Magnetic resonance imaging (MRI) plays a valuable role in many task related with characterization of prostate cancer lesions. Recently, the DCE-MRI (Dynamic contrast Enhanced) has allowed to visualize and localize potential tumor regions. Specifically, K^trans, from DCR-MRI, has shown to be a powerful pharmacokinetic parameter that allows to characterize tumor biology and to detect treatment responses from reconstructed coefficient maps of capillary permeability. Nevertheless, even expert-based analysis of K^trans sequences are subject to a large false positive findings (FPF). In much of such cases, the prostate angiogenesis, or benign prostatic hyperplasia (BPH) regions are misclassified as cancer findings. This work introduces a robust deep convolutional strategy that characterizes K^transregions and allows an automatic prediction of cancer findings. The proposed strategy was validated over the SPIE-AAPM-NCI PROSTATEx public dataset with 320 multimodal images on peripheral, transitional and anterior fibromuscular stroma regions. The best configuration of proposal strategy achieved an area under the ROC curve (AUC) of 0.74. Additionally, the proposed strategy achieved a proper characterization by using mainly K^trans information that together with T2-MRI-transaxial overcome baseline strategies that use additional modalities of MRI.