Integrating Spatial Omics with Biobank Consent Metadata for Breast Cancer Risk Prediction: Interpretability, Bias, and Real-World Performance

Mugisha Emmanuel K.

Faculty of Science and Technology Kampala International University Uganda

ABSTRACT

The integration of spatial omics with biobank consent metadata represents a novel and promising approach to improving breast cancer risk prediction in the era of precision medicine. This study explores how high-dimensional spatial transcriptomics capturing the molecular architecture of the tumor microenvironment can be combined with structured consent metadata to enhance predictive accuracy, interpretability, and fairness. By leveraging biobank-derived consent information, the framework not only enables compliance with ethical and legal standards but also provides a mechanism for identifying and mitigating biases embedded within heterogeneous datasets. The proposed methodology employs integrative machine learning architectures capable of handling multimodal data, while maintaining separation between spatial omics and metadata to preserve data integrity and privacy. Key challenges addressed include model interpretability, bias across demographic groups, and real-world generalizability. Validation using large-scale biobank datasets and external cohorts demonstrates the potential of this approach to improve risk stratification and clinical decision-making. Despite limitations related to data quality, harmonization, and regulatory constraints, the study underscores the importance of ethical governance and continuous performance monitoring. Ultimately, integrating spatial omics with consent-aware metadata offers a scalable and equitable pathway toward more robust and clinically relevant breast cancer risk prediction models.

Keywords: Spatial Omics, Biobank Consent Metadata, Breast Cancer Risk Prediction, Model Interpretability and Algorithmic Bias. https://doi.org/10.59298/ROJESR/2026/5.11800