Quantitative diffusion-weighted imaging and dynamic contrast-enhanced characterization of the index lesion with multiparametric MRI in prostate cancer patients.

To compare a simplified intravoxel incoherent motion (sIVIM) model to commonly used monoexponential and biexponential models in the characterization of prostate cancer (PCa) and noncancerous prostate tissues, and to investigate combinations of diffusion-weighted imaging (DWI) measures with dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI)-derived parameters in MRI-visible index lesions, to facilitate PCa risk stratification.

In this retrospective, Institutional Review Board (IRB)-approved study, 43 consecutive patients with PCa who had 3T MRI exams followed by radical prostatectomy were included. DWI and DCE parameters were measured from one index lesion per patient, and noncancerous central gland and peripheral zone. Logistic regression modeling was performed to select the optimal combination of DWI and DCE measurements for tumor risk assessment.

All diffusion models showed the lowest diffusion coefficients in tumors, intermediate values in noncancerous central gland, and highest values in noncancerous peripheral zone (all P < 0.001). K(trans) and kep were higher in tumors compared to central gland (P < 0.005) and peripheral zone (P < 0.001). The initial area under the contrast concentration curve was higher in tumor than the peripheral zone (P < 0.001). The area under the receiver operating characteristic curve of the combined DWI and DCE parameters (0.78) was higher than its individual components (0.73 and 0.63, respectively) for discriminating low- and intermediate-to-high-risk tumors.

The sIVIM model provided comparable results with fewer b-values and shorter image acquisition time. The combination of DWI and DCE measurements of MRI-visible index lesions improved the preoperative prostate cancer risk characterization compared to the individual parameters from either technique alone. J. Magn. Reson. Imaging 2016.

Journal of magnetic resonance imaging : JMRI. 2016 Jul 21 [Epub ahead of print]

Qing Yuan, Daniel N Costa, Julien Sénégas, Yin Xi, Andrea J Wiethoff, Neil M Rofsky, Claus Roehrborn, Robert E Lenkinski, Ivan Pedrosa

Department of Radiology, UT Southwestern Medical Center, Dallas, Texas, USA., Department of Radiology, UT Southwestern Medical Center, Dallas, Texas, USA., Philips Research Laboratories, Hamburg, Germany., Department of Radiology, UT Southwestern Medical Center, Dallas, Texas, USA., Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, Texas, USA., Department of Radiology, UT Southwestern Medical Center, Dallas, Texas, USA., Department of Urology, UT Southwestern Medical Center, Dallas, Texas, USA., Department of Radiology, UT Southwestern Medical Center, Dallas, Texas, USA., Department of Radiology, UT Southwestern Medical Center, Dallas, Texas, USA.