Background:The combination of multi-omics data, including genomics, transcriptomics, and epigenomics, with medical imaging modalities (PET, CT, MRI, histopathology) has emerged in recent years as a promising direction for the advancement of precision oncology. Many researchers have contributed to this domain, exploring the multi-modality aspect of using both multi-omics and image data for better cancer identification, subtype classifications, cancer prognosis, etc.Methods:We present an umbrella review summarizing the state of the art in fusing imaging modalities with omics and artificial intelligence, focusing on existing reviews and meta-analyses. The analysis highlights early, late, and hybrid fusion strategies and their advantages and disadvantages, mainly in tumor classification, prognosis, and treatment prediction. We searched review articles until 25 May 2025 across multiple databases following PRISMA guidelines, with registration on PROSPERO (CRD420251062147).Results:After identifying 56 articles from different databases (i.e., PubMed, Scopus, Web of Science and Dimensions.ai), 35 articles were screened out based on the inclusion and exclusion criteria, keeping 21 studies for the umbrella review.Discussion:We investigated prominent fusion techniques in various contexts of cancer types and the role of machine learning in model performance enhancement. We address the problems of model generalizability versus interpretability within the clinical context and argue how these multi-modal issues can facilitate translating research into actual clinical scenarios.Conclusions:Lastly, we recommend future work to define clearer and more reliable validation criteria, address the need for integration of human clinicians with the AI system, and describe the trust issue with AI in cancer care, which requires more standardized approaches.
背景：近年来，将多组学数据（包括基因组学、转录组学和表观基因组学）与医学影像模态（PET、CT、MRI、组织病理学）相结合，已成为推动精准肿瘤学发展的前沿方向。众多研究者致力于该领域，探索利用多组学与影像数据的多模态特性，以提升癌症识别、亚型分类及预后评估等方面的效能。 方法：本文通过伞状综述系统梳理了影像模态与组学及人工智能融合领域的最新进展，重点关注现有综述与荟萃分析。研究系统分析了早期融合、晚期融合及混合融合策略在肿瘤分类、预后评估和治疗预测中的优势与局限。我们遵循PRISMA指南，在PROSPERO平台（注册号CRD420251062147）完成注册，并检索了截至2025年5月25日多个数据库中的相关综述文献。 结果：通过检索PubMed、Scopus、Web of Science及Dimensions.ai等数据库，初步筛选出56篇文献，依据纳入与排除标准排除35篇，最终纳入21项研究进行伞状综述分析。 讨论：我们深入探讨了不同癌症类型背景下主流的融合技术，以及机器学习对模型性能提升的作用。针对临床实践中模型泛化性与可解释性之间的平衡问题，本文分析了多模态研究方法如何促进科研成果向真实临床场景的转化。 结论：最后，我们建议未来研究应建立更清晰可靠的验证标准，加强人工智能系统与临床医生的协同整合，并针对肿瘤诊疗中人工智能的信任问题提出更标准化的解决方案。
肿瘤(癌症)患者之家