Artificial Intelligence-Driven Precision Medicine: Multi-Omics and Spatial Multi-Omics Approaches in Diffuse Large B-Cell Lymphoma (DLBCL).

Abstract

In this comprehensive review, we delve into the transformative role of artificial intelligence (AI) in refining the application of multi-omics and spatial multi-omics within the realm of diffuse large B-cell lymphoma (DLBCL) research. We scrutinized the current landscape of multi-omics and spatial multi-omics technologies, accentuating their combined potential with AI to provide unparalleled insights into the molecular intricacies and spatial heterogeneity inherent to DLBCL. Despite current progress, we acknowledge the hurdles that impede the full utilization of these technologies, such as the integration and sophisticated analysis of complex datasets, the necessity for standardized protocols, the reproducibility of findings, and the interpretation of their biological significance. We proceeded to pinpoint crucial research voids and advocated for a trajectory that incorporates the development of advanced AI-driven data integration and analytical frameworks. The evolution of these technologies is crucial for enhancing resolution and depth in multi-omics studies. We also emphasized the importance of amassing extensive, meticulously annotated multi-omics datasets and fostering translational research efforts to connect laboratory discoveries with clinical applications seamlessly. Our review concluded that the synergistic integration of multi-omics, spatial multi-omics, and AI holds immense promise for propelling precision medicine forward in DLBCL. By surmounting the present challenges and steering towards the outlined futuristic pathways, we can harness these potent investigative tools to decipher the molecular and spatial conundrums of DLBCL. This will pave the way for refined diagnostic precision, nuanced risk stratification, and individualized therapeutic regimens, ushering in a new era of patient-centric oncology care.