Isavand Pouria, Aghamiri Sara Sadat, Amin Rada
Department of Radiology, School of Medicine, Zanjan University of Medical Sciences, Zanjan 4513956184, Iran.
Department of Biochemistry, University of Nebraska, Lincoln, NE 68503, USA.
Biomedicines. 2024 Aug 5;12(8):1753. doi: 10.3390/biomedicines12081753.
Given advancements in large-scale data and AI, integrating multimodal artificial intelligence into cancer research can enhance our understanding of tumor behavior by simultaneously processing diverse biomedical data types. In this review, we explore the potential of multimodal AI in comprehending B-cell non-Hodgkin lymphomas (B-NHLs). B-cell non-Hodgkin lymphomas (B-NHLs) represent a particular challenge in oncology due to tumor heterogeneity and the intricate ecosystem in which tumors develop. These complexities complicate diagnosis, prognosis, and therapy response, emphasizing the need to use sophisticated approaches to enhance personalized treatment strategies for better patient outcomes. Therefore, multimodal AI can be leveraged to synthesize critical information from available biomedical data such as clinical record, imaging, pathology and omics data, to picture the whole tumor. In this review, we first define various types of modalities, multimodal AI frameworks, and several applications in precision medicine. Then, we provide several examples of its usage in B-NHLs, for analyzing the complexity of the ecosystem, identifying immune biomarkers, optimizing therapy strategy, and its clinical applications. Lastly, we address the limitations and future directions of multimodal AI, highlighting the need to overcome these challenges for better clinical practice and application in healthcare.
鉴于大规模数据和人工智能的进展，将多模态人工智能整合到癌症研究中，可以通过同时处理多种生物医学数据类型来增强我们对肿瘤行为的理解。在本综述中，我们探讨了多模态人工智能在理解B细胞非霍奇金淋巴瘤（B-NHL）方面的潜力。B细胞非霍奇金淋巴瘤（B-NHL）由于肿瘤异质性以及肿瘤发生的复杂生态系统，在肿瘤学中是一个特殊的挑战。这些复杂性使诊断、预后和治疗反应变得复杂，强调需要使用复杂的方法来加强个性化治疗策略以获得更好的患者预后。因此，可以利用多模态人工智能从可用的生物医学数据（如临床记录、成像、病理学和组学数据）中综合关键信息，以描绘整个肿瘤。在本综述中，我们首先定义了各种类型的模态、多模态人工智能框架以及在精准医学中的一些应用。然后，我们提供了其在B-NHL中的几个使用示例，用于分析生态系统的复杂性、识别免疫生物标志物、优化治疗策略及其临床应用。最后，我们讨论了多模态人工智能的局限性和未来方向，强调了克服这些挑战以在医疗保健中实现更好的临床实践和应用的必要性。
