Artificial intelligence and multi-omics convergence in breast cancer: Revolutionizing diagnosis, prognostication, and precision oncology

Clinical Snapshot

Key Findings

• P Value: Not applicable - no statistical pooling

• Effect Size: Not quantified - narrative synthesis only

• Primary Outcome: Multi-omics AI integration shows promise for improved breast cancer diagnosis and prognosis but faces significant clinical translation barriers

• Nnt Or Sensitivity: Individual studies report improved accuracy over unimodal approaches but no pooled diagnostic performance metrics

• Confidence Interval: Not applicable - no meta-analysis performed

Clinical Application

Currently limited by technical barriers, data standardisation issues, and lack of prospective validation Relevant to Australian breast cancer care but implementation would require TGA approval for AI diagnostic tools and integration with existing pathology workflows. Cost-effectiveness analysis needed for PBS consideration. Women with breast cancer requiring diagnostic workup, prognostic stratification, or treatment selection

Abstract

Breast cancer (BC) is a highly heterogeneous malignancy and remains a major cause of cancer-related mortality among women worldwide. Advances in multi-omics profiling spanning genomics, transcriptomics, epigenomics, proteomics, and metabolomics have enabled finer subtype stratification and more comprehensive characterisation of tumour biology, thereby accelerating the discovery of diagnostic and prognostic biomarkers and actionable therapeutic targets. Nonetheless, translating multi-layer molecular signals into clinically robust decision support remains challenging because of the high dimensionality and heterogeneity of omics data, cross-cohort and cross-platform variability, and the fragmentation inherent to single-modality analyses. This review summarises how multi-omics studies have refined BC subtype definitions and advanced biomarker and target identification, and then synthesises recent progress in artificial intelligence, particularly deep learning, for integrating multi-omics with imaging, pathology, and clinical variables to improve diagnosis, risk stratification, prognosis prediction, and treatment response assessment. We critically examine representative multimodal integration frameworks and emerging deep learning architectures that learn both shared and modality-specific representations, which in many settings enable more accurate patient-level prediction than unimodal baselines. We further delineate key barriers to clinical translation, including cross-centre heterogeneity and inconsistent endpoint definitions, structural missingness of modalities in real-world workflows, inadequate cross-platform normalisation, limited interpretability and auditability, and a lack of prospective validation. Finally, we propose realistic next steps, including standardised and auditable preprocessing pipelines, missingness-aware fusion strategies, explainable and uncertainty-aware modelling, privacy-preserving multi-centre learning, and prospective, workflow-based evaluation. Collectively, these perspectives provide a roadmap for advancing multimodal AI-multi-omics integration toward reliable clinical deployment in BC management.

References

1. 1.Jiang, B., Wu, Y., Chen, X., Jian, C., & Wang, W. (2026). Artificial intelligence and multi-omics convergence in breast cancer: Revolutionizing diagnosis, prognostication, and precision oncology. Critical Reviews in Oncology/Hematology, 105160. https://doi.org/10.1016/j.critrevonc.2026.105160