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Background <p>Intracranial metastatic disease is a severe complication of cancer that confers substantial morbidity and mortality. Patients with breast or lung cancer are at particularly elevated risk of IMD. Early identification of individuals at increased risk could enable targeted surveillance and timely intervention.</p> Methods <p>We developed interpretable machine-learning competing-risk models to estimate the risk of intracranial metastatic disease among patients with breast or lung cancer. For each cancer type, cause-specific Cox models were combined via the Aalen–Johansen estimator to produce absolute risk estimates at one, three, and five years.</p> Results <p>Here we show high test set discrimination for intracranial metastatic disease (Uno’s C-index: breast 0.95; lung 0.88) and favorable time-dependent precision–recall performance (AUPRC(t) at 1/3/5 years: breast 0.17/0.53/0.63; lung 0.37/0.61/0.64). Decision-curve analysis across relevant thresholds demonstrates greater net clinical benefit than baseline strategies. Model interpretability analysis identifies cancer stage as the dominant determinant in both cancers; in breast cancer, triple-negative and HER2-positive subtypes contribute additional risk, whereas in lung cancer, histology and tumor size are prominent contributors.</p> Conclusions <p>Machine-learning based competing-risk survival models offer greater insight into prognostication of intracranial metastatic disease than baseline strategies. These findings support the potential of such models to strengthen personalized risk stratification and guide targeted surveillance for Intracranial metastatic disease among patients with breast or lung cancer.</p>

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Machine learning identifies prognosticators of intracranial metastatic disease in patients with breast or lung cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月23日

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  • Marco V. Istasy,
  • Amol Verma,
  • Katarzyna J. Jerzak,
  • Sunit Das

摘要

Background

Intracranial metastatic disease is a severe complication of cancer that confers substantial morbidity and mortality. Patients with breast or lung cancer are at particularly elevated risk of IMD. Early identification of individuals at increased risk could enable targeted surveillance and timely intervention.

Methods

We developed interpretable machine-learning competing-risk models to estimate the risk of intracranial metastatic disease among patients with breast or lung cancer. For each cancer type, cause-specific Cox models were combined via the Aalen–Johansen estimator to produce absolute risk estimates at one, three, and five years.

Results

Here we show high test set discrimination for intracranial metastatic disease (Uno’s C-index: breast 0.95; lung 0.88) and favorable time-dependent precision–recall performance (AUPRC(t) at 1/3/5 years: breast 0.17/0.53/0.63; lung 0.37/0.61/0.64). Decision-curve analysis across relevant thresholds demonstrates greater net clinical benefit than baseline strategies. Model interpretability analysis identifies cancer stage as the dominant determinant in both cancers; in breast cancer, triple-negative and HER2-positive subtypes contribute additional risk, whereas in lung cancer, histology and tumor size are prominent contributors.

Conclusions

Machine-learning based competing-risk survival models offer greater insight into prognostication of intracranial metastatic disease than baseline strategies. These findings support the potential of such models to strengthen personalized risk stratification and guide targeted surveillance for Intracranial metastatic disease among patients with breast or lung cancer.