Noninvasive prediction of failure of the conservative treatment in lateral epicondylitis by clinicoradiological features and elbow MRI radiomics based on interpretable machine learning: a multicenter cohort study
We developed and validated an interpretable LightGBM machine learning model that integrates clinicoradiological and radiomic features to predict the failure of conservative treatment in LE. The model demonstrates high predictive accuracy and offers valuable insights into key prognostic factors.
Authors
- Yuming Yin (ORCID: https://orcid.org/0000-0002-2854-921X)
- Rongjie Bai (ORCID: https://orcid.org/0009-0007-9184-4039)
- Ping Zhang (ORCID: https://orcid.org/0000-0002-3907-1127)
- Ping Wang (ORCID: https://orcid.org/0000-0002-1557-0394)
- Xiaodong Zhang (ORCID: https://orcid.org/0000-0002-0535-6466)
- Jianing Cui
Institutions
- Capital Medical University (CN)
- Peking University (CN)
- Beijing Jishuitan Hospital (CN)
- Beijing Geriatric Hospital (CN)
- Third Affiliated Hospital of Southern Medical University (CN)
- Pomona Valley Hospital Medical Center (US)
Publication Details
- Journal
- Journal of Orthopaedic Surgery and Research
- Published
- 2025-05-24
- DOI
- https://doi.org/10.1186/s13018-025-05901-1
- Citations
- 1
- Primary Topic
- Tendon Structure and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.72
Funders
- National Natural Science Foundation of China