Leveraging immune and clinicopathological profiles with machine learning to predict axillary lymph node metastasis in breast cancer patients

BACKGROUND: . METHODS: ). Three machine learning models were developed using the Random Forest algorithm: Model 1 included clinicopathological data from Dataset 1; Model 2 used clinicopathological and immune response data from Dataset 1; and Model 3 used clinicopathological data from Dataset 2. All models followed the same machine learning pipeline, including data pre-processing, feature selection using recursive feature elimination with cross-validation, algorithm optimization using random search cross-validation, and results interpretability using Shapley additive explanations values. After selecting the best-performing model, Model 4 was developed using its dataset and features. The optimal feature set was determined at the point where adding more features led to a decline in model performance metrics. RESULTS: was the most predictive feature, even surpassing tumor diameter, a well-established prognostic factor in breast cancer. Thus, it might stand as a novel biomarker candidate. CONCLUSIONS: This study not only identifies promising biomarker candidates but also highlights the importance of including mechanistic features, such as mediating inflammation, in breast cancer patient stratification.