ArticleClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico2026
A highly interpretable machine learning model for predicting lung cancer bone metastasis: uncovering the synergistic effect of routine biochemical markers.
Article in Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 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
backgroundBone metastasis (BM) significantly impairs lung cancer prognosis and patient quality of life. Conventional imaging modalities often face limitations in early detection and cost-effectiveness. This study aimed to develop and validate an interpretable machine learning (ML) model using routine, cost-effective biochemical markers for the early, non-invasive prediction of BM.
methodsThis retrospective study included 566 lung cancer patients. Clinicopathological and laboratory features such as alkaline phosphatase (ALP), D-dimer, and lactate dehydrogenase (LDH) were collected. The dataset was partitioned into training and independent test sets. Six ML algorithms were evaluated using cross-validation, with the gradient boosting decision tree (GBDT) identified as the optimal model. Robustness and transparency were rigorously assessed via SHAP analysis, 1000 bootstrap resamples, and multi-dimensional subgroup analyses.
resultsALP, D-dimer, and LDH were significantly elevated in BM( +) patients (P < 0.001). In the test set, GBDT (gradient boosting decision tree) achieved an overall AUC of 0.774 (95% CI: 0.721-0.827) and an F1-score of 0.762. After subgroup integration, predictive performance improved to an AUC of 0.811 (95% CI 0.752-0.870), significantly outperforming traditional logistic regression (AUC = 0.755). Peak performance was observed in lung adenocarcinoma (AUC = 0.864). SHAP analysis quantitatively revealed a synergistic, non-linear interaction between ALP and D-dimer as a primary, quantifiable driver of BM risk.
conclusionOur routine-marker-based ML model demonstrates high diagnostic accuracy and robust generalizability. By precisely identifying high-risk populations with high transparency, this cost-effective tool provides scientific decision support for implementing personalized bone scan screening strategies and optimizing resource allocation in clinical practice.
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