Machine Learning Detects Symptomatic Plaques in Patients With Carotid Atherosclerosis on CT Angiography

Q: What was the study’s main goal?

To develop and validate a CT angiography–based machine learning model that detects symptomatic carotid plaques using plaque composition features and carotid stenosis.

Q: How was the machine learning model trained and evaluated?

It was trained using stenosis degree and volumes of 13 CT-angiography-derived intracarotid plaque subcomponents, then assessed via repeated 10-fold cross-validation and testing-cohort discrimination and calibration.

Q: Which plaque features were most associated with symptomatic status?

The intraplaque hemorrhage to lipid volume ratio (≥50%) and the percentage of intraplaque hemorrhage volume (≥10%) remained significantly associated with higher symptomatic likelihood after adjustment.

Aug 3, 2026

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A computed tomography angiography–based machine learning model was developed and validated to identify symptomatic carotid plaques in patients with carotid atherosclerosis using plaque composition features together with the degree of carotid stenosis. The model was trained on 13 intracarotid plaque subcomponents derived from CT angiography (including lipid, intraplaque hemorrhage, and calcium) and evaluated with repeated 10-fold cross-validation and testing-cohort discrimination and calibration. In a retrospective single-center cohort of 268 patients, model performance outperformed traditional logit analyses based on stenosis degree and individual plaque factors. The interpretable framework highlighted intraplaque hemorrhage–to–lipid volume ratio and intraplaque hemorrhage volume proportion as key predictors, supporting clinical decision-making.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Background

Methods

Results

Conclusions

Clinical Perspective

Key Words

Downloaded from [http://ahajournals.org by on June](http://ahajournals.org by on June) 19, 2024 Circulation: Cardiovascular Imaging ORIGINAL ARTICLE Machine Learning Detects Symptomatic Plaquesin Patients With Carotid Atherosclerosis on CT Angiography Francesco Pisu, MS*; Brady J. Williamson, PhD*; Valentina Nardi, MD; Kosmas I. Paraskevas, MD, PhD; Josep Puig, MD, PhD; Achala Vagal, MD, MS; Gianluca de Rubeis, MD; Michele Porcu, MD; Riccardo Cau, MD; John C. Benson, MD; Antonella Balestrieri, MD; Giuseppe Lanzino, MD; Jasjit S. Suri, MD, PhD; Abdelkader Mahammedi, MD; Luca Saba, MD BACKGROUND: This study aimed to develop and validate a computed tomography angiography based machine learning model that uses plaque composition data and degree of carotid stenosis to detect symptomatic carotid plaques in patients with carotid atherosclerosis. METHODS: The machine learning based model was trained using degree of stenosis and the volumes of 13 computed tomography angiography derived intracarotid plaque subcomponents (eg, lipid, intraplaque hemorrhage, calcium) to identify plaques associated with cerebrovascular events. The model was internally validated through repeated 10-fold cross-validation and tested on a dedicated testing cohort according to discrimination and calibration. RESULTS: This retrospective, single-center study evaluated computed tomography angiography scans of 268 patients with both symptomatic and asymptomatic carotid atherosclerosis (163 for the derivation set and 106 for the testing set) performed between March 2013 and October 2019. The area-under-receiver-operating characteristics curve by machine learning on the testing cohort (0.89) was significantly higher than the areas under the curve of traditional logit analysis based on the degree of stenosis (0.51, P<0.001), presence of intraplaque hemorrhage (0.69, P<0.001), and plaque composition (0.78, P<0.001), respectively. Comparable performance was obtained on internal validation. The identified plaque components and associated cutoff values that were significantly associated with a higher likelihood of symptomatic status after adjustment were the ratio of intraplaque hemorrhage to lipid volume (≥50%, 38.5 [10.1–205.1]; odds ratio, 95% CI) and percentage of intraplaque hemorrhage volume (≥ 10%, 18.5 [5.7–69.4]; odds ratio, 95% CI) . CONCLUSIONS: This study presented an interpretable machine learning model that accurately identifies symptomatic carotid plaques using computed tomography angiography derived plaque composition features, aiding clinical decision-making. GRAPHIC ABSTRACT: A graphic abstract is available for this article. Key Words: atherosclerosis ◼ angiography ◼ calibration ◼ carotid stenosis ◼ hemorrhage | Stroke is a common cause of death and severe dis
ability worldwide, and a significant proportion of ischemic stroke is related to carotid artery atherosclerosis. 1,2 Current guidelines for stroke prediction are primarily based on the degree of stenosis.1 However, recent literature have shown that plaque structure and | composition play a fundamental role in plaque vulnerability or stability.3–7 Previous studies have demonstrated that morphological and composition differences between plaques can predict the occurrence of ischemic strokes.8–11 Investigation of these unique biomarkers and their predictive value is an area of active research. | | --- | --- | Correspondence to: Luca Saba, MD, Department of Radiology, Azienda Ospedaliero-Universitaria, Monserrato (Cagliari), Italy. Email [lucasaba@tiscali.it](lucasaba@tiscali.it) *F. Pisu and B.J. Williamson are joint first authors. Supplemental Material is available at [https://www.ahajournals.org/doi/suppl/10.1161/CIRCIMAGING.123.016274](https://www.ahajournals.org/doi/suppl/10.1161/CIRCIMAGING.123.016274) . For Sources of Funding and Disclosures, see page 475. © 2024 The Authors. Circulation: Cardiovascular Imaging is published on behalf of the American Heart Asso

FAQs

What was the study’s main goal?

To develop and validate a CT angiography–based machine learning model that detects symptomatic carotid plaques using plaque composition features and carotid stenosis.

How was the machine learning model trained and evaluated?

It was trained using stenosis degree and volumes of 13 CT-angiography-derived intracarotid plaque subcomponents, then assessed via repeated 10-fold cross-validation and testing-cohort discrimination and calibration.

Which plaque features were most associated with symptomatic status?

The intraplaque hemorrhage to lipid volume ratio (≥50%) and the percentage of intraplaque hemorrhage volume (≥10%) remained significantly associated with higher symptomatic likelihood after adjustment.

Aug 3, 2026

Upload by Ivy