Advances in quantitative biomarker development have accelerated new forms of data-driven insights for patients with cancer. However, most approaches are limited to a single mode of data, leaving integrated approaches across modalities relatively underdeveloped. Multimodal integration of advanced molecular diagnostics, radiological and histological imaging, and codified clinical data presents opportunities to advance precision oncology beyond genomics and standard molecular techniques. However, most medical datasets are still too sparse to be useful for the training of modern machine learning techniques, and significant challenges remain before this is remedied. Combined efforts of data engineering, computational methods for analysis of heterogeneous data and instantiation of synergistic data models in biomedical research are required for success. In this Perspective, we offer our opinions on synthesizing complementary modalities of data with emerging multimodal artificial intelligence methods. Advancing along this direction will result in a reimagined class of multimodal biomarkers to propel the field of precision oncology in the coming decade.

定量生物标志物开发的进展加速了对癌症患者的新形式的数据驱动见解。然而，大多数方法仅限于单一模式的数据，跨模式的集成方法相对不发达。先进分子诊断、放射学和组织学成像以及编码临床数据的多模式集成为推进超越基因组学和标准分子技术的精准肿瘤学提供了机会。然而，大多数医学数据集仍然过于稀疏，无法用于现代机器学习技术的训练，在解决这一问题之前仍存在重大挑战。数据工程、异构数据分析的计算方法和生物医学研究中协同数据模型的实例化是成功的必要条件。在这个观点中，我们就使用新兴的多模态人工智能方法合成互补的数据模态提供了我们的意见。沿着这个方向前进将产生一种重新构想的多模式生物标志物类别，以在未来十年推动精准肿瘤学领域。