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Background <p>Bone 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.</p> Methods <p>This 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.</p> Results <p>ALP, D-dimer, and LDH were significantly elevated in BM( +) patients (<i>P</i> &lt; 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.</p> Conclusion <p>Our 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.</p>

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A highly interpretable machine learning model for predicting lung cancer bone metastasis: uncovering the synergistic effect of routine biochemical markers

  • 期刊论文

  • 发表日期：

    2026年6月22日

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  • Zi-Feng Jiang,
  • Zhang-Yan Ke,
  • Min Wang,
  • Jin-Bao Fu,
  • Yan-Bei Zhang

摘要

Background

Bone 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.

Methods

This 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.

Results

ALP, 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.

Conclusion

Our 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.