Department of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600, Yi Shan Road, Shanghai, 200233, China.
State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, 2 South Taibai Road, Xi'an, 710071, Shaanxi, China.
Eur Radiol. 2021 Mar;31(3):1760-1769. doi: 10.1007/s00330-020-07227-4. Epub 2020 Sep 16.
We aimed to compare the efficiency of prostate cancer (PCa) detection using a radiomics signature based on advanced zoomed diffusion-weighted imaging and conventional full-field-of-view DWI.
A total of 136 patients, including 73 patients with PCa and 63 without PCa, underwent multi-parametric magnetic resonance imaging (mp-MRI). Radiomic features were extracted from prostate lesion areas segmented on full-field-of-view DWI with b-value = 1500 s/mm (f-DWI), advanced zoomed DWI images with b-value = 1500 s/mm (z-DWI), calculated zoomed DWI with b-value = 2000 s/mm (z-calDWI), and apparent diffusion coefficient (ADC) maps derived from both sequences (f-ADC and z-ADC). Single-imaging modality radiomics signature, mp-MRI radiomics signature, and a mixed model based on mp-MRI and clinically independent risk factors were built to predict PCa probability. The diagnostic efficacy and the potential net benefits of each model were evaluated.
Both z-DWI and z-calDWI had significantly better predictive performance than f-DWIb1500 (z-DWIb1500 vs. f-DWIb1500: p = 0.048; z-calDWIb2000 vs. f-DWIb1500: p = 0.014). z-ADC had a slightly higher area under the curve (AUC) value compared with f-ADC value but was not significantly different (p = 0.127). For predicting the presence of PCa, the AUCs of clinical independent risk factors model, mp-MRI model, and mixed model were 0.81, 0.93, and 0.94 in training sets, and 0.74, 0.92, and 0.93 in validation sets, respectively.
Radiomics signatures based on the z-DWI technology had better diagnostic accuracy for PCa than that based on the f-DWI technology. The mixed model was better at diagnosing PCa and guiding clinical interventions for patients with suspected PCa compared with mp-MRI signatures and clinically independent risk factors.
• Advanced zoomed DWI technology can improve the diagnostic accuracy of radiomics signatures for PCa. • Radiomics signatures based on z-calDWI have the best diagnostic performance among individual imaging modalities. • Compared with the independent clinical risk factors and the mp-MRI model, the mixed model has the best diagnostic efficiency.
我们旨在比较基于先进的放大弥散加权成像和全视野弥散加权成像的放射组学特征对前列腺癌（PCa）检测的效率。
共纳入 136 名患者，其中 73 名患者患有 PCa，63 名患者无 PCa。所有患者均行多参数磁共振成像（mp-MRI）检查。从全视野弥散加权成像（f-DWI，b 值=1500 s/mm）、先进放大弥散加权成像图像（z-DWI，b 值=1500 s/mm）、计算放大弥散加权成像（z-calDWI，b 值=2000 s/mm）以及两个序列的表观弥散系数（ADC）图（f-ADC 和 z-ADC）中勾画前列腺病变区域，提取放射组学特征。建立单一成像方式放射组学特征、mp-MRI 放射组学特征和基于 mp-MRI 与临床独立危险因素的混合模型，以预测 PCa 概率。评估每个模型的诊断效能和潜在净收益。
z-DWI 和 z-calDWI 均显著优于 f-DWIb1500（z-DWIb1500 与 f-DWIb1500 比较：p=0.048；z-calDWIb2000 与 f-DWIb1500 比较：p=0.014）。z-ADC 的曲线下面积（AUC）值略高于 f-ADC 值，但无统计学差异（p=0.127）。在训练集中，临床独立危险因素模型、mp-MRI 模型和混合模型预测 PCa 存在的 AUC 值分别为 0.81、0.93 和 0.94，在验证集中，分别为 0.74、0.92 和 0.93。
基于 z-DWI 技术的放射组学特征对 PCa 的诊断准确性优于基于 f-DWI 技术的放射组学特征。与 mp-MRI 特征和临床独立危险因素相比，混合模型在诊断 PCa 并指导疑似 PCa 患者的临床干预方面具有更好的诊断效能。
① 先进的放大弥散加权成像技术可提高放射组学特征对 PCa 的诊断准确性。② z-calDWI 成像的放射组学特征具有最佳的诊断性能。③ 与独立临床危险因素和 mp-MRI 模型相比，混合模型的诊断效率最佳。
