Share Email Print

Proceedings Paper

Classification of lesion specific myocardial ischemia using cardiac computed tomography radiomics
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Lesion-specific myocardial ischemia is a common heart disorder and a significant cause of cardiovascular morbidity and mortality. It alters left ventricular myocardial thickness progressively. Clinical decision-making is based on Fractional flow reserve (FFR), which is invasive and may prolong the surgery time and with extra radiation exposure. Although coronary computed tomography angiogram (CCTA) has high accuracy and negative predictive value (NPV) in the evaluation of coronary artery disease (CAD), it has low specificity in the diagnosis of lesion-specific myocardial ischemia. We propose a learning method for the assessment of lesion-specific myocardial ischemia using noninvasive CCTA and radiomics study. Sixty patients with suspected or known to have CAD were enrolled. The left ventricular myocardial on the CCTA was manually segmented. One hundred radiomic features of left ventricular myocardial were extracted. The most informative and non-redundant features were selected to train a Support Vector Machine (SVM) is to differentiate lesion-specific myocardial ischemia and without lesion-specific myocardial ischemia (normal). Analysis of the predictions showed that the reported method consistently predicted lesion-specific myocardial ischemia with the accuracy of 0.8550 ± 0.0333 and area under the receiver operating characteristic curve (AUC) 0.8952 ± 0.0370. This study shows that LVMradiomic features derived from CCTA data can be used to classify lesion specific myocardial ischemia. The radiomic features of left ventricular myocardial from CCTA could be a useful tool for determining lesion specific myocardial ischemia.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113143P (16 March 2020); doi: 10.1117/12.2548471
Show Author Affiliations
Xiuxiu He, Emory Univ. (United States)
Bang Jun Guo, Emory Univ. (United States)
Southern Medical Univ. (China)
Nanjing Univ. (China)
Tonghe Wang, Emory Univ. (United States)
Yang Lei, Emory Univ. (United States)
Tian Liu, Emory Univ. (United States)
Long Jiang Zhang, Southern Medical Univ. (China)
Nanjing Univ. (China)
Xiaofeng Yang, Emory Univ. (United States)

Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?