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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.

Authors
  • Priya, Sarv1
  • Aggarwal, Tanya2
  • Ward, Caitlin3
  • Bathla, Girish1
  • Jacob, Mathews4
  • Gerke, Alicia5
  • Hoffman, Eric A1, 6
  • Nagpal, Prashant1
  • 1 Department of Radiology, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA.
  • 2 Department of Family Medicine, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA.
  • 3 Department of Biostatistics, University of Iowa College of Public Health, Iowa City, IA 52242, USA.
  • 4 Department of Electrical Engineering, University of Iowa College of Engineering, Iowa City, IA 52242, USA.
  • 5 Department of Pulmonary Medicine, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA.
  • 6 Roy J. Carver Department of Biomedical Engineering, University of Iowa College of Engineering, Iowa City, IA 52242, USA.
Type
Published Article
Journal
Journal of Clinical Medicine
Publisher
MDPI AG
Publication Date
Apr 28, 2021
Volume
10
Issue
9
Identifiers
DOI: 10.3390/jcm10091921
PMID: 33925262
Source
Medline
Keywords
Language
English
License
Unknown

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.

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