de Perrot Thomas, Scheffler Max, Boto José, Delattre Bénédicte M A, Combescure Christophe, Pusztaszeri Marc, Tille Jean-Christophe, Iselin Christophe, Vallée Jean-Paul
Division of Radiology, Geneva University Hospitals, Geneva, Switzerland.
Division of Clinical Epidemiology, Geneva University Hospitals, Geneva, Switzerland.
J Magn Reson Imaging. 2016 Sep;44(3):601-9. doi: 10.1002/jmri.25206. Epub 2016 Feb 23.
To assess the influence of perfusion on apparent coefficient diffusion (ADC) maps, the contribution of b-value images, and the number of b-values needed in prostate cancer detection by diffusion-weighted imaging (DWI).
Patients scheduled for prostatectomy were scanned by 3T magnetic resonance imaging (MRI) with DWI based on b-values 0-500-1000-1500 s/mm(2) . A monoexponential model was fitted to obtain ADC using multiple b-values, with or without b0 (perfusion-sensitive ADC4b-b0-500-1000-1500 , perfusion-insensitive ADC3b-b500-1000-1500 ), or two b-values (ADC2b-b0-500 , ADC2b-b0-1000 , ADC2b-b0-1500 ). Prostate and cancer foci were segmented to label voxels as normal or tumoral, according to histology. Areas under receiver operating characteristic curves (AUC) were calculated for each ADC and b-value, then for multivariate logistic regression models combining them. A threshold of 85 tumoral voxels (=0.5 cm(3) ) was used to stratify AUC analysis.
In all, 21 patients were selected. Segmentation collected 143,665 prostatic voxels including 10,069 tumoral voxels. In five patients, tumor segmentation provided fewer than 85 voxels, resulting in an ADC with AUC inferior to 0.52. In 16 patients with larger tumors, perfusion-sensitive ADC4b-b0-500-1000-1500 performed better than perfusion-insensitive ADC3b-b500-1000-1500 and similar to ADC2b-b0-1500 (AUC of 0.840, 0.809, and 0.838, respectively). In comparison to the ADC alone, models combining ADC4b-b0-500-1000-1500 or ADC2b-b0-1500 with b1500 improved performance, leading to similar AUCs of 0.884 and 0.883, respectively. In both models, ADC and b1500 were significant markers (P < 0.001).
Including b0 in ADC calculation provided superior ADC maps for prostate cancer detection. b1500 images as a combined parameter with ADC also improved performance. Using more than two b-values showed no improvement. J. Magn. Reson. Imaging 2016;44:601-609.
评估灌注对表观扩散系数（ADC）图的影响、b值图像的贡献以及在前列腺癌扩散加权成像（DWI）检测中所需的b值数量。
计划接受前列腺切除术的患者采用基于b值0 - 500 - 1000 - 1500 s/mm²的3T磁共振成像（MRI）进行DWI扫描。使用多个b值（有或无b0）拟合单指数模型以获取ADC，即灌注敏感的ADC4b - b0 - 500 - 1000 - 1500、灌注不敏感的ADC3b - b500 - 1000 - 1500，或两个b值（ADC2b - b0 - 500、ADC2b - b0 - 1000、ADC2b - b0 - 1500）。根据组织学将前列腺和癌灶进行分割，将体素标记为正常或肿瘤性。计算每个ADC和b值的受试者操作特征曲线（AUC）下面积，然后计算将它们组合的多变量逻辑回归模型的AUC。使用85个肿瘤体素（=0.5 cm³）的阈值对AUC分析进行分层。
共选择了21例患者。分割收集了143,665个前列腺体素，其中包括10,069个肿瘤体素。在5例患者中，肿瘤分割提供的体素少于85个，导致ADC的AUC低于0.52。在16例肿瘤较大的患者中，灌注敏感的ADC4b - b0 - 500 - 1000 - 1500的表现优于灌注不敏感的ADC3b - b500 - 1000 - 15且与ADC2b - b0 - 1500相似（AUC分别为0.840、0.809和0.838）。与单独的ADC相比，将ADC4b - b0 - 500 - 1000 - 1500或ADC2b - b0 - 1500与b1500组合的模型性能有所提高，AUC分别为0.884和0.883。在这两个模型中，ADC和b1500都是显著标志物（P < 0.001）。
在ADC计算中纳入b0可为前列腺癌检测提供更好的ADC图。b1500图像作为与ADC的组合参数也提高了性能。使用超过两个b值并未显示出改善。《磁共振成像杂志》2016年；44：601 - 609。
