Constructing machine learning models based on non-contrast CT radiomics to predict hemorrhagic transformation after stoke: a two-center study
ML models based on clinical and NCCT radiomics characteristics can be used for early risk evaluation of HT development in AIS patients and show great potential for clinical precision in treatment and prognostic assessment.
Authors
- Wuli Tang
- Ling Yang (ORCID: https://orcid.org/0000-0001-5750-6588)
- Lingfeng Zhang (ORCID: https://orcid.org/0000-0003-4491-1554)
- Yue Zhang (ORCID: https://orcid.org/0000-0002-7786-0231)
- Kang Li (ORCID: https://orcid.org/0000-0002-8136-9816)
- Danni Wang (ORCID: https://orcid.org/0000-0003-3384-0287)
- Junlin Li (ORCID: https://orcid.org/0000-0003-4051-6805)
- Gang Xie
Institutions
- North Sichuan Medical University (CN)
- Chongqing University (CN)
- Third People's Hospital of Chengdu (CN)
- First People's Hospital of Chongqing (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2024-09-02
- DOI
- https://doi.org/10.3389/fneur.2024.1413795
- Citations
- 10
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 3.87