Multimodal Image Driven Pancreatic Tumor Growth Prediction - Abstract
Personalized tumor growth model is valuable in tumor staging and therapy planning. In this paper, we present a patient specific tumor growth model based on longitudinal multimodal imaging data including dual-phase CT and FDG-PET. The model was evaluated by comparing the predicted tumors with the observed tumors in terms of intracellular volume fraction of tumor surface on six patients with pathologically confirmed pancreatic neuroendocrine tumors, and the results demonstrated the promise of the proposed method.