Prediction of High-Dose Methotrexate Blood Concentration in Osteosarcoma Patients Using Machine Learning
This machine learning framework addresses a critical gap in high-dose methotrexate therapeutic monitoring by achieving early and personalized blood drug concentration prediction, allowing for personalized dosing of patients based on predicted concentrations. The interpretability of SHAP-derived feature importance enhances clinical utility, offering a paradigm shift from reactive toxicity management to proactive precision dosing in osteosarcoma therapy.
Authors
- Li Su (ORCID: https://orcid.org/0000-0001-6789-8618)
- Shuyu Dai (ORCID: https://orcid.org/0000-0001-6384-8437)
- Baowanze Zhang
- Na Liu (ORCID: https://orcid.org/0000-0003-1432-3858)
- Jiali He (ORCID: https://orcid.org/0000-0001-7468-9996)
- Jin Zhao (ORCID: https://orcid.org/0009-0004-4912-4079)
Institutions
- Qujing Normal University (CN)
- Liaoning Cancer Hospital & Institute (CN)
- China Medical University (CN)
Publication Details
- Journal
- Drug Design Development and Therapy
- Published
- 2025-05-01
- DOI
- https://doi.org/10.2147/dddt.s515535
- Citations
- 5
- Primary Topic
- Systemic Sclerosis and Related Diseases
- Type
- article
- Field-Weighted Citation Impact
- 7.36