MRI-based deep learning with clinical and imaging features to differentiate medulloblastoma and ependymoma in children
T2-weighted MRI-based DL combined with multimodal clinical and imaging features can be used to effectively differentiate MB from EM in children. Thus, the structure of the decision tree in the decision tree classifier is expected to greatly assist clinicians in daily practice.
Authors
- Parhat Yasin
- Mayidili Nijiati (ORCID: https://orcid.org/0000-0002-1346-6921)
- Xiaoguang Zou (ORCID: https://orcid.org/0000-0001-9960-5015)
- Chencui Huang (ORCID: https://orcid.org/0000-0003-3307-6872)
- Abudouresuli Tuersun
- Yasen Yimit
- Yue Hao (ORCID: https://orcid.org/0000-0001-7876-8878)
- Yunling Wang (ORCID: https://orcid.org/0009-0006-4779-4671)
- Ya Qiu (ORCID: https://orcid.org/0009-0004-9233-8996)
Institutions
- Xinjiang Medical University (CN)
- First Affiliated Hospital of Xinjiang Medical University (CN)
- Beijing Academy of Artificial Intelligence (CN)
- Center for High Pressure Science & Technology Advanced Research (CN)
- Sixth Affiliated Hospital of Xinjiang Medical University (CN)
- Second Affiliated Hospital of Xinjiang Medical University (CN)
Publication Details
- Journal
- Frontiers in Molecular Biosciences
- Published
- 2025-04-28
- DOI
- https://doi.org/10.3389/fmolb.2025.1570860
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36