title: Patient specific tumor growth prediction using multimodal images.
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 proposed Reaction-Advection-Diffusion model is capable of integrating cancerous cell proliferation, infiltration, metabolic rate and extracellular matrix biomechanical response. To bridge the model with multimodal imaging data, we introduce Intracellular Volume Fraction (ICVF) measured from dual-phase CT and Standardized Uptake Value (SUV) measured from FDG-PET into the model. The patient specific model parameters are estimated by fitting the model to the observation, which leads to an inverse problem formalized as a coupled Partial Differential Equations (PDE)-constrained optimization problem. The optimality system is derived and solved by the Finite Difference Method. The model was evaluated by comparing the predicted tumors with the observed tumors in terms of average surface distance (ASD), root mean square difference (RMSD) of the ICVF map, average ICVF difference (AICVFD) of tumor surface and tumor relative volume difference (RVD) on six patients with pathologically confirmed pancreatic neuroendocrine tumors. The ASD between the predicted tumor and the reference tumor was 2.4±0.5mm, the RMSD was 4.3±0.4%, the AICVFD was 2.6±0.6%, and the RVD was 7.7±1.3%.
authors.affiliation: Clinical Image Processing Service, Radiology and Imaging Sciences, NIH, United States.
authors.forename: Yixun
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Endocrine Oncology Branch, National Cancer Institute, NIH, United States.
authors.forename: Samira M
authors.initials: SM
authors.lastname: Sadowski
authors.affiliation: Endocrine Oncology Branch, National Cancer Institute, NIH, United States.
authors.forename: Allison B
authors.initials: AB
authors.lastname: Weisbrod
authors.affiliation: Endocrine Oncology Branch, National Cancer Institute, NIH, United States.
authors.forename: Electron
authors.initials: E
authors.lastname: Kebebew
authors.affiliation: Clinical Image Processing Service, Radiology and Imaging Sciences, NIH, United States.
authors.forename: Ronald M
authors.initials: RM
authors.lastname: Summers
authors.affiliation: Clinical Image Processing Service, Radiology and Imaging Sciences, NIH, United States. Electronic address: JYao@cc.nih.gov.
authors.forename: Jianhua
authors.initials: J
authors.lastname: Yao
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D055088:Early Detection of Cancer / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008137:Longitudinal Studies; D064847:Multimodal Imaging / Q000379:methods*; D018358:Neuroendocrine Tumors / Q000473:pathology*; D010190:Pancreatic Neoplasms / Q000175:diagnosis*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011379:Prognosis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D047368:Tumor Burden
keywords: Intracellular Volume Fraction; Metabolic rate; Multimodal images; Tumor growth modeling