Toward Foundation Models in Oncology: BCP-HyEnS: A Scalable Hybrid Ensemble Integrating Biomarkers and Explainability for Breast Cancer Diagnosis and Treatment

The emergence of large-scale foundation models in artificial intelligence promises to revolutionize disease diagnosis and treatment planning, however their translation to clinical practice faces fundamental challenges: interpretability, biomarker integration, and regulatory readiness. Addressing these gaps, we present BCP-HyEnS (Breast Cancer Predictor-Hybrid Ensemble System), a foundation model-inspired architecture that combines a foundation model-inspired architecture that combines the scalability of large ensemble methods with clinically mandated transparency. The Key novelty lies in harmonizing large-scale ensemble learning with biomarker-driven interpretability-a hybrid framework that achieves state-of-art performance while maintaining full clinical transparency, unlike black-box deep learning systems. Our Model integrates clinically validated cytological biomarkers-including characteristics such as radius, texture, perimeter, area, and so-on, with in a hybrid framework of SVM, XGBoost, and Logistic Regression. This design preserves biological relevance while achieving the scale necessary for generalizable disease diagnosis. To ensure clinical trust, we implement SHAP and LIME for per-case interpretability, enabling clinicians to validate predictions against established cytopathological knowledge, on the WBCD dataset, our model achieved exceptional performance (AUC-ROC:0.994, Sensitivity:98.6%, Specificity:95.2%) with sub-millisecond inference, reducing false negatives critical for early intervention, beyond diagnostic accuracy, the framework supports treatment decision making by linking biomarker profiles to prediction path-ways, with interpretable architecture, our framework represents a scalable step toward clinically viable foundation models in oncology, demonstrating how Large-scale AI can be harmonized with interpretability demands of precision medicine.