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Purpose <p>Lung metastasis in breast cancer (BCLM) is a critical determinant of poor prognosis, occurring in approximately 30–50% of advanced cases and associated with significantly reduced median survival. This study aimed to develop machine learning models for predicting BCLM and evaluating prognosis using the SEER database.</p> Methods <p>Data from the SEER database (2018–2021) were analyzed. For the prediction model, 11 independent predictors were identified via univariate and multivariate logistic regression. Machine learning models were developed and evaluated using AUC, accuracy, precision, specificity, recall, F-score. The prognostic model incorporated 12 features through Cox regression, via a nomogram, and validated by C-index, calibration plots, decision curve analysis (DCA), and integrated discrimination improvement (IDI).</p> Results <p>Among 124,505 patients, 168 (0.135%) developed lung metastasis. Multivariate logistic analysis identified HR-/HER2- subtype (OR = 2.701, 95% CI 1.614–4.52) and brain metastasis (OR = 11.088, 95% CI 3.518–34.946) as independent high-risk factors. The LR-based prediction model demonstrated the highest discriminative ability among the evaluated individual models, achieving an AUC of 0.947 (95% CI 0.902–0.977), sensitivity of 0.816, specificity of 0.911, and an F-score of 0.024. Given the extremely low incidence of lung metastasis (0.135%), the low F-score mainly reflected the limited positive predictive value inherent to rare-event prediction scenarios. An online tool (<a href="https://9um39fycfyx4icd6cs8gcw.streamlit.app/">https://9um39fycfyx4icd6cs8gcw.streamlit.app/</a>) was deployed for risk assessment. 12 factors confirmed by multivariate COX regression were incorporated the nomogram. The prognostic model achieved a C-index of 0.79 (se = 0.009), with 1-year and 3-year survival AUCs of 0.86 and 0.62. The 1-year calibration plots showed high consistency between predicted and observed survival (mean absolute error = 0.001; 0.9 quantile error = 0.003). DCA and IDI confirmed improved clinical net benefits compared to traditional TNM models.</p> Conclusion <p>This study identified key risk factors for BCLM and developed prediction and prognosis models that may assist population-level risk stratification. However, given the rare-event nature of lung metastasis, the prediction model should be interpreted cautiously and is more suitable for risk assessment rather than individual-level screening or diagnostic replacement.</p>

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Development of machine learning and nomogram models to predict lung metastasis and prognosticate survival in breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月20日

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  • Rong Bai,
  • Yukai Zeng,
  • Fengwu Lin,
  • Lening Zhang

摘要

Purpose

Lung metastasis in breast cancer (BCLM) is a critical determinant of poor prognosis, occurring in approximately 30–50% of advanced cases and associated with significantly reduced median survival. This study aimed to develop machine learning models for predicting BCLM and evaluating prognosis using the SEER database.

Methods

Data from the SEER database (2018–2021) were analyzed. For the prediction model, 11 independent predictors were identified via univariate and multivariate logistic regression. Machine learning models were developed and evaluated using AUC, accuracy, precision, specificity, recall, F-score. The prognostic model incorporated 12 features through Cox regression, via a nomogram, and validated by C-index, calibration plots, decision curve analysis (DCA), and integrated discrimination improvement (IDI).

Results

Among 124,505 patients, 168 (0.135%) developed lung metastasis. Multivariate logistic analysis identified HR-/HER2- subtype (OR = 2.701, 95% CI 1.614–4.52) and brain metastasis (OR = 11.088, 95% CI 3.518–34.946) as independent high-risk factors. The LR-based prediction model demonstrated the highest discriminative ability among the evaluated individual models, achieving an AUC of 0.947 (95% CI 0.902–0.977), sensitivity of 0.816, specificity of 0.911, and an F-score of 0.024. Given the extremely low incidence of lung metastasis (0.135%), the low F-score mainly reflected the limited positive predictive value inherent to rare-event prediction scenarios. An online tool (https://9um39fycfyx4icd6cs8gcw.streamlit.app/) was deployed for risk assessment. 12 factors confirmed by multivariate COX regression were incorporated the nomogram. The prognostic model achieved a C-index of 0.79 (se = 0.009), with 1-year and 3-year survival AUCs of 0.86 and 0.62. The 1-year calibration plots showed high consistency between predicted and observed survival (mean absolute error = 0.001; 0.9 quantile error = 0.003). DCA and IDI confirmed improved clinical net benefits compared to traditional TNM models.

Conclusion

This study identified key risk factors for BCLM and developed prediction and prognosis models that may assist population-level risk stratification. However, given the rare-event nature of lung metastasis, the prediction model should be interpreted cautiously and is more suitable for risk assessment rather than individual-level screening or diagnostic replacement.