Xing Hui, Abudureheman Yibanu, Ai Xueru, Wang Yunling, Xu Jingxu, Huang Chencui, Kelimu Gulimire, Li Ting
The Third People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China.
The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Transl Androl Urol. 2025 Aug 30;14(8):2269-2278. doi: 10.21037/tau-2025-209. Epub 2025 Aug 25.
Given the significant economic burden of prostate cancer (PCa), its diagnostic methods need to be improved. The limitations of the subjective Prostate Imaging Reporting and Data System (PI-RADS) underscore the need for generalizable radiomics in clinically significant PCa (csPCa). This study aimed to build a machine-learning model based on biparametric magnetic resonance imaging (bpMRI) to diagnose csPCa.
Prognostic model development: This study retrospectively included the data of 445 patients from two centers, of whom 206 had csPCa and 239 had clinically non-significant PCa (ncsPCa). The training set comprised 120 csPCa patients and 141 ncsPCa patients. The test set comprised 52 csPCa patients and 61 ncsPCa patients. The external validation comprised 34 csPCa patients and 37 ncsPCa patients.
Features were extracted from T2-weighted imaging (T2WI) sequences and apparent diffusion coefficient (ADC) maps based on bpMRI radiomics. From 3662 radiomics features, 10 stable radiomics features were selected for model construction based on intraclass correlation coefficients (ICCs). Three diagnostic models for csPCa were constructed. The area under the curve (AUC) values for the PI-RADS-scoring model, which was based on visual assessments by radiologists, were 0.8271, 0.7905, and 0.8331 in the training, test, and external validation sets, respectively; while those for the clinical scoring model were 0.9236, 0.8846, and 0.8378, respectively; and those for the radiomics model were 0.9790, 0.9584, and 0.9523, respectively. There were significant differences between the radiomics model and the PI-RADS-scoring model (P0.001) in both the training and test sets. The P value for the radiomics model and clinical scoring model in the training set was <0.001, while that in the validation set was 0.056. Overall, the AUC values for the three models indicated that the diagnostic performance of the bpMRI radiomics model, which was based on T2WI sequences and ADC images, for csPCa was better than that of both the PI-RADS-scoring and clinical scoring models.
The radiomics model can reliably detect and classify csPCa, and is a very powerful non-invasive auxiliary tool that could be used as an alternative method for diagnosing csPCa in personalized medicine.
鉴于前列腺癌（PCa）带来的巨大经济负担，其诊断方法需要改进。主观的前列腺影像报告和数据系统（PI-RADS）存在局限性，这凸显了在临床显著前列腺癌（csPCa）中应用可推广的放射组学的必要性。本研究旨在构建基于双参数磁共振成像（bpMRI）的机器学习模型来诊断csPCa。
预后模型开发：本研究回顾性纳入了来自两个中心的445例患者的数据，其中206例为csPCa，239例为临床非显著前列腺癌（ncsPCa）。训练集包括120例csPCa患者和141例ncsPCa患者。测试集包括52例csPCa患者和61例ncsPCa患者。外部验证包括34例csPCa患者和37例ncsPCa患者。
基于bpMRI放射组学从T2加权成像（T2WI）序列和表观扩散系数（ADC）图中提取特征。从3662个放射组学特征中，基于组内相关系数（ICC）选择了10个稳定的放射组学特征用于模型构建。构建了三种csPCa诊断模型。基于放射科医生视觉评估的PI-RADS评分模型在训练集、测试集和外部验证集中的曲线下面积（AUC）值分别为0.8271、0.7905和0.8331；而临床评分模型的AUC值分别为0.9236、0.8846和0.8378；放射组学模型的AUC值分别为0.9790、0.9584和0.9523。在训练集和测试集中，放射组学模型与PI-RADS评分模型之间存在显著差异（P<0.001）。训练集中放射组学模型与临床评分模型的P值<0.001，而验证集中的P值为0.056。总体而言，三种模型的AUC值表明，基于T2WI序列和ADC图像的bpMRI放射组学模型对csPCa的诊断性能优于PI-RADS评分模型和临床评分模型。
放射组学模型能够可靠地检测和分类csPCa，是一种非常强大的非侵入性辅助工具，可作为个性化医疗中诊断csPCa的替代方法。
