Operations research models and methods in the screening, detection, and treatment of prostate cancer: A categorized, annotated review

According to data from the Surveillance, Epidemiology, and End Results (SEER) program in the United States, approximately 15% of men will be diagnosed with prostate cancer during their lifetimes. Over the past 15 years, the battle against prostate cancer has been joined by researchers and practitioners who have used a wide variety of operations research (OR) models and methods to investigate decisions involving screening, detection, and treatment of prostate cancer. We provide a narrative review of articles falling into the following five categories: decision analysis, machine learning, optimization, simulation, and statistics. We identified a total of 523 archival journal articles describing the use of methods in these categories in the context of prostate cancer since 2000. We categorize and annotate 49 of these articles in order to provide representative examples of the use of OR models and methods in each of these areas. We conclude with a summary of the trends in research using OR methods in the context of prostate cancer over the past 15 years, and a discussion about how these trends will influence future research.