PURPOSE: 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). MATERIALS AND METHODS: 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. RESULTS: 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). CONCLUSION: 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.
PURPOSE: 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). MATERIALS AND METHODS: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. RESULTS: 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). CONCLUSION: 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.
Authors: I Friedli; L A Crowe; L Berchtold; S Moll; K Hadaya; T de Perrot; C Vesin; P-Y Martin; S de Seigneux; J-P Vallée Journal: Sci Rep Date: 2016-07-21 Impact factor: 4.379
Authors: Sarah L Hurrell; Sean D McGarry; Amy Kaczmarowski; Kenneth A Iczkowski; Kenneth Jacobsohn; Mark D Hohenwalter; William A Hall; William A See; Anjishnu Banerjee; David K Charles; Marja T Nevalainen; Alexander C Mackinnon; Peter S LaViolette Journal: J Med Imaging (Bellingham) Date: 2017-10-27