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

Intracranial metastatic disease (IMD) is a devastating complication of cancer associated with high morbidity and mortality. Patients with breast and lung cancer have a particularly high risk of developing IMD. Early identification of individuals with breast or lung cancer at high risk for IMD development would enable targeted surveillance and timely intervention. In this study, we leverage machine learning (ML) algorithms to develop and validate predictive models for IMD risk using a population-based dataset of 143,341 patients with breast or lung cancer from Ontario, Canada, collected from 2010 to 2023. Our ML models outperform traditional statistical paradigms, demonstrating strong discriminative ability in predicting both global and five-year risk of IMD with area under the precision-recall curve values ranging from 0.75 to 0.85. We further employed Shapley Additive exPlanations analysis to elucidate the key predictors of IMD; histology, laterality and age emerged as significant factors for patients with breast cancer while tumour site, histology and sex predicted IMD among patients with lung cancer. These findings underscore the potential of ML algorithms to bolster personalised risk stratification and enable targeted surveillance for IMD in patients with metastatic cancer.