Preoperative MRI-based estimation of risk for positive resection margin after radical prostatectomy in patients with prostate cancer: development and validation of a simple scoring system

Objectives To develop a simplified MRI-based model to predict the risk for positive surgical margins (PSMs) after radical prostatectomy (RP) in patients with prostate cancer (PCa). Methods Consecutive patients who underwent RP for PCa were retrospectively identified from a tertiary referral hospital. Patients who underwent RP between January 2014 and June 2014 were assigned as derivation cohort (n = 330) and those between January 2018 and February 2018 were assigned as validation cohort (n = 100). MRI-based predictors associated with PSM were assessed: tumor size, tumor-capsule contact length, the Prostate Imaging Reporting and Data System (PI-RADS) category, tumor location (tumor contact to the apex or posterolateral side near the neurovascular bundle), apical depth, and prostate volume. A prediction model was developed by using multivariable logistic regression, and then it was transformed into a scoring system. The prediction and calibration performance of this scoring system was evaluated using the C statistics and Hosmer-Lemeshow goodness-of-fit test. Results A total of 121 (36.7%) and 32 (32.0%) of patients in the derivation and validation cohorts had PSMs after RP. The scoring system consisted of the following variables: tumor-capsule contact length, PI-RADS category, tumor located at the apex and/or posterolateral side. This scoring system provided good prediction performance for PSM in the derivation (C statistics, 0.80 [95% CI: 0.76, 0.85]) and validation (C statistics, 0.77 [95% CI: 0.68, 0.87]) cohorts, and also showed good calibration in both cohorts (p = 0.83 and 0.86, respectively). Conclusions An MRI-based scoring system can help estimate the risk of PSM after RP.

Automated summary

A simplified MRI-based model was developed to predict the risk of positive surgical margins after radical prostatectomy in patients with prostate cancer. The model showed good prediction performance and calibration using tumor characteristics on MRI images. This scoring system can help estimate the risk of positive surgical margins after radical prostatectomy.