Nakai Hirotsugu, Takahashi Hiroaki, LeGout Jordan D, Kawashima Akira, Froemming Adam T, Klug Jason R, Korfiatis Panagiotis, Lomas Derek J, Humphreys Mitchell R, Dora Chandler, Takahashi Naoki
Department of Radiology, Mayo Clinic, Rochester, MN.
Department of Radiology, Mayo Clinic, Jacksonville, FL.
J Comput Assist Tomogr. 2025;49(2):247-257. doi: 10.1097/RCT.0000000000001679. Epub 2024 Nov 13.
The aims of the study are to develop a prostate cancer risk prediction model that combines clinical and magnetic resonance imaging (MRI)-related findings and to assess the impact of adding Prostate Imaging-Reporting and Data System (PI-RADS) ≥3 lesions-level findings on its diagnostic performance.
This 3-center retrospective study included prostate MRI examinations performed with clinical suspicion of clinically significant prostate cancer (csPCa) between 2018 and 2022. Pathological diagnosis within 1 year after the MRI was used to diagnose csPCa. Seven clinical, 3 patient-level MRI-related, and 4 lesion-level MRI-related findings were extracted. After feature selection, 2 logistic regression models with and without lesions-level findings were created using data from facility I and II (development cohort). The area under the receiver operating characteristic curve (AUC) between the 2 models was compared in the PI-RADS ≥3 population in the development cohort and Facility III (validation cohort) using the Delong test. Interfacility differences of the selected predictive variables were evaluated using the Kruskal-Wallis test or chi-squared test.
Selected lesion-level features included the peripheral zone involvement and apparent diffusion coefficient (ADC) values. The model with lesions-level findings had significantly higher AUC than the model without in 655 examinations in the development cohort (0.81 vs 0.79, respectively, P = 0.005), but not in 553 examinations in the validation cohort (0.77 vs 0.76, respectively). Large interfacility differences were seen in the ADC distribution ( P < 0.001) and csPCa proportion in PI-RADS 3-5 ( P < 0.001).
Adding lesions-level findings improved the csPCa discrimination in the development but not the validation cohort. Interfacility differences impeded model generalization, including the distribution of reported ADC values and PI-RADS score-level csPCa proportion.
本研究旨在开发一种结合临床和磁共振成像（MRI）相关结果的前列腺癌风险预测模型，并评估添加前列腺影像报告和数据系统（PI-RADS）≥3级病变水平结果对其诊断性能的影响。
这项三中心回顾性研究纳入了2018年至2022年间因临床怀疑患有临床显著性前列腺癌（csPCa）而进行的前列腺MRI检查。MRI检查后1年内的病理诊断用于诊断csPCa。提取了7项临床、3项患者水平的MRI相关以及4项病变水平的MRI相关结果。经过特征选择后，使用机构I和II（开发队列）的数据创建了有和没有病变水平结果的2个逻辑回归模型。使用德龙检验在开发队列和机构III（验证队列）的PI-RADS≥3人群中比较这2个模型之间的受试者操作特征曲线下面积（AUC）。使用克鲁斯卡尔-沃利斯检验或卡方检验评估所选预测变量的机构间差异。
所选的病变水平特征包括外周带受累情况和表观扩散系数（ADC）值。在开发队列的655次检查中，有病变水平结果的模型的AUC显著高于没有病变水平结果的模型（分别为0.81和0.79，P = 0.005），但在验证队列的553次检查中并非如此（分别为0.77和0.76）。在ADC分布（P < 0.001）和PI-RADS 3 - 5中的csPCa比例方面存在较大的机构间差异（P < 0.001）。
添加病变水平结果改善了开发队列中csPCa的鉴别能力，但在验证队列中未改善。机构间差异阻碍了模型的推广，包括报告的ADC值分布和PI-RADS评分水平的csPCa比例。
