Classification of lesion specific myocardial ischemia using cardiac computed tomography radiomics

Xiuxiu He; Bang Jun Guo; Tonghe Wang; Yang Lei; Tian Liu; Long Jiang Zhang; Xiaofeng Yang

doi:10.1117/12.2548471

16 March 2020 Classification of lesion specific myocardial ischemia using cardiac computed tomography radiomics

Xiuxiu He, Bang Jun Guo, Tonghe Wang, Yang Lei, Tian Liu, Long Jiang Zhang, Xiaofeng Yang

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113143P (2020) https://doi.org/10.1117/12.2548471
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

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Xiuxiu He, Bang Jun Guo, Tonghe Wang, Yang Lei, Tian Liu, Long Jiang Zhang, and Xiaofeng Yang "Classification of lesion specific myocardial ischemia using cardiac computed tomography radiomics", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113143P (16 March 2020); https://doi.org/10.1117/12.2548471

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KEYWORDS

Ischemia

Computed tomography

Heart

Feature extraction

Machine learning

Computer aided diagnosis and therapy

Image segmentation

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