Differentiating between cardiac amyloidosis and hypertrophic cardiomyopathy on non-contrast cine-magnetic resonance images using machine learning-based radiomics - Jiang, Shu; Zhang, Lianlian; Wang, Jia; Li, Xia; Hu, Su; Fu, Yigang; Wang, Xin; Hao, Shaowei; Hu, Chunhong (Frontiers in Cardiovascular Medicine, 2022)
Objectives This study aimed to determine whether texture analysis (TA) and machine learning-based classifications can be applied in differential diagnosis of cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM) using non-contrast cine cardiac magnetic resonance (CMR) images. Methods In this institutional review board-approved study, we consecutively enrolled 167 patients with CA (n=85), HCM (n=82), and 84 patients with normal CMR served as controls. All cases were randomized into training (119 patients [70%]) and validation (48 patients [30%]) groups. A total of 275 texture features were extracted from cine images. Based on regression analysis with the least absolute shrinkage and selection operator (LASSO), nine machine learning models were established and their diagnostic performance determined. Results Nineteen radiomics texture features derived from cine imeges were used to differentiate CA and HCM. In the validation cohort, the support vector machine (SVM), which had an accuracy of 0.85, showed the best performance (MCC=0.637). Gray level non-uniformity (GLevNonU) was the single most effective feature. The combined model of radiomics texture features and conventional MR metrics had superior discriminatory performance (AUC=0.89) over conventional MR metrics model (AUC=0.79). Moreover, results showed that GLevNonU levels in HCM patients were significantly higher compared with levels in CA patients and control groups (P < 0.001). A cut-off of GLevNonU ≥25 was shown to differentiate between CA and HCM patients, with an an area under the curve (AUC) of 0.86 (CI:0.804-0.920). Multiple comparisons tests showed that GLevNonU was significantly greater in LGE+, relative to LGE-patient groups (CA+ vs. CA- and HCM+ vs. HCM-, P= 0.01, 0.001, respectively). Conclusion Machine learning-based classifiers can accurately differentiate between CA and HCM on non-contrast cine images. The radiomics-MR combined model can be used to improve the discriminatory performance. TA may be used to assess myocardial microstructure changes that occur during different stages of cardiomyopathies.