Deep learning-based survival prediction model for adult diffuse low-grade glioma: a multi-cohort validation study
Our deep learning model demonstrates reliable prognostic capabilities for DLGG patients, with age and IDH status as key determinants of survival. The model has been implemented as a web-based platform ( https://seerlggs-f4nze5jr7iuu9k9uuaemjf.streamlit.app ) for clinical use, offering personalized survival predictions. These findings contribute to more precise prognostication and may aid in treatment strategy optimization for DLGG patients.
Authors
- Haibo Su (ORCID: https://orcid.org/0000-0003-2619-9276)
- Tang Ye (ORCID: https://orcid.org/0000-0002-8704-465X)
- Peng‐Fei Xu (ORCID: https://orcid.org/0000-0003-3073-9108)
- Tao Wu (ORCID: https://orcid.org/0000-0002-9859-4534)
- Baodong Chen (ORCID: https://orcid.org/0000-0002-4647-0089)
- Wenxin Liu (ORCID: https://orcid.org/0000-0002-3869-6520)
- Guangyuan Wu (ORCID: https://orcid.org/0009-0003-4689-4097)
Institutions
- Peking University Shenzhen Hospital (CN)
- University of Hong Kong - Shenzhen Hospital (CN)
Publication Details
- Journal
- Discover Oncology
- Published
- 2025-10-02
- DOI
- https://doi.org/10.1007/s12672-025-03613-w
- Citations
- 2
- Primary Topic
- Glioma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 3.25
Funders
- Peking University