title: Novel MRI-derived quantitative biomarker for cardiac function applied to classifying ischemic cardiomyopathy within a Bayesian rule learning framework.
abstract: Characterization of regional left ventricular (LV) function may have application in prognosticating timely response and informing choice therapy in patients with ischemic cardiomyopathy. The purpose of this study is to characterize LV function through a systematic analysis of 4D (3D + time) endocardial motion over the cardiac cycle in an effort to define objective, clinically useful metrics of pathological remodeling and declining cardiac performance, using standard cardiac MRI data for two distinct patient cohorts accessed from CardiacAtlas.org: a) MESA - a cohort of asymptomatic patients; and b) DETERMINE - a cohort of symptomatic patients with a history of ischemic heart disease (IHD) or myocardial infarction. The LV endocardium was segmented and a signed phase-to-phase Hausdorff distance (HD) was computed at 3D uniformly spaced points tracked on segmented endocardial surface contours, over the cardiac cycle. An LV-averaged index of phase-to-phase endocardial displacement (P2PD) time-histories was computed at each tracked point, using the HD computed between consecutive cardiac phases. Average and standard deviation in P2PD over the cardiac cycle was used to prepare characteristic curves for the asymptomatic and IHD cohort. A novel biomarker of RMS error between mean patient-specific characteristic P2PD over the cardiac cycle for each individual patient and the cumulative P2PD characteristic of a cohort of asymptomatic patients was established as the RMS-P2PD marker. The novel RMS-P2PD marker was tested as a cardiac function based feature for automatic patient classification using a Bayesian Rule Learning (BRL) framework. The RMS-P2PD biomarker indices were significantly different for the symptomatic patient and asymptomatic control cohorts (p<0.001). BRL accurately classified 83.8% of patients correctly from the patient and control populations, with leave-one-out cross validation, using standard indices of LV ejection fraction (LV-EF) and LV end-systolic volume index (LV-ESVI). This improved to 91.9% with inclusion of the RMS-P2PD biomarker and was congruent with improvements in both sensitivity for classifying patients and specificity for identifying asymptomatic controls from 82.6% up to 95.7%. RMS-P2PD, when contrasted against a collective normal reference, is a promising biomarker to investigate further in its utility for identifying quantitative signs of pathological endocardial function which may boost standard image makers as precursors of declining cardiac performance.
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, USA ; Electrical & Computer Engineering, Sun Yat-Sen University - Carnegie Mellon University Joint Institute of Engineering, USA.
authors.forename: Prahlad G
authors.initials: PG
authors.lastname: Menon
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, USA.
authors.forename: Lailonny
authors.initials: L
authors.lastname: Morris
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, USA.
authors.forename: Mara
authors.initials: M
authors.lastname: Staines
authors.affiliation: Cardiovascular Imaging, The John Hopkins Hospital, USA.
authors.forename: Joao
authors.initials: J
authors.lastname: Lima
authors.affiliation: Feinberg Cardiovascular Research Institute, Northwestern University, USA.
authors.forename: Daniel C
authors.initials: DC
authors.lastname: Lee
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, USA.
authors.forename: Vanathi
authors.initials: V
authors.lastname: Gopalakrishnan
keywords: Bayesian Rule Learning; Cardiac Magnetic Resonance Imaging; Cardiomyopathy; Classification; Left Ventricular Function