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Machine learning de...
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Pisu, FrancescoDepartment of Radiology, Azienda Ospedaliero Universitaria, Cagliari, Monserrato, Italy
(författare)
Machine learning detects symptomatic patients with carotid plaques based on 6-type calcium configuration classification on CT angiography
- Artikel/kapitelEngelska2024
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Springer,2024
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LIBRIS-ID:oai:DiVA.org:umu-217209
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https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-217209URI
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https://doi.org/10.1007/s00330-023-10347-2DOI
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Språk:engelska
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Sammanfattning på:engelska
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Errata: Pisu, F., Chen, H., Jiang, B. et al. Correction: Machine learning detects symptomatic patients with carotid plaques based on 6-type calcium configuration classification on CT angiography. Eur Radiol. 2024. DOI: 10.1007/s00330-024-10824-2
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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.].
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Chen, HuiDepartment of Neuroradiology, University of Texas MD Anderson Cancer Center, TX, Houston, United States
(författare)
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Jiang, BinDepartment of Radiology, Stanford University, CA, Stanford, United States
(författare)
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Zhu, GuangmingDepartment of Neurology, University of Arizona, AZ, Tucson, United States
(författare)
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Usai, Marco VirgilioDepartment of Vascular Surgery, St. Franziskus Hospital, University of Münster, Münster, Germany
(författare)
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Austermann, MartinDepartment of Vascular Surgery, St. Franziskus Hospital, University of Münster, Münster, Germany
(författare)
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Shehada, YousefDepartment of Vascular Surgery, St. Franziskus Hospital, University of Münster, Münster, Germany
(författare)
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Johansson, EliasUmeå universitet,Neurovetenskaper(Swepub:umu)elsjon02
(författare)
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Suri, JasjitGlobal Biomedical Technologies Inc., CA, Roseville, United States
(författare)
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Lanzino, GiuseppeDepartment of Neurosurgery, Mayo Clinic, MN, Rochester, United States
(författare)
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Benson, John C.Department of Radiology, Mayo Clinic, MN, Rochester, United States
(författare)
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Nardi, ValentinaDepartment of Cardiovascular Medicine, Mayo Clinic, MN, Rochester, United States
(författare)
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Lerman, AmirDepartment of Cardiovascular Medicine, Mayo Clinic, MN, Rochester, United States
(författare)
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Wintermark, MaxDepartment of Neuroradiology, University of Texas MD Anderson Cancer Center, TX, Houston, United States
(författare)
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Saba, LucaDepartment of Radiology, Azienda Ospedaliero Universitaria, Cagliari, Monserrato, Italy
(författare)
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Department of Radiology, Azienda Ospedaliero Universitaria, Cagliari, Monserrato, ItalyDepartment of Neuroradiology, University of Texas MD Anderson Cancer Center, TX, Houston, United States
(creator_code:org_t)
Sammanhörande titlar
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Ingår i:European Radiology: Springer34:6, s. 3612-36230938-79941432-1084
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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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visa fler...
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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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