旨在确定动态对比增强 (DCE) MRI 放射组学在预测初治前列腺癌 (PCa) 患者的国际泌尿病理学会分级组 (ISUP-GG) 中的作用。在这项伦理审查委员会批准的回顾性研究中在 2017 年至 2020 年间的两项前瞻性临床试验中，纳入了 73 名疑似/确诊 PCa 的男性。所有参与者均接受了多参数 MRI 检查。在 MRI 上，确定了主要病变（根据 PI-RADS）。根据图像生物标志物标准化倡议 (IBSI) 指南，在 14 个时间点从分段体积中提取 DCE-MRI 放射组学特征。将认知融合靶向活检的组织病理学评估作为参考标准。进行单变量回归来评估所有计算特征的潜在预测因子。使用随机森林插补进行多变量建模。对 73 个指标病变进行了回顾。组织病理学显示ISUP-GG-阴性/1/2、ISUP-GG-3、ISUP-GG-4和ISUP-GG-5分别有28、16、13和16个病变。从提取的特征来看，总病灶增强（TLE）、最小增强强度和灰度游程矩阵（GLRLM）是 ISUP-GG 之间最显着差异的参数（Neg/1/2 vs 3/4 vs 5）。与 ISUP-GG 具有显着横截面关联的 16 个特征进入了多变量分析。最终的 DCE 分区模型仅使用四个特征（病变球形度、TLE、GLRLM 和灰级区域长度矩阵）。对于二值化诊断（ISUP-GG≤2 vs ISUP-GG>2），准确率达到 81%。DCE-MRI 放射组学可作为一种非侵入性工具，辅助初治 PCa 患者的病理分级组预测，可能会向 PI-RADS 添加补充信息，以支持定制的诊断途径和治疗计划。版权所有 © 2024 大学放射科医生协会。由爱思唯尔公司出版。保留所有权利。

To determine the role of dynamic contrast-enhanced (DCE) MRI-radiomics in predicting the International Society of Urological Pathology Grade Group (ISUP-GG) in therapy-naïve prostate cancer (PCa) patients.In this ethics review board-approved retrospective study on two prospective clinical trials between 2017 and 2020, 73 men with suspected/confirmed PCa were included. All participants underwent multiparametric MRI. On MRI, dominant lesions (per PI-RADS) were identified. DCE-MRI radiomic features were extracted from the segmented volumes following the image biomarker standardisation initiative (IBSI) guidelines through 14 time points. Histopathology evaluation on the cognitive-fusion targeted biopsies was set as the reference standard. Univariate regression was done to evaluate potential predictors across all calculated features. Random forest imputation was used for multivariate modelling.73 index lesions were reviewed. Histopathology revealed 28, 16, 13 and 16 lesions with ISUP-GG-Negative/1/2, ISUP-GG-3, ISUP-GG-4 and ISUP-GG-5, respectively. From the extracted features, total lesion enhancement (TLE), minimum enhancement intensity and Grey-Level Run Length Matrix (GLRLM) were the most significantly different parameters among ISUP-GGs (Neg/1/2 vs 3/4 vs 5). 16 features with significant cross-sectional associations with ISUP-GGs entered the multivariate analysis. The final DCE partitioning model used only four features (lesion sphericity, TLE, GLRLM and Grey-Level Zone Length Matrix). For the binarized diagnosis (ISUP-GG≤2 vs ISUP-GG>2), the accuracy reached 81%.DCE-MRI radiomics might be used as a non-invasive tool for aiding pathological grade group prediction in therapy-naïve PCa patients, potentially adding complementary information to PI-RADS for supporting tailored diagnostic pathways and treatment planning.Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.