Multimodal radiomics in glioma: predicting recurrence in the peritumoural brain zone using integrated MRI
The study highlights the potential of a multimodal radiomics approach for predicting glioma recurrence, with the fusion model serving as a robust tool for clinical decision-making.
Authors
- Min Jia (ORCID: https://orcid.org/0000-0003-3551-8654)
- Jing Yang (ORCID: https://orcid.org/0000-0002-0745-5011)
- Xianchun Zeng (ORCID: https://orcid.org/0000-0002-3035-2846)
- Chan Xiang (ORCID: https://orcid.org/0000-0003-1819-1645)
- Ang Liao
- Xiaofei Hu (ORCID: https://orcid.org/0000-0001-5376-2779)
- Lingheng Song (ORCID: https://orcid.org/0000-0002-7011-1262)
- Qian Li (ORCID: https://orcid.org/0000-0002-6581-7199)
- Kang Chen (ORCID: https://orcid.org/0000-0003-0745-7946)
- Yong Li (ORCID: https://orcid.org/0000-0003-3914-5840)
Institutions
- Army Medical University (CN)
- Chongqing University (CN)
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- Southwest Hospital (CN)
- The 309th Hospital of Chinese People's Liberation Army (CN)
- Guizhou Provincial People's Hospital (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- BMC Medical Imaging
- Published
- 2025-08-11
- DOI
- https://doi.org/10.1186/s12880-025-01853-4
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Chongqing