A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound

Figures

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  Fig. 1. Study process. This study consisted of three parts as shown in panel a to c. In the first part (panel a), we used unsupervised clustering to explore patient phenotypes and their clinical implications in total 405 patients with various stages of HF. Next, supervised machine learning was used to predict the functional (impaired LVEF and GLS) LV remodeling using the texture features in the same patient cohort (panel b). Finally, in an independent cohort of patients who underwent CMR and echocardiography, we explored the usefulness of texture-based supervised models for predicting myocardial fibrosis (panel c). WVU, West Virginia University; HF, heart failure; ML, machine learning; TDA, topological data analysis, LV, left ventricular; RFE, recursive feature elimination; CMR, cardiac magnetic resonance.

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  Fig. 2. Patient similarity network based on myocardial texture features. Panel a: Extracted texture features were integrated using topological data analysis to create a patient similarity network. In the network, patients with similar features form a node, and adjacent nodes, including similar patients, are connected with edges. The network demonstrated the shape of a bar that was geometrically divided into two parts which had significantly different clinical and echocardiographic characteristics although the groups were created using only texture features. Panel b. Patients X, Y, Z, and W were identified in corresponding x, y, z, and w nodes, respectively. X and Y had a normal cardiac function and were found in cluster A, whereas Z and W were located in cluster B with significantly impaired cardiac function. GLS, global longitudinal strain; LVEF, left ventricular ejection fraction.

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  Fig. 3. Clinical outcomes between clusters. Kaplan–Meier curve analyses showed that cluster B had a significantly higher incidence of cardiovascular death (panel a) and of the composite of cardiovascular death and major adverse cardiac events (MACE; panel b) compared with cluster A.

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  Fig. 4. Direct prediction of impaired cardiac function. Panels a to c show receiver operator characteristics curves for predicting reduced LVEF (<50%), impaired GLS (<16%), and the presence of myocardial fibrosis. Supervised machine learning models were successful in predicting these outcomes with the texture features extracted from still ultrasound images. LVEF, left ventricular ejection fraction; GLS, global longitudinal strain; ROC AUC, area under the curve; Accu, accuracy; Sens, sensitivity; Spec, specificity; F-1, F1 score.

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  Fig. 5. Prediction of myocardial fibrosis. The upper panels show still ultrasound images and the corresponding myocardial textures where the texture features were extracted. The lower panels show magnetic resonance images with late gadolinium enhancement.