Abstract
Personalized tumor growth model using clinical imaging data is valuable in tumor staging and therapy planning. In this paper, we build a patient specific tumor growth model based on longitudinal dual phase CT and FDG-PET. We propose a reaction-advection-diffusion model integrating cancerous cell proliferation, infiltration, metabolic rate and extracellular matrix biomechanical response. We then develop a scheme to bridge our model with multimodal radiologic images through intracellular volume fraction (ICVF) and Standardized Uptake Value (SUV). 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, the average ICVF difference (AICVFD) of tumor surface and the 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 2.5±0.7 mm, the RMSD was 4.3±0.6%, the AICVFD was 2.6±0.8%, and the RVD was 7.7±1.9%.
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Keywords
- Standardize Uptake Value
- Root Mean Square Difference
- Pancreatic Neuroendocrine Tumor
- Tumor Surface
- Glucose Metabolic Rate
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Liu, Y., Sadowski, S.M., Weisbrod, A.B., Kebebew, E., Summers, R.M., Yao, J. (2013). Multimodal Image Driven Patient Specific Tumor Growth Modeling. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40760-4_36
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DOI: https://doi.org/10.1007/978-3-642-40760-4_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40759-8
Online ISBN: 978-3-642-40760-4
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