprivation therapy (ADT) between 2010 and 2017 were identified from 34 medical centers within the Veterans Health Administration. Medical charts were manually reviewed, and details regarding each patient's clinical history and treatment were extracted. Support Vector Machine and Random forest-based classification was used to identify clinical and nonclinical predictors of adherence to the treatment guidelines from the National Comprehensive Cancer Network (NCCN). We created models for predicting both initial treatment intent and treatment alterations. Our results demonstrate that besides clinical factors, the center in which the patient was treated (nonclinical factor) played a significant role in adherence to NCCN guidelines. Furthermore, the treatment center served as an important predictor to decide on whether or not to prescribe ADT; however, it was not associated with ADT duration and weakly associated with treatment alterations. Such center-bias motivates further investigation on details of center-specific barriers to both NCCN guideline adherence and on oncological outcomes. In addition, we demonstrate that publicly available data sets, for example, that from Surveillance, Epidemiology, and End Results (SEERs), may not be well equipped to build such predictive models on treatment plans.
Year
DOI
Venue
2021
10.1089/cmb.2020.0188
JOURNAL OF COMPUTATIONAL BIOLOGY
Keywords
DocType
Volume
androgen deprivation therapy, localized prostate cancer, radiation therapy, random forests, SEER, SVM
Journal
28
Issue
ISSN
Citations 
2
1066-5277
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Khajamoinuddin Syed101.01
William Sleeman200.34
Payal Soni300.34
Michael Hagan401.35
Jatinder Palta501.69
Rishabh Kapoor601.69
Preetam Ghosh734943.69