Differential diagnosis of left ventricular hypertrophy with cardiac magnetic resonance imaging texture feature analysis

Abstract

Abstract Funding Acknowledgements Type of funding sources: None. Background Hypertrophic cardiomyopathy (HCM), hypertensive cardiomyopathy (HTCM), and cardiac amyloidosis (CA) are characterized by left ventricular (LV) hypertrophy. CMR with late gadolinium enhancement is used in the differential diagnosis. Radiomics aims to extract features from medical images and quantify their patterns to produce quantitative biomarkers.  Our aim was to develop radiomics-derived biomarkers capable of differentiating between HCM, HTCM, CA and healthy controls using CMR cine images. Methods We included 103 patients (42 HCM, 51 HTCM, 10 CA; 31 females, age 65.0 ± 12.8 years) and 50 healthy subjects (29 females, age 52.5 ± 18.0 years). The study was done on a 1.5 T CMR scanner (Sonata; Siemens, Erlangen, Germany) and all the subjects underwent a conventional protocol including cine sequences acquired in the 2-, 4- and 3-chamber views as well as in a short axis stack. The most basal slice of the short axis stack immediately distal to the LV outflow tract was used for texture feature analysis and regions of interest (ROI) were defined by segmenting the LV myocardium and dividing it into 6 segments. For image analysis a radiomic approach was used. Five different quantizations were obtained in order to find the optimal one and 43 texture features were extracted by quantization and segment. Feature selection (FS) methods were studied to see the optimal number of textures, and 4 predictive models were evaluated including k-Nearest Neighbour (KNN) and Support Vector Machine with a linear kernel (SVML) models. Performance of each model was evaluated with the area under the ROC curve (AUC). Results The AUCs of each of the models for the different texture features were analysed, from 1 to 43, according to the ranking obtained and for 5 different number of grey levels. The maximum average AUC, 1.0, was obtained with KNN model trained to classify CA and control subjects. More interestingly, 0.89 average AUC was achieved with SVML model and 1 single feature for HCM vs HTCM vs CA study. Table 1 shows the best average AUCs (mean ± SD) obtained for each study, number of subjects (N), number of grey levels used in each case, number of optimal features and the classification model. Conclusions Texture analysis allows for the correct differential diagnosis of LVH. Best results (AUC of 89%), were achieved using the SVML model in septal segments. Texture feature analysis applied to CMR cine sequences is a promising advance that might allow for the use of contrast-free protocols for the differential diagnosis of LVH.