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Sökning: id:"swepub:oai:DiVA.org:umu-217209" > Machine learning de...

  • 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

Förlag, utgivningsår, omfång ...

  • Springer,2024
  • printrdacarrier

Nummerbeteckningar

  • LIBRIS-ID:oai:DiVA.org:umu-217209
  • https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-217209URI
  • https://doi.org/10.1007/s00330-023-10347-2DOI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

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Klassifikation

  • Ämneskategori:ref swepub-contenttype
  • Ämneskategori:art swepub-publicationtype

Anmärkningar

  • 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 och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Chen, HuiDepartment of Neuroradiology, University of Texas MD Anderson Cancer Center, TX, Houston, United States (författare)
  • Jiang, BinDepartment of Radiology, Stanford University, CA, Stanford, United States (författare)
  • Zhu, GuangmingDepartment of Neurology, University of Arizona, AZ, Tucson, United States (författare)
  • Usai, Marco VirgilioDepartment of Vascular Surgery, St. Franziskus Hospital, University of Münster, Münster, Germany (författare)
  • Austermann, MartinDepartment of Vascular Surgery, St. Franziskus Hospital, University of Münster, Münster, Germany (författare)
  • Shehada, YousefDepartment of Vascular Surgery, St. Franziskus Hospital, University of Münster, Münster, Germany (författare)
  • Johansson, EliasUmeå universitet,Neurovetenskaper(Swepub:umu)elsjon02 (författare)
  • Suri, JasjitGlobal Biomedical Technologies Inc., CA, Roseville, United States (författare)
  • Lanzino, GiuseppeDepartment of Neurosurgery, Mayo Clinic, MN, Rochester, United States (författare)
  • Benson, JohnDepartment of Radiology, Mayo Clinic, MN, Rochester, United States (författare)
  • Nardi, ValentinaDepartment of Cardiovascular Medicine, Mayo Clinic, MN, Rochester, United States (författare)
  • Lerman, AmirDepartment of Cardiovascular Medicine, Mayo Clinic, MN, Rochester, United States (författare)
  • Wintermark, MaxDepartment of Neuroradiology, University of Texas MD Anderson Cancer Center, TX, Houston, United States (författare)
  • Saba, LucaDepartment of Radiology, Azienda Ospedaliero Universitaria, Cagliari, Monserrato, Italy (författare)
  • 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

  • Ingår i:European Radiology: Springer34:6, s. 3612-36230938-79941432-1084

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