Atherosclerosis risk classification with computed tomography angiography: A radiologic-pathologic validation study. (February 2023)

• Record Type: Journal Article
• Authors: Buckler, Andrew J.; Gotto, Antonio M.; Rajeev, Akshay; Nicolaou, Anna; Sakamoto, Atsushi; St Pierre, Samantha; Phillips, Matthew; Virmani, Renu; Villines, Todd C.
• Is Part Of: Atherosclerosis. Volume 366(2023)
• Page Start: 42
• Page End: 48
• Publication Date: 2023-02
• Subjects: Plaque stability -- Histopathology -- Phenotype; Arteriosclerosis -- Periodicals; Electronic journals; 616.136
• DOI: 10.1016/j.atherosclerosis.2022.11.013
• Languages: English
• ISSNs: 0021-9150

Abstract

Abstract: Background and aims: The application of machine learning to assess plaque risk phenotypes on cardiovascular CT angiography (CTA) is an area of active investigation. Studies using accepted histologic definitions of plaque risk as ground truth for machine learning models are uncommon. The aim was to evaluate the accuracy of a machine-learning software for determining plaque risk phenotype as compared to expert pathologists (histologic ground truth). Methods: Sections of atherosclerotic plaques paired with CTA were prospectively collected from patients undergoing carotid endarterectomy at two centers. Specimens were annotated for lipid-rich necrotic core, calcification, matrix, and intraplaque hemorrhage at 2 mm spacing and classified as minimal disease, stable plaque, or unstable plaque according to a modified American Heart Association histological definition. Phenotype is determined in two steps: plaque morphology is delineated according to histological tissue definitions, followed by a machine learning classifier. The performance in derivation and validation cohorts for plaque risk categorization and stenosis was compared to histologic ground truth at each matched cross-section. Results: A total of 496 and 408 vessel cross-sections in the derivation and validation cohorts (from 30 and 23 patients, respectively). The software demonstrated excellent agreement in the validation cohort with histological ground truth plaque risk phenotypes with weighted kappa of 0.82 [0.78–0.86] and area under the receiver operating curve for correct identification of plaque type was 0.97 [0.96, 0.98], 0.95 [0.94, 0.97], 0.99 [0.99, 1.0] for unstable plaque, stable plaque, and minimal disease, respectively. Diameter stenosis correlated poorly to histologically defined plaque type; weighted kappa 0.25 in the validation cohort. Conclusions: A machine-learning software trained on histological ground-truth tissue inputs demonstrated high accuracy for identifying plaque stability phenotypes as compared to expert pathologists. Graphical abstract: Image 1 Highlights: Machine learning software based on histologically defined tissue characterization inputs demonstrated high accuracy for plaque risk phenotype determination compared to matched 2 mm tissue sample cross-sections: weighted kappa 0.82 and 0.70 in derivation and validation cohorts. The area under the receiver operating curve for software correct identification of plaque type was 0.97, 0.95, 0.99 for unstable plaque, stable plaque, and minimal disease, respectively. Percent lumen diameter stenosis per cross-section correlated poorly with plaque risk phenotypes.