Radiomics detection of pulmonary hypertension via texture‐based assessments of cardiac mri: A machine‐learning model comparison—cardiac mri radiomics in pulmonary hypertension

Abstract

The role of reliable, non‐invasive imaging‐based recognition of pulmonary hypertension (PH) remains a diagnostic challenge. The aim of the current pilot radiomics study was to assess the diagnostic performance of cardiac MRI (cMRI)‐based texture features to accurately predict PH. The study involved IRB‐approved retrospective analysis of cMRIs from 72 patients (42 PH and 30 healthy controls) for the primary analysis. A subgroup analysis was performed including patients from the PH group with left ventricle ejection fraction ≥ 50%. Texture features were generated from mid‐left ventricle myocardium using balanced steady‐state free precession (bSSFP) cine short‐axis imaging. Forty‐five different combinations of classifier models and feature selection techniques were evaluated. Model performance was assessed using receiver operating characteristic curves. A multilayer perceptron model fitting using full feature sets was the best classifier model for both the primary analysis (AUC 0.862, accuracy 78%) and the subgroup analysis (AUC 0.918, accuracy 80%). Model performance demonstrated considerable variation between the models (AUC 0.523–0.918) based on the chosen model–feature selection combination. Cardiac MRI‐based radiomics recognition of PH using texture features is feasible, even with preserved left ventricular ejection fractions.