Assessment of Remote Myocardium Heterogeneity in Patients with Ventricular Tachycardia Using Texture Analysis of Late Iodine Enhancement (LIE) Cardiac Computed Tomography (cCT) Images
Open Access
- 13 March 2018
- journal article
- research article
- Published by Springer Nature in Molecular Imaging and Biology
- Vol. 20 (5) , 816-825
Abstract
Purpose Diffuse remodeling of myocardial extra-cellular matrix is largely responsible for left ventricle (LV) dysfunction and arrhythmias. Our hypothesis is that the texture analysis of late iodine enhancement (LIE) cardiac computed tomography (cCT) images may improve characterization of the diffuse extra-cellular matrix changes. Our aim was to extract volumetric extracellular volume (ECV) and LIE texture features of non-scarred (remote) myocardium from cCT of patients with recurrent ventricular tachycardia (rVT), and to compare these radiomic features with LV-function, LV-remodeling, and underlying cardiac disease. Procedures Forty-eight patients suffering from rVT were prospectively enrolled: 5/48 with idiopathic VT (IVT), 23/48 with post-ischemic dilated cardiomyopathy (ICM), 9/48 with idiopathic dilated cardiomyopathy (IDCM), and 11/48 with scars from a previous healed myocarditis (MYO). All patients underwent echocardiography to assess LV systolic and diastolic function and cCT with pre-contrast, angiographic, and LIE scan to obtain end-diastolic volume (EDV), ECV, and first-order texture parameters of Hounsfield Unit (HU) of remote myocardium in LIE [energy, entropy, HU-mean, HU-median, standard deviation (SD), and mean absolute deviation (MAD)]. Results Energy, HU mean, and HU median by cCT texture analysis correlated with ECV (rho = 0.5650, rho = 0.5741, rho = 0.5068; p < 0.0005). cCT-derived ECV, HU-mean, HU-median, SD, and MAD correlated directly to EDV by cCT and inversely to ejection fraction by echocardiography (p < 0.05). SD and MAD correlated with diastolic function by echocardiography (rho = 0.3837, p = 0.0071; rho = 0.3330, p = 0.0208). MYO and IVT patients were characterized by significantly lower values of SD and MAD when compared with ICM and IDCM patients, independently of LV-volume systolic and diastolic function. Conclusions Texture analysis of LIE may expand cCT capability of myocardial characterization. Myocardial heterogeneity (SD and MAD) was associated with LV dilatation, systolic and diastolic function, and is able to potentially identify the different patterns of structural remodeling characterizing patients with rVT of different etiology.Keywords
Funding Information
- Italian Ministry of Health: “Giovani Ricercatori—Ricerca Finalizzata,” (GR-2009-1594705)
This publication has 38 references indexed in Scilit:
- Association Between Extracellular Matrix Expansion Quantified by Cardiovascular Magnetic Resonance and Short-Term MortalityCirculation, 2012
- Radiomics: Extracting more information from medical images using advanced feature analysisEuropean Journal of Cancer, 2012
- Prognostic Value of Myocardial Contrast Delayed Enhancement With 64-Slice Multidetector Computed Tomography After Acute Myocardial InfarctionJACC, 2012
- Diminished Cardiac Fibrosis in Heart Failure is Associated with Altered Ventricular Arrhythmia PhenotypeJournal of Cardiovascular Electrophysiology, 2010
- Contribution of extracellular matrix to the mechanical properties of the heartJournal of Molecular and Cellular Cardiology, 2010
- Evaluation of Diffuse Myocardial Fibrosis in Heart Failure With Cardiac Magnetic Resonance Contrast-Enhanced T1 MappingJACC, 2008
- Characterization of Acute and Chronic Myocardial Infarcts by Multidetector Computed TomographyCirculation, 2006
- Ventricular Remodeling After Infarction and the Extracellular Collagen MatrixCirculation, 2003
- Microfibrosis Produces Electrical Load Variations Due to Loss of Side‐to‐Side Cell Connections; A Major Mechanism of Structural Heart Disease ArrhythmiasPacing and Clinical Electrophysiology, 1997
- Functional and histopathologic correlation in patients with dilated cardiomyopathy: An integrated evaluation by multivariate analysisJACC, 1987