Multimodal deep learning-based radiomics for meningioma consistency prediction: integrating T1 and T2 MRI in a multi-center study
Our study introduced and evaluated a deep learning radiomics model (DLR-Model) to accurately predict the consistency of meningiomas, which has the potential to improve preoperative assessments and surgical planning.
Authors
- Fan Yang (ORCID: https://orcid.org/0000-0001-6861-9596)
- Yubiao Yue (ORCID: https://orcid.org/0000-0001-6348-555X)
- Huanjie Lin
- Bingbing Chen (ORCID: https://orcid.org/0000-0001-5995-4464)
- Huai Chen (ORCID: https://orcid.org/0000-0001-7650-289X)
- Weifeng Li (ORCID: https://orcid.org/0000-0002-3555-5992)
- Qinrong Zhang (ORCID: https://orcid.org/0000-0001-8893-514X)
- Li Xie (ORCID: https://orcid.org/0000-0001-6605-2274)
Institutions
- Second Affiliated Hospital of Guangzhou Medical University (CN)
- General Hospital of Guangzhou Military Command (CN)
- Gannan Medical University (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- BMC Medical Imaging
- Published
- 2025-07-01
- DOI
- https://doi.org/10.1186/s12880-025-01787-x
- Citations
- 1
- Primary Topic
- Meningioma and schwannoma management
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- Guangzhou Medical University
- Natural Science Foundation of Guangdong Province