A machine learning-driven prognostic model based on peripheral blood lymphocyte subsets in osteosarcoma

Background: The prognosis of osteosarcoma (OS) remains heterogeneous, and the prognostic value of peripheral blood lymphocyte subsets, analyzed through machine learning (ML), is not fully explored. This study aimed to develop an ML-based prognostic model using lymphocyte subset data to improve risk stratification for OS patients. Methods: We retrospectively analyzed data from 65 high-grade OS patients. Peripheral blood lymphocyte subsets were quantified by flow cytometry prior to treatment. Seven algorithms, including stepwise Cox, LASSO, and five ML models (RSF, GBM, XGBoost, SVM, KNN), were employed to construct prognostic models. Model performance was evaluated using the C-index, AUC, and validated via bootstrap and cross-validation. Results: activated cytotoxic T cells (AUC = 0.959). The resulting gbm_risk_score was an independent prognostic factor (HR = 14.516, P = 0.012) and effectively stratified patients into significantly divergent survival groups (P