Abstract: Pulmonary hypertension (PH) is a life-threatening and rapidly progressive disease in which functional adaptation of the right ventricle (RV), as quantified by RV ejection fraction (RVEF), is a key prognostic marker. However, RVEF is largely insensitive to regional or early RV dysfunction which may improve prognostication and allow prompt identification of high-risk cases. Cardiac Magnetic Resonance (MR) imaging is a standard modality for quantification of RV function, and can be used to derive anatomically accurate, high-resolution 3D shape models of RV contraction using recently developed computational imaging analysis techniques. These time-resolved 3D models may lend additional insights beyond what is offered by simple conventional measures like RVEF. In this study, we train a deep survival network to predict mortality in PH patients by learning complex RV contraction patterns from 3D shape models of RV motion. To handle right-censored survival time outcomes, our network utilized a Cox proportional hazards partial likelihood loss function. The network was trained on imaging and mortality data on 148 PH patients. It yielded improved prediction accuracy and superior risk stratification, compared with a multivariable survival model consisting of RVEF and other conventional parameters of RV function. This study demonstrates the utility of deep learning for identification of prognostic spatio-temporal patterns in 3D models of RV motion.
Keywords: Deep Learning, Cardiac Magnetic Resonance Imaging, Pulmonary Hypertension, Survival Analysis
Author Affiliation: Imperial College London, United Kingdom