Coronary plaque risk classification in images acquired with photon-counting-detector (PCD) CT was performed using a radiomics-based machine learning (ML) model. With IRB approval, 19 coronary CTA patients were scanned on a PCD-CT (NAEOTOM Alpha, Siemens Healthineers) with median CTDIvol of 8.02 mGy. Five types of images: virtual monoenergetic images (VMIs) at 50-keV, 70-keV, and 100-keV, iodine maps, and virtual non-contrast (VNC) images were reconstructed using an iterative reconstruction algorithm (QIR), a quantitative kernel (Qr40) and 0.6-mm/0.3-mm slice thickness/increment. Atherosclerotic plaques were segmented using semi-automatic software (Research Frontier, Siemens). Segmentation confirmation and risk stratification (low- vs high-risk) were performed by a board-certified cardiac radiologist. A total of 93 radiomic features were extracted from each image using PyRadiomics (v2.2.0b1). For each feature, a t-test was performed between low- and high-risk plaques (p<0.05 considered significant). Two significant and non-redundant features were input into a support vector machine (SVM). A leave-one-out cross-validation strategy was adopted and the classification accuracy was computed. Fifteen low-risk and ten high-risk plaques were identified by the radiologist. A total of 18, 32, 43, 16, and 55 out of 93 features in 50-keV, 70-keV, 100-keV, iodine map, and VNC images were statistically significant. A total of 17, 19, 22, 20, and 22 out of 25 plaques were classified correctly in 50-keV, 70-keV, 100-keV, iodine map, and VNC images, respectively. A ML model using 100-keV VMIs and VNC images derived from coronary PCD-CTA best automatically differentiated low- and high-risk coronary plaques.
Keywords: cardiac CT; computed tomography; photon-counting-detector; radiomics, machine learning.