Radiomics-Based Classification of Left Ventricular Non-compaction, Hypertrophic Cardiomyopathy, and Dilated Cardiomyopathy in Cardiovascular Magnetic Resonance.

Guillem Casas,Andrea Guala,Jose F Rodríguez-Palomares,Cristian Izquierdo,Carlos Martin-Isla,Victor M Campello,Karim Lekadir,Polyxeni Gkontra

doi:10.3389/fcvm.2021.764312

Abstract

Left Ventricular (LV) Non-compaction (LVNC), Hypertrophic Cardiomyopathy (HCM), and Dilated Cardiomyopathy (DCM) share morphological and functional traits that increase the diagnosis complexity. Additional clinical information, besides imaging data such as cardiovascular magnetic resonance (CMR), is usually required to reach a definitive diagnosis, including electrocardiography (ECG), family history, and genetics. Alternatively, indices of hypertrabeculation have been introduced, but they require tedious and time-consuming delineations of the trabeculae on the CMR images. In this paper, we propose a radiomics approach to automatically encode differences in the underlying shape, gray-scale and textural information in the myocardium and its trabeculae, which may enhance the capacity to differentiate between these overlapping conditions. A total of 118 subjects, including 35 patients with LVNC, 25 with HCM, 37 with DCM, as well as 21 healthy volunteers (NOR), underwent CMR imaging. A comprehensive radiomics characterization was applied to LV short-axis images to quantify shape, first-order, co-occurrence matrix, run-length matrix, and local binary patterns. Conventional CMR indices (LV volumes, mass, wall thickness, LV ejection fraction—LVEF—), as well as hypertrabeculation indices by Petersen and Jacquier, were also analyzed. State-of-the-art Machine Learning (ML) models (one-vs.-rest Support Vector Machine—SVM—, Logistic Regression—LR—, and Random Forest Classifier—RF—) were used for one-vs.-rest classification tasks. The use of radiomics models for the automated diagnosis of LVNC, HCM, and DCM resulted in excellent one-vs.-rest ROC-AUC values of 0.95 while generating these results without the need for the delineation of the trabeculae. First-order and texture features resulted to be among the most discriminative features in the obtained radiomics signatures, indicating their added value for quantifying relevant tissue patterns in cardiomyopathy differential diagnosis.

Highlights

Cardiomyopathies (CMs) are defined as primary myocardial disorders in the absence of other conditions that may affect the structural or functional properties of the heart’s muscle [1]
The results described in this paper show the promise of the proposed radiomics approach for achieving state-of-the-art Left ventricular non-compaction (LVNC), Hypertrophic Cardiomyopathy (HCM), and Dilated Cardiomyopathy (DCM) differential diagnosis more efficiently, while removing the need for the delineation of the Left Ventricular (LV) trabeculae
We evaluated and compared the performance of the different machine learning models

Summary

Introduction

Cardiomyopathies (CMs) are defined as primary myocardial disorders in the absence of other conditions that may affect the structural or functional properties of the heart’s muscle [1]. The difficulties to differentially and timely diagnose LVNC in clinical practice has motivated the development of new imaging indices, in particular, the Petersen [7] and Jacquier [8] coefficients, which estimate the level of hypertrabeculation in the LV myocardium. These coefficients, while they improve LVNC diagnosis [9], are challenging and tedious to estimate in practice, as they require expert and accurate identification and delineation of the trabeculae on the CMR images. This is a timeconsuming task that is subject to inter-observer variability given the inherent complexity of the trabeculae

Results

Discussion

Conclusion