ArticleFrontiers in immunology2026
A machine learning-driven prognostic model based on peripheral blood lymphocyte subsets in osteosarcoma.
Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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: The Gradient Boosting Machine (GBM) algorithm yielded the optimal two-variable model, incorporating CD3 Conclusion: We developed and validated a robust ML-driven prognostic model based on peripheral blood lymphocyte subsets. This model, demonstrating superior prognostic value over conventional inflammatory markers, provides a novel and practical tool for personalized risk assessment in OS, potentially guiding more tailored treatment strategies.
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