The essence of precision oncology lies in its commitment to tailor targeted treatments and care measures to each patient based on the individual characteristics of the tumor. The inherent heterogeneity of tumors necessitates gathering information from diverse data sources to provide valuable insights from various perspectives, fostering a holistic comprehension of the tumor. Over the past decade, multimodal data integration technology for precision oncology has made significant strides, showcasing remarkable progress in understanding the intricate details within heterogeneous data modalities. These strides have exhibited tremendous potential for improving clinical decision-making and model interpretation, contributing to the advancement of cancer care and treatment. Given the rapid progress that has been achieved, we provide a comprehensive overview of about 300 papers detailing cutting-edge multimodal data integration techniques in precision oncology. In addition, we conclude the primary clinical applications that have reaped significant benefits, including early assessment, diagnosis, prognosis, and biomarker discovery. Finally, derived from the findings of this survey, we present an in-depth analysis that explores the pivotal challenges and reveals essential pathways for future research in the field of multimodal data integration for precision oncology.

翻译：精准肿瘤学的核心在于根据每位患者肿瘤的个体特征，为其量身定制靶向治疗方案和护理措施。肿瘤固有的异质性要求从多种数据源收集信息，以提供多角度的宝贵见解，从而促进对肿瘤的整体理解。过去十年间，用于精准肿瘤学的多模态数据整合技术取得了显著进展，在理解异质数据模态内复杂细节方面展现出卓越成就。这些进展在改善临床决策与模型可解释性方面显示出巨大潜力，为癌症护理与治疗的进步作出了贡献。鉴于已取得的快速进展，本文对约300篇论文进行了全面综述，详细阐述了精准肿瘤学中前沿的多模态数据整合技术。此外，我们总结了已获得显著效益的主要临床应用领域，包括早期评估、诊断、预后及生物标志物发现。最后，基于本综述的研究发现，我们进行了深入分析，探讨了多模态数据整合在精准肿瘤学领域面临的关键挑战，并揭示了未来研究的重要路径。