OR Practice–Data Analytics for Optimal Detection of Metastatic Prostate Cancer
研究用数据分析方法开发预测模型，帮助泌尿科医生基于患者风险因素做前列腺癌分期决策，优化影像检查指南，减少不必要的影像检查40%以上，同时将漏诊转移性病例控制在1%以下。
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.