ReviewFrontiers in molecular biosciences2026
AI-driven multi-omics integration in breast cancer: clinical applications, immunotherapy prediction, and translational challenges.
Review in Frontiers in molecular biosciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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Abstract
Breast cancer remains a major global threat to women's health, and its marked molecular, spatial, and temporal heterogeneity continues to limit the accuracy of early diagnosis, risk stratification, and treatment-response prediction. Conventional single-dimensional diagnostic and therapeutic models are often insufficient to capture the dynamic complexity of the tumor microenvironment (TME). In this context, artificial intelligence (AI) provides powerful tools for high-dimensional feature extraction, multimodal data integration, and cross-scale modeling of heterogeneous biological and clinical information. AI-enabled approaches can link microscopic molecular alterations with macroscopic imaging and pathological phenotypes, while also characterizing complex cellular interactions within the TME. This review summarizes current applications of AI-driven multimodal and multi-omics integration in breast cancer, focusing on early detection, precision diagnosis, immunotherapy-response prediction, drug-sensitivity assessment, and prognostic evaluation. We also discuss key translational challenges, including data heterogeneity, batch effects, interpretability, privacy protection, regulatory considerations, and clinical workflow integration. Overall, AI-driven multi-omics strategies offer a promising framework for improving individualized treatment selection and real-time monitoring in breast cancer, although robust prospective validation and multidisciplinary implementation are still required before routine clinical adoption. Compared with previous modality-specific reviews, this article emphasizes cross-modal evidence appraisal, clinical-maturity stratification, data-quality constraints, foundation model opportunities, and regulatory requirements for translating AI-enabled multi-omics from exploratory research to clinically auditable decision support.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.