Texture analysis of cardiovascular magnetic resonance cine images differentiates aetiologies of left ventricular hypertrophy. Issue 2 (February 2019) - Schofield, R.; Ganeshan, B.; Fontana, M.; Nasis, A.; Castelletti, S.; Rosmini, S.; Treibel, T.A.; Manisty, C.; Endozo, R.; Groves, A.; Moon, J.C.
Abstract : AIM: To investigate whether unenhanced cardiovascular magnetic resonance (CMR) balanced steady state free precession (bSSFP) cine images could be analysed using textural analysis (TA) software to differentiate different aetiologies of disease causing increased myocardial wall thickness (left ventricular hypertrophy [LVH]) and indicate the severity of myocardial tissue abnormality. MATERIALS AND METHODS: A mid short axis unenhanced cine frame of 216 patients comprising 50 cases of hypertrophic cardiomyopathy (HCM; predominantly Left ventricular outflow tract obstruction [LVOTO] subtype), 52 cases of cardiac amyloid (CA; predominantly AL: light chain subtype), 68 cases of aortic stenosis (AS), 15 hypertensive patients with LVH (HTN+LVH), and 31 healthy volunteers (HV) underwent TA of the CMR cine images (CMRTA) using TexRAD (TexRAD Ltd, Cambridge, UK). Among the HV, 16/31 were scanned twice to form a test–retest reproducibility cohort. CMRTA comprised a filtration-histogram technique to extract and quantify features using six parameters. RESULTS: Test–retest analysis in the HV showed a medium filter (3 mm) was the most reproducible (intra-class correlation of 0.9 for kurtosis and skewness and 0.8 for mean and SD). Disease cohorts were statistically different ( p< 0.001) to HV for all parameters. Pairwise comparisons of CMRTA parameters showed kurtosis and skewness was consistently significant in ranking the degree of difference from HV (greatest to least): CA, HCM, LVH+HTN, AS ( p< 0.001). Similarly, mean, standard deviation, entropy, and mean positive pixel (MPP) were consistent in ranking degree of difference from HV: HCM, CA, AS and HTN+LVH. CONCLUSION: Radiomic features of bSSFP CMR data sets derived using TA show promise in discriminating between the aetiologies of LVH. Highlights: Textural Analysis provides additional information from routinely acquired medical imaging. Textural Analysis can be performed on a single frame bSSFP cine image (routinely acquired in CMR). CMRTA differentiates between health and disease. It shows promise in differentiating between diseases causing LVH. CMRTA, combined with machine learning, shows promise as an ancillary diagnostic tool and possible prognostic indicator.