Development of the interpretable typing prediction model for osteosarcoma and chondrosarcoma based on machine learning and radiomics: a multicenter retrospective study
This study confirms the effectiveness of combining machine learning with radiomic features in improving the accuracy and interpretability of osteosarcoma and chondrosarcoma diagnosis. The excellent performance of the RF model is particularly suitable for complex imaging data processing, providing valuable insights for the future.
Authors
- B Ban (ORCID: https://orcid.org/0000-0002-4621-6785)
- Qing-Yuan Long
- Bo Gao (ORCID: https://orcid.org/0000-0003-2703-7244)
- Chuan Zhang (ORCID: https://orcid.org/0000-0003-4736-1658)
- Yue Hu (ORCID: https://orcid.org/0000-0002-3846-9096)
- Xiaobin Tian (ORCID: https://orcid.org/0009-0004-5083-0587)
- Fengyan Wang
Institutions
- Guiyang Medical University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Affiliated Hospital of Guizhou Medical University (CN)
- Guang’anmen Hospital (CN)
- Qiannan Normal College For Nationalities (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2024-11-20
- DOI
- https://doi.org/10.3389/fmed.2024.1497309
- Citations
- 8
- Primary Topic
- Cancer-related molecular mechanisms research
- Type
- article
- Field-Weighted Citation Impact
- 2.28
Funders
- Guizhou Science and Technology Department