1. Machine learning detects symptomatic patients with carotid plaques based on 6-type calcium configuration classification on CT angiography.
- Author
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Pisu, Francesco, Chen, Hui, Jiang, Bin, Zhu, Guangming, Usai, Marco Virgilio, Austermann, Martin, Shehada, Yousef, Johansson, Elias, Suri, Jasjit, Lanzino, Giuseppe, Benson, J. C., Nardi, Valentina, Lerman, Amir, Wintermark, Max, and Saba, Luca
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ATHEROSCLEROTIC plaque , *MACHINE learning , *ASYMPTOMATIC patients , *ANGIOGRAPHY , *OLDER patients , *CALCIUM - Abstract
Objectives: While the link between carotid plaque composition and cerebrovascular vascular (CVE) events is recognized, the role of calcium configuration remains unclear. This study aimed to develop and validate a CT angiography (CTA)–based machine learning (ML) model that uses carotid plaques 6-type calcium grading, and clinical parameters to identify CVE patients with bilateral plaques. Material and methods: We conducted a multicenter, retrospective diagnostic study (March 2013–May 2020) approved by the institutional review board. We included adults (18 +) with bilateral carotid artery plaques, symptomatic patients having recently experienced a carotid territory ischemic event, and asymptomatic patients either after 3 months from symptom onset or with no such event. Four ML models (clinical factors, calcium configurations, and both with and without plaque grading [ML-All-G and ML-All-NG]) and logistic regression on all variables identified symptomatic patients. Internal validation assessed discrimination and calibration. External validation was also performed, and identified important variables and causes of misclassifications. Results: We included 790 patients (median age 72, IQR [61–80], 42% male, 64% symptomatic) for training and internal validation, and 159 patients (age 68 [63–76], 36% male, 39% symptomatic) for external testing. The ML-All-G model achieved an area-under-ROC curve of 0.71 (95% CI 0.58–0.78; p <.001) and sensitivity 80% (79–81). Performance was comparable on external testing. Calcified plaque, especially the positive rim sign on the right artery in older and hyperlipidemic patients, had a major impact on identifying symptomatic patients. Conclusion: The developed model can identify symptomatic patients using plaques calcium configuration data and clinical information with reasonable diagnostic accuracy. Clinical relevance: The analysis of the type of calcium configuration in carotid plaques into 6 classes, combined with clinical variables, allows for an effective identification of symptomatic patients. Key Points: • While the association between carotid plaques composition and cerebrovascular events is recognized, the role of calcium configuration remains unclear. • Machine learning of 6-type plaque grading can identify symptomatic patients. Calcified plaques on the right artery, advanced age, and hyperlipidemia were the most important predictors. • Fast acquisition of CTA enables rapid grading of plaques upon the patient's arrival at the hospital, which streamlines the diagnosis of symptoms using ML. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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