Predicting clinically significant prostate cancer using DCE-MRI habitat descriptors

// N. Andres Parra 1 , Hong Lu 1, 2 , Qian Li 2 , Radka Stoyanova 3 , Alan Pollack 3 , Sanoj Punnen 4 , Jung Choi 5 , Mahmoud Abdalah 1 , Christopher Lopez 3 , Kenneth Gage 5 , Jong Y. Park 6 , Yamoah Kosj 6, 7 , Julio M. Pow-Sang 8 , Robert J. Gillies 1, 5 and Yoganand Balagurunathan 1 1 Department of Cancer Physiology, H.L. Moffitt Cancer Center, Tampa, FL, USA 2 Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China 3 Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA 4 Department of Urology, University of Miami Miller School of Medicine, Miami, FL, USA 5 Department of Radiology, H.L. Moffitt Cancer Center, Tampa, FL, USA 6 Department of Cancer Epidemiology, H.L. Moffitt Cancer Center, Tampa, FL, USA 7 Department of Radiation Oncology, H.L. Moffitt Cancer Center, Tampa, FL, USA 8 Department of Urology, H.L. Moffitt Cancer Center, Tampa, FL, USA Correspondence to: Yoganand Balagurunathan, email: Yoganand.Balagurunathan@moffitt.org Keywords: radiomics of MRI; DCE; mpMRI; prostate; prostate imaging Received: August 19, 2018     Accepted: November 16, 2018     Published: December 14, 2018 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 to find the best discriminating characteristics between clinically significant and insignificant prostate lesions defined by Gleason score (GS). Multivariable analysis was performed independently on one institution and validated on the other, using a multi-parametric feature model, based on DCE characteristics and ADC features. The models had an intra institution Area under the Receiver Operating Characteristic (AUC) of 0.82. Trained on Institution I and validated on the cohort from Institution II, the AUC was also 0.82 (sensitivity 0.68, specificity 0.95).