Relationship between Extension or Texture Features of Late Gadolinium Enhancement and Ventricular Tachyarrhythmias in Hypertrophic Cardiomyopathy

Abstract

Purpose To evaluate the relationship between extension or texture features of late gadolinium enhancement (LGE) and ventricular tachyarrhythmias in hypertrophic cardiomyopathy (HCM). Materials and Methods Twenty-three patients with HCM were enrolled in this IRB-approved study. The extension of LGE was determined based on the American Heart Association segments model. Texture analysis was performed for 43 myocardial LGE using an open-access software (MaZda, Technical University of Lodz, Institute of Electronics, Poland). The relationship between the extension or texture features of LGE and ventricular tachyarrhythmias was evaluated using unpaired test and receiver-operating characteristic (ROC) analysis. Results Six of 23 patients had a history of ventricular tachyarrhythmias, and 16 patients had LGE. All of the 6 patients with the arrhythmias had more than 4 LGE segments and more LGE segments than those without (p < 0.01). Among 4 texture features, entropy LL was the only discriminator between the 2 patient groups (p < 0.01; threshold, 19624; area under the curve [AUC], 0.72). An ROC analysis gave the number of segments showing LGE a better result (AUC, 0.96) for identification of HCM patients with ventricular tachyarrhythmias than the entropy LL of LGE. Conclusion Patients with HCM and a history of ventricular tachyarrhythmias had a wider extension of LGE, and their entropy LL of LGE was significantly lower than those without. The extension of LGE and texture analysis may provide information about LGE related to ventricular tachyarrhythmias in HCM.

Radiomics Detection of Pulmonary Hypertension via Texture-Based Assessments of Cardiac MRI: A Machine-Learning Model Comparison—Cardiac MRI Radiomics in Pulmonary Hypertension

“…Classifier model performance depends both on the available data and on the classification task problem. Prior cMRI studies using radiomics performed in patients with myocarditis, hypertrophic, or other forms of cardiomyopathy or prognostication of tachyarrhythmias have only assessed single or limited machine learning models [ 16 , 17 , 18 , 24 , 31 , 32 ]. In our study, we explored the impact of multiple different models and feature selection techniques on myocardial radiomics performance.…”

Progress in radiomics of common heart disease based on cardiac magnetic resonance imaging

“…In recent years, many studies have begun to explore the relationship between CMR-LGE texture features and cardiac adverse events. As far as we know, Amano et al [44] reported, for the first time, the correlation between CMR-LGE image texture features of HCM and ventricular arrhythmia. The results showed that among the four texture features, the entropy LL of patients with arrhythmia was significantly smaller than that of patients without arrhythmia (P = 0.0058), the area under the curve of entropy LL was 0.72, and the area under the curve of delayed enhancement was 0.96.…”

“…Year Publication Image substrate Sample size Important feature Performance Myocardial infarction 2018 Gibbs et al [38] LGE 76 Kurtosis and skewness AUC = 0.73 2019 Androulakis et al [24] LGE 154 Entropy HR = 3.20 2018 Baessler et al [27] Cine [44] LGE 23 entropy LL AUC = 0.72 2018 Cheng et al [45] LGE 67 X0_GLRLM_energy, X0_H_skewness and X0_GLCM_cluster_tendency HR = 0.78 2020 Alis et al [30] LGE 64 GlcmV1SumVarnc, GlcmN3DifVarnc, AUC = 0.92…”

Image Preprocessing and Filtering Effect on the Estimate of Myocardial Radiomic Features From T1 and T2 Mapping in Hypertrophic Cardiomyopathy

“…While radiomic analysis has been mainly applied in the eld of oncology, there is a growing interest in improving the diagnostic accuracy and prognostic value of CMR imaging by exploiting radiomic techniques [25][26][27][28][29][30] . Recently, radiomics has aroused as a useful tool for unveiling myocardial tissue characteristics in HCM patients [18][19][20][21][31][32][33][34][35][36] . In this regard, Baeßler et al 31 have reported signi cant differences in texture features obtained from T1weighted images of HCM patients and healthy subjects, identifying a single feature (i.e., gray level non uniformity from the gray level run length matrix) as the best discriminator.…”

“…Scho eld et al 32 have found that texture analysis of cine CMR images was able to differentiate patients with HCM, cardiac amyloid, aortic stenosis, and healthy subjects. Moreover, some studies have focused on the prognostic role of radiomic analysis of LGE images in HCM, nding that texture features may be signi cantly related to the arrhythmic risk 33 and adverse clinical outcome 34 .…”