Enhanced precision in prostate surgery: determining key factors for rectal positive surgical margins through integrated imaging and clinical data analysis

TL;DR

This study identifies key factors that predict rectal positive surgical margins after prostate surgery, helping improve surgical precision and patient outcomes.

Contribution

The paper introduces a predictive model combining clinical and imaging data to forecast rectal positive surgical margins in prostate cancer patients.

Findings

Clinical stage, PSA level, Gleason score, and PI-RADS were significant predictors of RPSM.

The developed nomogram achieved a C-index of 0.833 and an AUC of 0.755, showing good predictive performance.

The model provides a tool for personalized patient management in prostate surgery.

Abstract

This study investigates the risk factors associated with rectal positive surgical margins (RPSM) following radical prostatectomy and aims to develop a predictive model.

Clinical data from 198 patients undergoing radical prostatectomy at the Department of Urology, Kunshan Hospital of Traditional Chinese Medicine from June 2022 to June 2024 were analyzed. Patients were categorized into groups with and without RPSM. Univariate and multivariate logistic regression analyses identified independent predictors of RPSM. Utilizing R software, we generated a column chart illustrating prostate cancer's RPSM incidence and constructed ROC curves with the area under the curve (AUC) to assess the discriminative performance and calibration of our model.

Multivariate logistic regression identified clinical stage, PSA level, Gleason score, bilateral prostate infiltration, and PI-RADS as significant predictors of RPSM (all P < 0.05). Using these predictors, we developed a nomogram that achieved a C-index of 0.833(95% CI: 0.785–0.887) and an AUC of 0.755 (95% CI: 0.645–0.866).

The predictive model effectively forecasts the likelihood of RPSM following radical prostatectomy, offering valuable insights for personalized patient management.

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