Transformer-based multimodal fusion framework for predicting postoperative cognitive improvement in glioma: integrating radiomics and pathomics
The Transformer-based multimodal fusion framework provides an effective solution for predicting postoperative cognitive improvement in glioma patients, demonstrating superior performance against conventional methods. By bridging computational innovation with clinical practice, this approach presents a promising proof-of-concept for a multimodal framework that could, upon future prospective validation, serve as a tool for optimizing rehabilitation strategies and improving functional independence in glioma survivors.
Authors
- Jay J. Park (ORCID: https://orcid.org/0000-0001-8762-6986)
- Jingyou Li (ORCID: https://orcid.org/0000-0003-1159-8821)
- Bowen Chang (ORCID: https://orcid.org/0000-0003-2829-8761)
- Yang Lü (ORCID: https://orcid.org/0000-0002-9425-2967)
- Anli Zhang (ORCID: https://orcid.org/0000-0001-7847-6541)
- Yang Yang (ORCID: https://orcid.org/0000-0001-8857-3679)
- Nannan Zhang (ORCID: https://orcid.org/0000-0003-0582-8934)
- Jingjing Wu (ORCID: https://orcid.org/0000-0002-6579-6738)
Institutions
- University of Science and Technology of China (CN)
- Palo Alto University (US)
- Yangtze University (CN)
- Anhui Medical University (CN)
- Stanford Medicine (US)
- Second Affiliated Hospital of Anhui Medical University (CN)
- Stanford University (US)
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2025-12-17
- DOI
- https://doi.org/10.1097/js9.0000000000004453
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00