High-resolution vessel wall imaging-driven radiomic analysis for the precision prediction of intracranial aneurysm rupture risk: a promising approach
The radiomic features extracted from HR-VWI images demonstrated robust predictive utility for IA rupture risk in both the Rad-score and ML models. The XGBoost-based ML model outperformed the Rad-score model in efficacy and performance, and proved to be a noninvasive, efficient, and accurate tool for identifying high-risk IA patients.
Authors
- Wenqing Yuan (ORCID: https://orcid.org/0000-0002-3723-3313)
- Chang Yan (ORCID: https://orcid.org/0000-0003-4675-322X)
- Yongxiang Jiang (ORCID: https://orcid.org/0000-0001-9770-5733)
- Dajing Guo (ORCID: https://orcid.org/0000-0001-8655-6621)
- Ting Chen (ORCID: https://orcid.org/0000-0001-5988-4371)
- Zihang Wang (ORCID: https://orcid.org/0000-0001-9032-5412)
- Shuangyan Jiang
Institutions
- Dalian Medical University (CN)
- Second Affiliated Hospital of Chongqing Medical University (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Frontiers in Neuroscience
- Published
- 2025-04-22
- DOI
- https://doi.org/10.3389/fnins.2025.1581373
- Citations
- 2
- Primary Topic
- Intracranial Aneurysms: Treatment and Complications
- Type
- article
- Field-Weighted Citation Impact
- 2.78
Funders
- Chongqing Medical University
- Centre Scientifique et Technique du Bâtiment