Characterization of interstitial diffuse fibrosis patterns using texture analysis of myocardial native T1 mapping

Background The pattern of myocardial fibrosis differs significantly between different cardiomyopathies. Fibrosis in hypertrophic cardiomyopathy (HCM) is characteristically as patchy and regional but in dilated cardiomyopathy (DCM) as diffuse and global. We sought to investigate if texture analyses o...

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Bibliographic Details
Published in:PloS one 2020-06, Vol.15 (6), p.e0233694-e0233694
Main Authors: El-Rewaidy, Hossam, Neisius, Ulf, Nakamori, Shiro, Ngo, Long, Rodriguez, Jennifer, Manning, Warren J., Nezafat, Reza
Format: Article
Language:English
Subjects:
Biology and Life Sciences
Cardiomyopathy
Cardiovascular disease
Computer and Information Sciences
Datasets
Dilated cardiomyopathy
Feature extraction
Fibrosis
Image acquisition
Mapping
Medical schools
Medicine
Medicine and Health Sciences
Patients
Regression analysis
Regression models
Reproducibility
Research and Analysis Methods
Support vector machines
Texture
Training
Womens health
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Summary:Background The pattern of myocardial fibrosis differs significantly between different cardiomyopathies. Fibrosis in hypertrophic cardiomyopathy (HCM) is characteristically as patchy and regional but in dilated cardiomyopathy (DCM) as diffuse and global. We sought to investigate if texture analyses on myocardial native T1 mapping can differentiate between fibrosis patterns in patients with HCM and DCM. Methods We prospectively acquired native myocardial T1 mapping images for 321 subjects (55±15 years, 70% male): 65 control, 116 HCM, and 140 DCM patients. To quantify different fibrosis patterns, four sets of texture descriptors were used to extract 152 texture features from native T1 maps. Seven features were sequentially selected to identify HCM- and DCM-specific patterns in 70% of data (training dataset). Pattern reproducibility and generalizability were tested on the rest of data (testing dataset) using support vector machines (SVM) and regression models. Results Pattern-derived texture features were capable to identify subjects in HCM, DCM, and controls cohorts with 202/237(85.2%) accuracy of all subjects in the training dataset using 10-fold cross-validation on SVM (AUC = 0.93, 0.93, and 0.93 for controls, HCM and DCM, respectively), while pattern-independent global native T1 mapping was poorly capable to identify those subjects with 121/237(51.1%) accuracy (AUC = 0.78, 0.51, and 0.74) (P
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0233694