The roles of radiomics and deep learning for automatic detection, stability assessment, and rupture risk prediction in intracranial aneurysms: a systematic review

作者：Chunyue Yan, Hang Zhang, Xiaojiao Zhang, Kun Wang, Ming Yang, Fei Wang · 发表于：European journal of medical research · 年份：2025 · DOI：10.1186/s40001-025-03588-y · 被引用次数：2 · 研究领域：Intracranial Aneurysms: Treatment and Complications、Meningioma and schwannoma management、Radiomics and Machine Learning in Medical Imaging

BACKGROUND AND OBJECTIVES: Presently, the radiomics and deep learning have achieved notable advancements in the automatic detection, stability assessment, and rupture risk prediction of intracranial aneurysms (IAs). However, there are still certain challenges in data quality, model robustness, and applicability among the published studies. Therefore, this study aims to provide a systematic review of the roles of radiomics and deep learning in the automatic detection, stability assessment, and rupture risk prediction of IAs, with providing insights for the individual stratified management of IAs patients. METHODS: Between January 2015 and December 2024, the literatures were retrieved in PubMed, Web of Science, Embase and the Cochrane Library using a combination of subject headings and keywords. Two researchers independently screened the literature of radiomics or deep learning in the automatic detection, stability and rupture risk prediction of IAs. The QUADAS-2 tool was used to assess the methodological quality of the included studies. The study characteristics and area under the curve (AUC) were summarized using tables to clearly present the research progress in this field. RESULTS: Ultimately, 28 original research of a total of 32,991 IAs were included. Notably, 89% of the publications (25/28) appeared between 2021 and 2024. Six studies for IAs automatic detection, all of them established deep learning frameworks, while five conducted multicenter analyses and only two carri...