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An MRI-based grading system for preoperative risk estimation of positive surgical margin after radical prostatectomy
Lili Xu1,2, Gumuyang Zhang1, Daming Zhang1
1Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, No.1 Shuaifuyuan, Wangfujing Street, Dongcheng District, Beijing, 100730, China.
Objective:
To construct a simplified grading system based on MRI features to predict positive surgical margin (PSM) after radical prostatectomy (RP).
Methods:
Patients 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.
Results:
There 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.
Conclusion:
The 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.
Insights
This study developed simplified MRI grading systems (mrPSM1 and mrPSM2) to predict positive surgical margins (PSM) after prostatectomy. These systems, using features like capsule contact length and extraprostatic extension, offer high specificity for risk stratification.
Area of Science:
- Radiology
- Oncology
- Urology
Background:
- Predicting positive surgical margin (PSM) status after radical prostatectomy (RP) is crucial for prostate cancer management.
- Current prediction methods may lack simplicity or sufficient specificity.
Purpose of the Study:
- To develop and validate simplified MRI-based grading systems for predicting PSM after RP.
- To assess the performance of these systems in terms of accuracy and specificity.
Main Methods:
- Retrospective enrollment of patients undergoing prostate MRI and RP into derivation (2017-2021) and validation (2021-2022) cohorts.
- Evaluation of MRI features including capsule contact length (CCL), extraprostatic extension (EPE), and apex abutting by a single radiologist.
- Development of two grading systems (mrPSM1 and mrPSM2) using logistic regression and decision tree analysis; performance assessed by AUC, sensitivity, and specificity.
Main Results:
- PSM rates were 29.8% in the derivation and 36.4% in the validation cohorts.
- mrPSM1 (CCL ≥ 20 mm, apex abutting) and mrPSM2 (including EPE) showed AUCs of 0.705/0.713 (derivation) and 0.672-0.686 (validation).
- The proposed systems demonstrated significantly higher specificity (0.732-0.750) compared to a previous model (0.411) in the validation cohort, with good interreader agreement (kappa=0.764-0.776).
Conclusions:
- Simplified MRI-based grading systems (mrPSM1, mrPSM2) can effectively predict PSM after RP.
- These systems offer high specificity, aiding in preoperative risk stratification.
- The mrPSM systems are easily interpretable and may improve prostate cancer management decisions.
