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Radiomics Detection of Pulmonary Hypertension via Texture-Based Assessments of Cardiac MRI: A Machine-Learning Model Comparison—Cardiac MRI Radiomics in Pulmonary Hypertension.
- Source :
-
Journal of Clinical Medicine . May2021, Vol. 10 Issue 9, p1921-1921. 1p. - Publication Year :
- 2021
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20770383
- Volume :
- 10
- Issue :
- 9
- Database :
- Academic Search Index
- Journal :
- Journal of Clinical Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 150375546
- Full Text :
- https://doi.org/10.3390/jcm10091921