Deciding between different treatment modalities such as surgery, chemotherapy, radiation therapy, targeted treatment or immunotherapy is an almost insurmountable challenge for medical oncologists treating cancer patients.

We propose to develop a Digital Twin (DT) by integrating baseline multi-modal data and repeated measurements for real-time dynamic model training and updating. We will use innovations in cancer research, artificial intelligence, and computing technologies to realize a DT to help decide treatment strategies.

More specifically, we propose an adaptive dynamic DT to predict response to initial therapy as well as predicting resistance and enable rapid and effective treatment reassignment. This DT will help physicians make initial treatment determinations, monitor treatment response and effectiveness, and decide when to discontinue or change approach.