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Image-driven Proliferation and Infiltration Rates Estimate of Pancreas Neuroendocrine Tumor

Thursday, October 11, 2012 — Poster Session III

10:00 a.m. – Noon

Natcher Conference Center, Building 45




  • Yixun Liu
  • Allis Weisbrod
  • Elect Kebebew
  • Ronal Summers
  • Jianh Yao


Pancreatic cancer is the fourth-leading cause of cancer death and one of the most aggressive malignancies in humans, with only a five-year relative survival rate. Quantitatively characterizing the tumor spatial-temporal progression is valuable in staging tumor and designing optimum treatment strategies. Tumors growth not only relies on the genetic and molecular properties of cancer cells, but also depends on dynamical interactions among the cancer cells, and between cells and the microenvironment. These dynamical interactions cannot be investigated purely through biological experiments due to the experimental complexity, which motives the study of the cancer system using a mathematical modeling. The parameters involved in the model vary from patient to patient, necessitating accuracy estimate based on patient specific data. In this work, we present a parameter estimate framework based on a cell population-based reaction-diffusion model and longitudinal pancreas neuroendocrine CT images. To estimate proliferation and infiltration rates in the reaction-diffusion model, Intracellular Space Fraction (ISF) is derived from pre- and post-CT images, and the original reaction-diffusion model is adapted to predict ISF rather than the size of cell population. The deviation between the real ISF and the predicted ISF is minimized, leading to an accurate estimate of the parameters.

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