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.