Spatial radiomics-based interpretable multimodal machine learning model enhances outcomes prediction for minor stroke: A multicenter cohort study
The spatial radiomics-based interpretable model significantly improved the accuracy of predicting unfavorable outcomes in minor stroke. Furthermore, integrating spatial radiomics enhanced conventional radiomics model and introduced a novel approach within the spatial-omics family.
Authors
- Z. H. Zhang (ORCID: https://orcid.org/0009-0004-2733-7970)
- Xiangming Fang (ORCID: https://orcid.org/0000-0001-9922-8977)
- Changjie Pan (ORCID: https://orcid.org/0000-0002-7455-1636)
- XinDao Yin
- Zehong Cao
- Feng Shi
- Hongjie Huang
- Shukui Wang
- Wusheng Zhu
- Qiang Xu
- Jing Ye
- Liang Jiang
- Changcheng Li
- Zhen Jiang
- Dante Mantini
- Guangming Lu
- Yi Zhou
- Caifeng Guo
Institutions
- Xuzhou Medical College (CN)
- Soochow University (CN)
- Second Affiliated Hospital of Soochow University (CN)
- United Imaging Healthcare (China) (CN)
- Northern Jiangsu People's Hospital (CN)
- Second Affiliated Hospital of Nanjing Medical University (CN)
- Second Affiliated Hospital of Xuzhou Medical College (CN)
- Changzhou No.2 People's Hospital (CN)
- Wuxi People's Hospital (CN)
- Nanjing Medical University (CN)
- Nanjing University (CN)
- KU Leuven (BE)
Publication Details
- Journal
- Journal of Advanced Research
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1016/j.jare.2025.12.047
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36