Jiang Bitao, Wu Yuefei, Chen Xiao, Jian Chunyan, Wang Wenjuan
Department of Hematology and Oncology, Beilun Branch of the First Affiliated Hospital, College of Medicine, Zhejiang University, Ningbo, China; Department of Hematology and Oncology, Beilun People's Hospital, Ningbo, China.
Radiotherapy Department, The Second People's Hospital of Wuhu, Wuhu, China.
Crit Rev Oncol Hematol. 2026 Apr;220:105160. doi: 10.1016/j.critrevonc.2026.105160. Epub 2026 Jan 29.
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.
乳腺癌（BC）是一种高度异质性的恶性肿瘤，仍然是全球女性癌症相关死亡的主要原因。涵盖基因组学、转录组学、表观基因组学、蛋白质组学和代谢组学的多组学分析技术的进步，使得肿瘤生物学的亚型分层更加精细，特征描述更加全面，从而加速了诊断和预后生物标志物以及可操作治疗靶点的发现。尽管如此，由于组学数据的高维度和异质性、跨队列和跨平台的变异性以及单模态分析固有的碎片化问题，将多层分子信号转化为临床稳健的决策支持仍然具有挑战性。本综述总结了多组学研究如何完善乳腺癌亚型定义以及推进生物标志物和靶点识别，然后综合了人工智能，特别是深度学习在将多组学与影像学、病理学和临床变量整合以改善诊断、风险分层、预后预测和治疗反应评估方面的最新进展。我们批判性地审视了代表性的多模态整合框架和新兴的深度学习架构，这些架构能够学习共享的和特定模态的表示，在许多情况下，与单模态基线相比，能够实现更准确的患者水平预测。我们进一步阐述了临床转化的关键障碍，包括跨中心异质性和终点定义不一致、现实世界工作流程中模态的结构缺失、跨平台标准化不足、可解释性和可审计性有限以及缺乏前瞻性验证。最后，我们提出了切实可行的下一步措施，包括标准化和可审计的预处理流程、缺失值感知融合策略、可解释和不确定性感知建模、隐私保护的多中心学习以及基于工作流程的前瞻性评估。总体而言，这些观点为推进多模态人工智能 - 多组学整合在乳腺癌管理中实现可靠的临床应用提供了路线图。
