  To our knowledge, all deep computer-aided detection and diagnosis (CAD)
systems for prostate cancer (PCa) detection consider bi-parametric magnetic
resonance imaging (bp-MRI) only, including T2w and ADC sequences while
excluding the 4D perfusion sequence,which is however part of standard clinical
protocols for this diagnostic task. In this paper, we question strategies to
integrate information from perfusion imaging in deep neural architectures. To
do so, we evaluate several ways to encode the perfusion information in a U-Net
like architecture, also considering early versus mid fusion strategies. We
compare performance of multiparametric MRI (mp-MRI) models with the baseline
bp-MRI model based on a private dataset of 219 mp-MRI exams. Perfusion maps
derived from dynamic contrast enhanced MR exams are shown to positively impact
segmentation and grading performance of PCa lesions, especially the 3D MR
volume corresponding to the maximum slope of the wash-in curve as well as Tmax
perfusion maps. The latter mp-MRI models indeed outperform the bp-MRI one
whatever the fusion strategy, with Cohen's kappa score of 0.318$\pm$0.019 for
the bp-MRI model and 0.378 $\pm$ 0.033 for the model including the maximum
slope with a mid fusion strategy, also achieving competitive Cohen's kappa
score compared to state of the art.
