AI-driven spatial multimodal 3D and 4D molecular tumor modeling: innovations and applications (Invited Paper)

In this talk I willl introduce AI-driven approaches to spatial multimodal 3D and 4D molecular tumor modeling, which integrates Holotomography, spatial transcriptomics, proteomics, and in situ molecular imaging. This innovative methodology allows for the comprehensive characterization of the tumor microenvironment (TME) at single-cell and subcellular levels. By leveraging AI and machine learning algorithms, we analyze petabyte-scale molecular data to identify novel biomarkers and therapeutic targets, enhance patient stratification, and develop new therapeutic modalities including CAR-T cell, ADC, and immunotherapy and combinatory therapies. These advancements have resulted in significant progress in understanding the interactions of cellular and non-cellular components within the TME and overcoming its barriers. This presentation will discuss the methodologies employed, key findings, and the potential implications for cancer treatment.