Machine learning algorithms and web-based prognostic tool for different histological subtypes of osteosarcoma: a retrospective cohort
Machine learning models, particularly XGBoost for regression and Logistic Regression for classification, demonstrate strong potential for predicting survival outcomes in OSC patients. These findings underscore the utility of machine learning in enhancing clinical decision-making and suggest avenues for future research, including incorporating additional clinical variables and advanced modeling techniques to improve long-term survival predictions.
Authors
- Hassan Elbahri (ORCID: https://orcid.org/0000-0002-3056-4419)
- Leen Albraik
- Abdullah M Alharran (ORCID: https://orcid.org/0009-0003-2337-6588)
- Muteb N. Alotaibi
- Ohood Yahya Alasmari
- Ibrahim Saleh Allehaimeed
- Layan Albraik
- Ali M AlMaazmi
- Fahad A Alsaid
Institutions
- Princess Nourah bint Abdulrahman University (SA)
- Alfaisal University (SA)
- Royal College of Surgeons in Ireland (IE)
- Qassim University (SA)
- Buraydah Colleges (SA)
- Arabian Gulf University (BH)
Publication Details
- Journal
- Annals of Medicine and Surgery
- Published
- 2025-11-25
- DOI
- https://doi.org/10.1097/ms9.0000000000004433
- Primary Topic
- Sarcoma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00