ArticleInsights into imaging2023
An MRI-based grading system for preoperative risk estimation of positive surgical margin after radical prostatectomy.
Article in Insights into imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- 10 mm (PI-RADS v2.1) versus 15 mm (PI-RADS v1.0) tumor capsule contact length in predicting extracapsular extension in prostate cancer: Meta-analysis and systematic review.Abdominal radiology (New York) · 2025Pooled it
- An interpretable nomogram for positive surgical margin risk after laparoscopic and robot-assisted radical prostatectomy: a single-centre development and internal validation study.Journal of robotic surgery · 2026Article
- Preoperative prediction of positive surgical margins in prostate cancer using multimodal deep learning model: a multicenter study.Abdominal radiology (New York) · 2026Article
- Imaging-based clinical decision tree enables risk stratification of extraprostatic extension before radical prostatectomy in prostate cancer patients.Insights into imaging · 2026Article
- Article
- Contemporary Evidence for Optimization of Robotic Radical Prostatectomy Outcomes Using Advanced Imaging Techniques.Journal of clinical medicine · 2026Review
- Preoperative MRI-based predictive model for biochemical recurrence following radical prostatectomy.Abdominal radiology (New York) · 2025Article
- Lesion-based grading system using clinicopathological and MRI features for predicting positive surgical margins in prostate cancer.Abdominal radiology (New York) · 2025Article
- Development of preoperative nomograms to predict the risk of overall and multifocal positive surgical margin after radical prostatectomy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
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Authors and funding
12 authors at 1 institution in 2 countries.
Funding
Abstract
objectiveTo construct a simplified grading system based on MRI features to predict positive surgical margin (PSM) after radical prostatectomy (RP).
methodsPatients who had undergone prostate MRI followed by RP between January 2017 and January 2021 were retrospectively enrolled as the derivation group, and those between February 2021 and November 2022 were enrolled as the validation group. One radiologist evaluated tumor-related MRI features, including the capsule contact length (CCL) of lesions, frank extraprostatic extension (EPE), apex abutting, etc. Binary logistic regression and decision tree analysis were used to select risk features for PSM. The area under the curve (AUC), sensitivity, and specificity of different systems were calculated. The interreader agreement of the scoring systems was evaluated using the kappa statistic.
resultsThere were 29.8% (42/141) and 36.4% (32/88) of patients who had PSM in the derivation and validation cohorts, respectively. The first grading system was proposed (mrPSM1) using two imaging features, namely, CCL ≥ 20 mm and apex abutting, and then updated by adding frank EPE (mrPSM2). In the derivation group, the AUC was 0.705 for mrPSM1 and 0.713 for mrPSM2. In the validation group, our grading systems showed comparable AUC with Park et al.'s model (0.672-0.686 vs. 0.646, p > 0.05) and significantly higher specificity (0.732-0.750 vs. 0.411, p < 0.001). The kappa value was 0.764 for mrPSM1 and 0.776 for mrPSM2. Decision curve analysis showed a higher net benefit for mrPSM2.
conclusionThe proposed grading systems based on MRI could benefit the risk stratification of PSM and are easily interpretable. CRITICAL RELEVANCE STATEMENT: The proposed mrPSM grading systems for preoperative prediction of surgical margin status after radical prostatectomy are simplified compared to a previous model and show high specificity for identifying the risk of positive surgical margin, which might benefit the management of prostate cancer. KEY POINTS: • CCL ≥ 20 mm, apex abutting, and EPE were important MRI features for PSM. • Our proposed MRI-based grading systems showed the possibility to predict PSM with high specificity. • The MRI-based grading systems might facilitate a structured risk evaluation of PSM.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.