OR Practice-Data Analytics for Optimal Detection of Metastatic Prostate Cancer

• 成果类型: Article
• 署名作者: Merdan, Selin; Barnett, Christine L.; Denton, Brian T.; Montie, James E.; Miller, David C.
• 署名单位: University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2020.2020
• 发表日期: 2021
• 页码: 774-794
• 关键词: skeletal-related events; external validation; logistic-regression; predictive models; class imbalance; RISK; radiation; machine; burden; regularization

摘要

We used data-analytics approaches to develop, calibrate, and validate predictive models, to help urologists in a large statewide collaborative make prostate cancer staging decisions on the basis of individual patient risk factors. The models were validated using statistical methods based on bootstrapping and evaluation on out-of-sample data. These models were used to design guidelines that optimally weigh the benefits and harms of radiological imaging for the detection of metastatic prostate cancer. The Michigan Urological Surgery Improvement Collaborative, a statewide medical collaborative, implemented these guidelines, which were predicted to reduce unnecessary imaging by more than 40% and limit the percentage of patients with missed metastatic disease to be less than 1%. The effects of the guidelines were measured after implementation to confirm their impact on reducing unnecessary imaging across the state of Michigan.