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Machine learning de...
Machine learning detects symptomatic patients with carotid plaques based on 6-type calcium configuration classification on CT angiography
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- Pisu, Francesco (författare)
- Department of Radiology, Azienda Ospedaliero Universitaria, Cagliari, Monserrato, Italy
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- Chen, Hui (författare)
- Department of Neuroradiology, University of Texas MD Anderson Cancer Center, TX, Houston, United States
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- Jiang, Bin (författare)
- Department of Radiology, Stanford University, CA, Stanford, United States
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- Zhu, Guangming (författare)
- Department of Neurology, University of Arizona, AZ, Tucson, United States
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- Usai, Marco Virgilio (författare)
- Department of Vascular Surgery, St. Franziskus Hospital, University of Münster, Münster, Germany
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- Austermann, Martin (författare)
- Department of Vascular Surgery, St. Franziskus Hospital, University of Münster, Münster, Germany
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- Shehada, Yousef (författare)
- Department of Vascular Surgery, St. Franziskus Hospital, University of Münster, Münster, Germany
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- Johansson, Elias (författare)
- Umeå universitet,Neurovetenskaper
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- Suri, Jasjit (författare)
- Global Biomedical Technologies Inc., CA, Roseville, United States
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- Lanzino, Giuseppe (författare)
- Department of Neurosurgery, Mayo Clinic, MN, Rochester, United States
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- Benson, John C. (författare)
- Department of Radiology, Mayo Clinic, MN, Rochester, United States
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- Nardi, Valentina (författare)
- Department of Cardiovascular Medicine, Mayo Clinic, MN, Rochester, United States
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- Lerman, Amir (författare)
- Department of Cardiovascular Medicine, Mayo Clinic, MN, Rochester, United States
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- Wintermark, Max (författare)
- Department of Neuroradiology, University of Texas MD Anderson Cancer Center, TX, Houston, United States
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- Saba, Luca (författare)
- Department of Radiology, Azienda Ospedaliero Universitaria, Cagliari, Monserrato, Italy
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(creator_code:org_t)
- Springer, 2024
- 2024
- Engelska.
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Ingår i: European Radiology. - : Springer. - 0938-7994 .- 1432-1084. ; 34:6, s. 3612-3623
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
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- 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. Graphical Abstract: [Figure not available: see fulltext.].
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
Nyckelord
- Calcified plaques
- Carotid arteries
- Cerebrovascular events
- CT angiography
- Machine learning
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Pisu, Francesco
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Chen, Hui
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Jiang, Bin
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Zhu, Guangming
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Usai, Marco Virg ...
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Austermann, Mart ...
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Shehada, Yousef
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Johansson, Elias
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Suri, Jasjit
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Lanzino, Giusepp ...
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Benson, John C.
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Nardi, Valentina
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Lerman, Amir
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Wintermark, Max
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Saba, Luca
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