A multiregional multimodal machine learning model for predicting outcome of surgery for symptomatic hemorrhagic brainstem cavernous malformations
Inclusion of BSCM and brainstem subregion imaging data in machine learning models yielded significant predictive capability for unfavorable postoperative outcomes. The integration of specific clinical features enhanced prediction accuracy.
Authors
- Kai Quan
- Jiejie Zhou (ORCID: https://orcid.org/0000-0002-4299-8046)
- Xiaolei Lin (ORCID: https://orcid.org/0000-0003-2463-1272)
- Wei Zhu (ORCID: https://orcid.org/0000-0001-7190-7476)
- Shaoxuan Yang
- Mingjian Liu (ORCID: https://orcid.org/0000-0002-1055-2138)
- Yunzhen Zhao (ORCID: https://orcid.org/0000-0002-0349-7257)
- Lin Wang (ORCID: https://orcid.org/0000-0002-6449-4930)
- Jun Dong
- Zongze Li (ORCID: https://orcid.org/0000-0002-0149-6971)
- Haojing Duan
- Xuchen Dong
- Xiao Ying
- Yu Ma (ORCID: https://orcid.org/0009-0008-4395-1240)
- Dongdong Wang (ORCID: https://orcid.org/0009-0009-6101-7504)
- Haohuai Gui
Institutions
- Fudan University (CN)
- Soochow University (CN)
- Shanghai Institute for Science of Science (CN)
- Second Affiliated Hospital of Soochow University (CN)
- Shanghai Clinical Research Center (CN)
- Second Affiliated Hospital of Zhejiang University (CN)
- Huashan Hospital (CN)
- Shanghai Center for Brain Science and Brain-Inspired Technology (CN)
Publication Details
- Journal
- Neurosurgical FOCUS
- Published
- 2025-07-01
- DOI
- https://doi.org/10.3171/2025.4.focus24778
- Citations
- 2
- Primary Topic
- Vascular Malformations Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 2.78