Drs Qi Wang and Yi Sun and six other institutions (IBM, Mayo Clinc, Yale University, University of Oslo, UMass at Amherst, and U of South Florida) were awarded an NCI/NIH grant to conduct a preliminary study on "Dynamic Multiscale Digital Twin for a Nonsmall Cell Lung Cancer Patient" for 6 months. Qi Wang is the PI. In this project a dynamic, multiscale digital twin (DT) that will be used to search for the optimized treatment pathway for a non-small cell lung cancer (NSCLC) patient by advancing the hypothesis that optimal pathways for a specific cancer patient can be selected by exploring the treatment pathway space through a dynamical, multiscale digital twin derived by harnessing patient’s own data and leveraging data from similar patients in the population. The digital twin represents a virtual copy of the NSCLC patient at any given time in the past and in the future. We take a novel approach by using a high performance computing infrastructure to build an in-depth virtual patient model leveraging multimodal patient data across observational and treatment scales as an initial digital twin. We then leverage data from similar patients in the population to simulate various nuanced treatment choices in a treatment pathway graph to form recommendations. The digital twin multiscale dynamical modeling and simulation will be developed using the latest advances in artificial intelligence (AI), deep learning, and physics-informed modeling.