Ji Yugang, Liu Wei, Liu Houdong, Wen Jing
Department of Radiology, Yacheng First People's Hospital; Yacheng First Hospital Affiliated of Nanjing Medical College, Yancheng, China.
Department of Radiology, The People's Hospital of Tinghu District, Yancheng, China.
Front Oncol. 2025 Aug 27;15:1615005. doi: 10.3389/fonc.2025.1615005. eCollection 2025.
This study was intended to evaluate the performance of biparametric MRI (bpMRI) radiomics for detecting clinically significant prostate cancer (csPCa) in men with prostate-specific antigen (PSA) of 4-10 ng/mL.
We retrospectively included 287 patients with PSA levels of 4-10 ng/mL. Radiomics features were extracted from two MRI protocols of T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI, with b-values of 0, 1000, and 2000 s/mm²), and then selected with the least absolute shrinkage and selection operator (LASSO) regression method. The apparent diffusion coefficient (ADC) maps were calculated from these images and used for analysis. The radiomics signature (Radscore) based on the most useful radiomics features was calculated with the logistic regression method. MRI/US fusion targeted biopsy results were used as the reference standard. Diagnostic performance was decided using the area under the receiver operating characteristic (ROC) curve (AUC), and compared with Delong's test. Finally, a model integrating radiomics features and Prostate Imaging Reporting and Data System (PI-RADS) was constructed.
A total of 15 T2WI radiomics features and 12 from DWI features were retained after selection with LASSO regression. On the test set, radiomics outperformed PI-RADS, with an AUC of 0.928 (95% CI 0.868-0.988) vs. 0.807 (95% CI 0.705-0.908; P=0.04). Additionally, the combined nomogram generated higher diagnostic accuracy (AUC 0.955, 95% CI 0.905-1.00), significantly outperforming both PI-RADS (P=0.002) and radiomics alone (P=0.02).
bpMRI-based radiomics exhibited promising diagnostic accuracy for the detection of csPCa, significantly outperforming either PI-RADS or PSAD among patients with PSA of 4-10 ng/mL. Furthermore, the developed nomogram integrating radiomics and PI-RADS could further enhance diagnostic performance.
本研究旨在评估双参数MRI（bpMRI）影像组学在检测前列腺特异性抗原（PSA）水平为4 - 10 ng/mL的男性患者中临床显著前列腺癌（csPCa）的性能。
我们回顾性纳入了287例PSA水平为4 - 10 ng/mL的患者。从T2加权成像（T2WI）和扩散加权成像（DWI，b值为0、1000和2000 s/mm²）的两种MRI检查方案中提取影像组学特征，然后采用最小绝对收缩和选择算子（LASSO）回归方法进行特征选择。根据这些图像计算表观扩散系数（ADC）图并用于分析。基于最有用的影像组学特征，采用逻辑回归方法计算影像组学特征值（Radscore）。以MRI/超声融合靶向活检结果作为参考标准。使用受试者操作特征（ROC）曲线下面积（AUC）来判定诊断性能，并通过德龙检验进行比较。最后，构建了一个整合影像组学特征和前列腺影像报告与数据系统（PI-RADS）的模型。
经LASSO回归选择后，共保留了15个T2WI影像组学特征和12个DWI影像组学特征。在测试集上，影像组学的表现优于PI-RADS，AUC为0.928（95%可信区间0.868 - 0.988），而PI-RADS的AUC为0.807（95%可信区间0.705 - 0.908；P = 0.04）。此外，联合列线图产生了更高的诊断准确性（AUC 0.955, 95%可信区间0.905 - 1.00），显著优于PI-RADS（P = 0.002）和单独的影像组学（P = 0.02）。
基于bpMRI的影像组学在检测csPCa方面表现出有前景的诊断准确性，在PSA为4 - 10 ng/mL的患者中显著优于PI-RADS或前列腺特异抗原密度（PSAD）。此外，所开发的整合影像组学和PI-RADS的列线图可进一步提高诊断性能。
