Predicting clinically significant prostate cancer using DCE-MRI habitat descriptors.

ABSTRACT: Prostate cancer diagnosis and treatment continues to be a major public health challenge. The heterogeneity of the disease is one of the major factors leading to imprecise diagnosis and suboptimal disease management. The improved resolution of functional multi-parametric magnetic resonance imaging (mpMRI) has shown promise to improve detection and characterization of the disease. Regions that subdivide the tumor based on Dynamic Contrast Enhancement (DCE) of mpMRI are referred to as DCE-Habitats in this study. The DCE defined perfusion curve patterns on the identified tumor habitat region are used to assess clinical significance. These perfusion curves were systematically quantified using seven features in association with the patient biopsy outcome and classifier models were built t

SUBMITTER: Parra NA 

PROVIDER: S-EPMC6324677 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature