ObjectivesTo assess the feasibility of extracting radiomics signal intensity based
features from the myocardium using cardiovascular magnetic resonance (CMR) imaging
stress perfusion sequences. Furthermore, to compare the diagnostic performance of
radiomics models against standard-of-care qualitative visual assessment of stress
perfusion images, with the ground truth stenosis label being defined by invasive Fractional
Flow Reserve (FFR) and quantitative coronary angiography.MethodsWe used the Dan-NICAD
1 dataset, a multi-centre study with coronary computed tomography angiography, 1,5
T CMR stress perfusion, and invasive FFR available for a subset of 148 patients with
suspected coronary artery disease. Image segmentation was performed by two independent
readers. We used the Pyradiomics platform to extract radiomics first-order (n = 14)
and texture (n = 75) features from the LV myocardium (basal, mid, apical) in rest
and stress perfusion images.ResultsOverall, 92 patients (mean age 62 years, 56 men)
were included in the study, 39 with positive FFR. We double-cross validated the model
and, in each inner fold, we trained and validated a per territory model. The conventional
analysis results reported sensitivity of 41% and specificity of 84%. Our final radiomics
model demonstrated an improvement on these results with an average sensitivity of
53% and specificity of 86%.ConclusionIn this proof-of-concept study from the Dan-NICAD
dataset, we demonstrate the feasibility of radiomics analysis applied to CMR perfusion
images with a suggestion of superior diagnostic performance of radiomics models over
conventional visual analysis of perfusion images in picking up perfusion defects defined
by invasive coronary angiography.
