Towards clinical significance prediction using ktrans evidences in prostate cancer

Currently, K
^trans
coefficient maps have emerged to characterize tumor biology and treatment response. Salient localized coefficient on K
^trans
allows to detect and localize tumor regions from non-invasive MRI scanners. Nevertheless, such identified lesions on K
^trans
maps are highly variable and in much of the cases result in false positive indicators. In this work a set of labeled K
^trans
regions are processed into a supervised framework to correctly find true positive regions that are prostate cancer indicators. Three different algorithms were implemented to perform the classification: K-Nearest Neighbors (k-NN), Support vector machine (SVM), and Random forest (RaF). On a public dataset with 339 K
^trans
images on peripheral, transitional and anterior fibromuscular stroma regions, the SVM achieved an average accuracy of 80.83% with a ROC AUC of 0.68 on true evidence identification.