Classification of high-risk coronary plaques using radiomic analysis of multi-energy photon-counting-detector computed tomography (PCD-CT) images

Chelsea A. S. Dunning; Prabhakar Shantha Rajiah; Scott S. Hsieh; Andrea Esquivel; Mariana Yalon; Nikkole M. Weber; Hao Gong; Joel G. Fletcher; Cynthia H. McCollough; Shuai Leng

doi:10.1117/12.2654412

7 April 2023 Classification of high-risk coronary plaques using radiomic analysis of multi-energy photon-counting-detector computed tomography (PCD-CT) images

Chelsea A. S. Dunning, Prabhakar Shantha Rajiah, Scott S. Hsieh, Andrea Esquivel, Mariana Yalon, Nikkole M. Weber, Hao Gong, Joel G. Fletcher, Cynthia H. McCollough, Shuai Leng

Author Affiliations +

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124652T (2023) https://doi.org/10.1117/12.2654412
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

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, 17 coronary CTA patients were scanned on a PCD-CT (NAEOTOM Alpha, Siemens Healthineers) with median CTDIvol of 4.56 mGy. Four types of images: 120-kV PCD-CT image, virtual monoenergetic images (VMIs) at 50-keV and 100-keV, and iodine maps were reconstructed using an iterative reconstruction algorithm, a vascular kernel (Bv40) and 0.6-mm/0.4-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 1674 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). Feature reduction was performed with a clustering algorithm and 6 non-redundant features were input into a linear support vector machine (SVM). A leave-one-out cross-validation strategy was adopted and the area under the ROC curve (AUC) was computed. Twelve low-risk and 5 high-risk plaques were identified by the radiologist. A total of 80, 66, 183, and 48 out of 1674 features in 120-kV, 50-keV, 100-keV, and iodine map images were statistically significant. The SVM classified 16/17 plaques correctly in the 120-kV PCD-CT and 50-keV VMI images. The AUC was 0.967, 0.967, 0.917, and 0.833 in 120-kV, 50-keV, 100-keV, and iodine map images, respectively. A ML model using coronary PCD-CTA images at 120-kV and 50-keV best automatically differentiated low- and high-risk coronary plaques.

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Chelsea A. S. Dunning, Prabhakar Shantha Rajiah, Scott S. Hsieh, Andrea Esquivel, Mariana Yalon, Nikkole M. Weber, Hao Gong, Joel G. Fletcher, Cynthia H. McCollough, and Shuai Leng "Classification of high-risk coronary plaques using radiomic analysis of multi-energy photon-counting-detector computed tomography (PCD-CT) images", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124652T (7 April 2023); https://doi.org/10.1117/12.2654412

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