Radiomics and Machine Learning for Detecting Scar Tissue on CT Delayed Enhancement Imaging

ABSTRACT:

Background

Delayed enhancement CT (CT-DE) has been evaluated as a tool for the detection of myocardial scar and compares well to the gold standard of MRI with late gadolinium enhancement (MRI-LGE). Prior work has established that high performance can be achieved with manual reading; however, few studies have looked at quantitative measures to differentiate scar and healthy myocardium on CT-DE or automated analysis.

Methods

Eighteen patients with clinically indicated MRI-LGE were recruited for CT-DE at multiple 80 and 100 kV post contrast imaging. Left ventricle segmentation was performed on both imaging modalities, along with scar segmentation on MRI-LGE. Segmentations were registered together and scar regions were estimated on CT-DE. 93 radiomic features were calculated

SUBMITTER: O'Brien H 

PROVIDER: S-EPMC9133416 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature