Radiomics and deep learning for intracranial aneurysm rupture-status assessment on CTA/MRA: a multicenter retrospective study
Tao Wang, Jiang Fan, Yi He, Changren Huang, Kunyang Bao · 2026
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Description
Wang and co-authors fuse radiomics and deep learning to judge whether a brain aneurysm has burst. Burst aneurysms cause severe bleeding around the brain. Most past scan studies sort burst from unburst cases after the fact, not future risk.
The study screened 520 patients with 572 aneurysms across centers. After quality checks, 400 patients with 400 aneurysms were kept and split into training and test sets. From each scan, 1,316 radiomic traits were pulled, then cut to 42 stable ones. A 3D ResNet added deep features, squeezed to 128 parts. Both fed an XGBoost classifier as 170 inputs.
Catalog record
| CREATOR | Tao Wang, Jiang Fan, Yi He, Changren Huang, Kunyang Bao |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 81 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |