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A Study of Reoxygenation of Cancer Cells After Application of Radiation of Different Sized Doses Using a Diffusion Model

Y Watanabe, E Dahlman*, University of Minnesota, Minneapolis, MN


(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose: Determine how the distribution of oxygen changes within a simulated tumor region before and after radiation therapy. As the oxygen enhancement ratio (OER) is an important factor in the efficacy of radiation therapy, understanding how oxygen diffuses within a tumor after treatment could help plan better multiple fraction treatments.

Methods: A two-dimensional diffusion model for the partial oxygen pressure (pO2) within a simulated 400x400 micron tumor region was developed. Blood vessels running perpendicular to the tumor region at the corners were used to supply oxygen to the tumor. Each live tumor cell consumed available oxygen at a constant rate. This model was solved using an alternating direction implicit Crank-Nicolson method using periodic boundary conditions. The radiation effect was simulated by calculating the survival fraction of a representative dose using the linear-quadratic model. Irradiated cells, which died instantly, had their oxygen consumption rate set to zero. Average pO2 of all live cells was compared after a steady-state solution was reached before and after radiation application.

Results: Four radiation doses were simulated: 0.65, 1.8, 3.6, and 6.7 Gy. For each dose value, average pO2 reached a steady state solution within 50-100 seconds after radiation. Larger doses resulted in a higher average pO2 within the surviving cells even if one of the blood vessels was destroyed by the radiation. For 0.65 and 1.8 Gy doses, this average pO2 value was lower than before radiation. For 3.6 and 6.7 Gy doses, average pO2 values were higher than pre-radiation values.

Conclusion: Partial oxygen pressures within a tumor reach a steady-state very rapidly after the delivery of radiation. Delivery of larger doses results in higher average partial oxygen pressure within the remaining surviving cells. This could be exploited to enhance OER’s for future treatments.

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Diffusion, Hypoxia, Modeling


TH- Response Assessment: Modeling: other than machine learning

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