Radiomics-based machine learning model for predicting clinically ineffective reperfusion in acute ischaemic stroke patients after endovascular treatment
The machine learning model based on intrathrombus and perithrombus radiomics features can accurately predict clinically ineffective reperfusion in patients after EVT. However, further study is needed to validate these findings in larger, independent cohorts and explore the broader clinical applicability of the model.
Authors
- Suya Li (ORCID: https://orcid.org/0000-0002-1388-1308)
- Liming Peng (ORCID: https://orcid.org/0000-0001-9891-9581)
- Shifei Ye
- Zhiliang Ding (ORCID: https://orcid.org/0000-0002-4402-6646)
- Xiao‐Long Hu (ORCID: https://orcid.org/0009-0007-5617-4746)
- Yibin Fang (ORCID: https://orcid.org/0000-0002-6773-4733)
Institutions
- Tongji University (CN)
- Suzhou Municipal Hospital (CN)
- Shanghai Tenth People's Hospital (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-09-18
- DOI
- https://doi.org/10.3389/fneur.2025.1606287
- Citations
- 1
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- National Natural Science Foundation of China