ORIGINAL RESEARCH article
Front. Physiol.
Sec. Clinical and Translational Physiology
Efficacy Analysis and Survival Prediction of Unique Chemotherapy Regimens for Osteosarcoma in China
Provisionally accepted
- Fourth Medical Center of PLA General Hospital, Beijing, China
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Objective: This study aimed to evaluate the effectiveness of a unique chemotherapy regimen, identify factors influencing overall survival (OS), and compare the predictive performance of six machine learning models in Chinese osteosarcoma patients. Methods: A retrospective analysis was conducted on 390 osteosarcoma patients treated between 2009 and 2019. All patients received standardized neoadjuvant chemotherapy (Ifosfamide + Methotrexate + Adriamycin or Ifosfamide + Adriamycin + Cisplatin depending on age) and subsequent surgery. Clinical and pathological data were collected. Survival analysis was performed using Kaplan-Meier curves and Log-rank tests. Multivariate analysis and survival prediction were conducted using Cox proportional hazards models and six machine learning algorithms (Random Forest (RF), AdaBoost, CatBoost, Extra Trees, XGBoost, and LightGBM) validated via 5-fold cross-validation. Clinical net benefit was assessed using Decision Curve Analysis (DCA). Results: The cohort had a mean age of 19 years, with 62.47% male and 88.82% diagnosed as stage II. The 3-year and 5-year survival rates were 76.00% (95% CI: 71.60%-80.40%) and 65.00% (95% CI: 60.20%-69.80%), respectively. Multiple factors—including tumor type, surgical method, recurrence/metastasis, tumor necrosis rate, and serum biomarkers (LDH, ALP, PLT, WBC, RBC)—were significantly associated with OS. Among the machine learning models, RF and Extra Trees demonstrated the highest predictive accuracy (AUC = 0.960), followed by CatBoost (0.942), AdaBoost (0.897), LightGBM (0.879), and XGBoost (0.853). Calibration curves showed excellent agreement between predicted and observed survival probabilities. DCA confirmed that RF and Extra Trees provided superior net benefit across a wide range of threshold probabilities. Conclusions: The unique chemotherapy regimen showed superior survival outcomes. Prognostic evaluation should integrate multiple clinical and pathological indicators. Machine learning models, particularly RF and Extra Trees, offer powerful tools for individualized survival prediction and treatment planning in osteosarcoma.
Keywords: data-centric AI, effectiveness, Osteosarcoma, Real-world data, Survival Prediction
Received: 26 Aug 2025; Accepted: 09 Jan 2026.
Copyright: © 2026 Wang, Bao, Wang, Wang and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Wei Wang
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