T1 and ECV mapping texture analysis distinguishing hypertrophic cardiomyopathy from athletes heart better than median values
Authors: Dresselaers T , Rafouli-Stergiou P , De Bosscher R , Tilborghs S , Dausin C , Cernicanu A , Claus P , Willems R , Claessen G , Bogaert J 
Published: July 2021

Abstract

Funding Acknowledgements
Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Ph.D fellowship of the Research Foundation Flanders (FWO). The Master@Heart trial is funded by the FWO.
Introduction
Differentiating intensive training induced hypertrophy from hyperthropic cardiomyopathy (HCM) is important to identify those young athletes at risk of sudden cardiac death. Swoboda and colleagues demonstrated that T1 and ECV mapping can aid such a differentiation between athletic and pathological hypertrophy, particularly in subjects with indeterminate wall thickness (1).
Recently texture analysis (TA) methods of CMR data have demonstrated improved diagnostic accuracy over conventional qualitative analysis in various heart diseases. Only few studies have applied TA to T1 and ECV mapping data (2-4). Here we aimed to demonstrate that a TA approach provides superior capacity to distinguish HCM from athlete’s heart over average native T1 and ECV values.
Purpose
It was our hypothesis that a texture analysis of T1 and ECV mapping images would identify features that could discriminate between a HCM and athlete’s heart with a higher classification accuracy (CA) than average T1 and ECV values.
Methods
This study included data from 97 subjects diagnosed with HCM (acc. to guidelines; 5) and 28 athletes that took part in the Master@Heart trial (an ongoing study assessing the beneficial effects of long-term endurance exercise for the prevention of coronary artery disease, 6). Long and short axis T1 mapping data was acquired on a 1.5T Philips Ingenia system using MOLLI (seconds scheme). After offline motion correction and T1 and ECV map calculation (7), the left ventricular myocardium was manually delineated (3D Slicer; 8). Texture analysis of the masked images resulted in 194 features (Pyradiomics, standard settings; 9). The dataset was then split (75/25%) for training and testing purposes keeping images from the same subject within the same set. A fast correlation based filter rank was applied to the training data to derive relevant features. A further reduction to only two features was based on the CA of a support vector machine (SVM) learning method (linear kernel; cost 0.9 regression loss epsilon 0.1; leave-one-out). Finally, ROC analysis on the test data was used to determine the diagnostic accuracy for the following predictors: (1) median T1 and ECV (2) two most relevant features (training) (3) combination of (1) and (2) (ROC AUC statistics (10)).
Results
The two most relevant features were the histogram feature ECV energy and the gray level size zone matrix (GLSZM) feature native T1 zone entropy, a measure of heterogeneity in the texture pattern.
A model to distinguish HCM from athletes based on these features outperformed the model using only median T1 and ECV values with both higher sensitivity and specificity (table 1) and a significantly higher AUC in the ROC analysis (p &lt; 0.05, figure 1). Combining these two features with median values did not improve the CA further.

Conclusion
Texture analysis of motion-corrected T1 and ECV mapping images out-performs classical analysis based on average values in distinguishing HCM from athlete"s heart.