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The current landscape of machine learning-based radiomics in arteriovenous malformations: a systematic review and radiomics quality score assessment.
Grossen, Audrey A; Evans, Alexander R; Ernst, Griffin L; Behnen, Connor C; Zhao, Xiaochun; Bauer, Andrew M.
Afiliação
  • Grossen AA; Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States.
  • Evans AR; Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States.
  • Ernst GL; Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States.
  • Behnen CC; Data Science and Analytics, University of Oklahoma, Norman, OK, United States.
  • Zhao X; Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States.
  • Bauer AM; Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States.
Front Neurol ; 15: 1398876, 2024.
Article em En | MEDLINE | ID: mdl-38915798
ABSTRACT

Background:

Arteriovenous malformations (AVMs) are rare vascular anomalies involving a disorganization of arteries and veins with no intervening capillaries. In the past 10 years, radiomics and machine learning (ML) models became increasingly popular for analyzing diagnostic medical images. The goal of this review was to provide a comprehensive summary of current radiomic models being employed for the diagnostic, therapeutic, prognostic, and predictive outcomes in AVM management.

Methods:

A systematic literature review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, in which the PubMed and Embase databases were searched using the following terms (cerebral OR brain OR intracranial OR central nervous system OR spine OR spinal) AND (AVM OR arteriovenous malformation OR arteriovenous malformations) AND (radiomics OR radiogenomics OR machine learning OR artificial intelligence OR deep learning OR computer-aided detection OR computer-aided prediction OR computer-aided treatment decision). A radiomics quality score (RQS) was calculated for all included studies.

Results:

Thirteen studies were included, which were all retrospective in nature. Three studies (23%) dealt with AVM diagnosis and grading, 1 study (8%) gauged treatment response, 8 (62%) predicted outcomes, and the last one (8%) addressed prognosis. No radiomics model had undergone external validation. The mean RQS was 15.92 (range 10-18).

Conclusion:

We demonstrated that radiomics is currently being studied in different facets of AVM management. While not ready for clinical use, radiomics is a rapidly emerging field expected to play a significant future role in medical imaging. More prospective studies are warranted to determine the role of radiomics in the diagnosis, prediction of comorbidities, and treatment selection in AVM management.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article