An MRI-based grading system for preoperative risk estimation of positive surgical margin after radical prostatectomy

Insights into Imaging

Original Article

Hao Wang¹  Yu Xiao¹  Xiaoxiao Zhang²  Qianyu Peng²  Jiahui Zhang²  Gumuyang Zhang²  Li Chen²  Daming Zhang²  Xin Bai²  Zhengyu Jin³  Lili Xu³  Hao Sun³ 

[1] Department of Pathology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, No.1 Shuaifuyuan, Wangfujing Street, Dongcheng District, 100730, Beijing, China;Department 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, 100730, Beijing, China;Department 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, 100730, Beijing, China;National Center for Quality Control of Radiology, No.1 Shuaifuyuan, Wangfujing Street, Dongcheng District, 100730, Beijing, China;

关键词: Prostatic neoplasms;    Magnetic resonance imaging;    Margins of excision;    Risk assessment;   

DOI  :  10.1186/s13244-023-01516-4

 received in 2023-06-05, accepted in 2023-09-03,  发布年份 2023

来源: Springer

【 摘 要 】

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 statementThe 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.Graphical Abstract

【 授权许可】

CC BY   
© European Society of Radiology (ESR) 2023

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