Zhang Jiahui, Xu Lili, Zhang Gumuyang, Zhang Daming, Zhang Xiaoxiao, Bai Xin, Chen Li, Peng Qianyu, Jin Zhengyu, Sun Hao
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, Beijing, China.
National Center for Quality Control of Radiology, Beijing, China.
Korean J Radiol. 2025 May;26(5):422-434. doi: 10.3348/kjr.2024.1008. Epub 2025 Mar 21.
This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy.
Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP.
The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PI-RADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone ( < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone ( < 0.001).
Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.
本研究调查了多参数磁共振成像（mpMRI）在预测根治性前列腺切除术（RP）中Gleason评分（GS）升级和降级方面相对于术前活检的价值。
回顾性收集2015年1月至2021年12月期间219例前列腺疾病患者的临床和mpMRI数据。所有患者均接受了系统性前列腺活检，随后进行了RP。mpMRI包括传统扩散加权成像和动态对比增强成像。进行多变量逻辑回归分析以分析与RP后GS升级和降级相关的因素。采用受试者工作特征曲线分析来估计曲线下面积（AUC），以表明多变量逻辑回归模型在预测RP后GS升级和降级方面的性能。
RP后GS升级、降级和不变的患者分别为92例、43例和84例。预测RP后GS升级的临床模型（阳性活检核心百分比[PBCs]、活检至RP的时间）和mpMRI模型（前列腺癌[PCa]位置、前列腺影像报告和数据系统[PI-RADS]v2.1评分）的AUC分别为0.714和0.749。联合诊断模型（年龄、PBCs百分比、总前列腺特异抗原[tPSA]、PCa位置和PI-RADS v2.1评分）的AUC为0.816，大于仅临床因素的AUC（<0.001）。预测RP后GS降级的临床模型（年龄、PBCs百分比、游离/总前列腺特异抗原[F/T]比值）和mpMRI模型（PCa直径、PCa位置和PI-RADS v2.1评分）的AUC分别为0.749和0.835。联合诊断模型（年龄、PBCs百分比、F/T、PCa直径、PCa位置和PI-RADS v2.1评分）的AUC为0.883，大于仅临床因素的AUC（<0.001）。
与仅使用临床因素相比，结合临床因素和mpMRI结果能够更准确地预测RP后GS的升级和降级。
