Radiomics machine learning based on asymmetrically prominent cortical and deep medullary veins combined with clinical features to predict prognosis in acute ischemic stroke: a retrospective study
Using radiomic methods, we propose a novel joint predictive model that combines the imaging histologic features of the APCV and DMV with clinical indicators. This model quantitatively characterizes the morphological and functional attributes of venous collateral circulation, elucidating its important role in accurately evaluating the prognosis of patients with AIS and providing a noninvasive and highly accurate imaging tool for early prognostic prediction.
Authors
- Shannan Chen (ORCID: https://orcid.org/0009-0001-0077-3172)
- Yu Lan (ORCID: https://orcid.org/0000-0003-0373-1944)
- Cancan Chang
- Ronghui Ju (ORCID: https://orcid.org/0009-0008-2284-6812)
- Shouliang Qi
- Bo Zhou (ORCID: https://orcid.org/0000-0002-2032-0584)
- Hongyi Li (ORCID: https://orcid.org/0000-0001-5507-0039)
- Yang Duan (ORCID: https://orcid.org/0000-0002-1522-0255)
- Peizhuo Zang
Institutions
- Dalian Medical University (CN)
- Liaoning Provincial People's Hospital (CN)
- Bozhou People's Hospital (CN)
- General Hospital of Shenyang Military Region (CN)
- Liaoning Cancer Hospital & Institute (CN)
- China Medical University (CN)
- Northeastern University (CN)
Publication Details
- Journal
- PeerJ
- Published
- 2025-06-06
- DOI
- https://doi.org/10.7717/peerj.19469
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00