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

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

ObjectiveThis study investigates the risk factors associated with rectal positive surgical margins (RPSM) following radical prostatectomy and aims to develop a predictive model.MethodsClinical data from 198 patients undergoing radical prostatectomy at the Department of Urology, Kunshan Hospital of T...

Full description

Saved in:
Bibliographic Details
Main Authors: Yufan Wu, Fei Liu, Shiyu Ma, Guodong Jing, Qiwei Yu, Linya Yao, Chengwei Shao, Weiguo Chen, Xingbo Wang
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-04-01
Series:Frontiers in Surgery
Subjects:
prostate cancer
radical prostatectomy
rectal positive surgical margins
predictive model
PI-RADS
Online Access:https://www.frontiersin.org/articles/10.3389/fsurg.2025.1563344/full
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:ObjectiveThis study investigates the risk factors associated with rectal positive surgical margins (RPSM) following radical prostatectomy and aims to develop a predictive model.MethodsClinical 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.ResultsMultivariate 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).ConclusionThe predictive model effectively forecasts the likelihood of RPSM following radical prostatectomy, offering valuable insights for personalized patient management.
ISSN:2296-875X

Similar Items