Using support vector machine analysis to assess PartinMR: A new prediction model for organ‐confined prostate cancer

Background Partin tables represent the most widely used predictive tool for prostate cancer stage at prostatectomy but with potential limitations. Purpose To develop a new PartinMR model for organ‐confined prostate cancer (OCPCA) by incorporating Partin table and mp‐MRI with a support vector machine (SVM) analysis. Study Type Retrospective. Population In all, 541 patients with biopsy‐confirmed prostate cancer underwent mp‐MRI. Field Strength T2‐weighted, diffusion‐weighted imaging with a 3.0T MR scanner. Assessment Candidate predictors included age, prostate‐specific antigen, clinical stage, biopsy Gleason score (GS), and mp‐MRI findings, ie, tumor location, Prostate Imaging and Reporting and Data System (PI‐RADS) score, diameter (D‐max), and 6‐point MR stage. The PartinMR model with combination of a Partin table and mp‐MRI findings was developed using SVM and 5‐fold crossvalidation analysis. Statistical Tests The predicted ability of the PartinMR model was compared with a standard Partin and a modified Partin table (mPartin) which used for mp‐MRI staging. Statistical tests were made by area under receiver operating characteristic curve (AUC), adjusted proportional hazard ratio (HR), and a cost‐effective benefit analysis. Results The rate of OCPCA at prostatectomy was 46.4% (251/541). Using MR staging, mPartin table (AUC, 0.814, 95% confidence interval [CI]: 0.779–0.846, P = 0.001) is appreciably better than the Partin table (AUC, 0.730, 95% CI: 0.690–0.767). Contrarily, adding all MR variables, the PartinMR model (AUC, 0.891, 95% CI: 0.884–0.899, P