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Tumor Control Probability Models for Hypofractionated Therapy: Known Unknowns, Unknown Unknowns, and Comparison with Data


J Deasy


J Deasy1*, J Jeong2, (1) Memorial Sloan Kettering, NEW YORK, NY, (2) University of Missouri, Columbia, MO.

TH-A-BRB-3 Thursday 8:00:00 AM - 9:55:00 AM Room: Ballroom B

In order to better understand factors that affect tumor dose response to hypofractionated and standard fractionation (~ 2 Gy/day), we recently constructed a state-based model to predict radiotherapy tumor outcomes. Put simply the model assumes that each small volume of the tumor at the beginning of therapy (a ‘tumorlet’) has a given, unchanging, supply level of oxygen and glucose. Cells are divided into those that have both adequate oxygen and adequate glucose (and are therefore at least intermittently proliferating), cells that only have adequate glucose (radiobiologically hypoxic but surviving), and those that are dying due to a lack of oxygen and glucose. The model is just complicated enough to take into account measured cell loss factors, growth fractions, and radiosensitivity as a function of cell cycle. The model naturally reproduces the effects of reoxygenation, accelerated clonogen proliferation, and regression as a function of the local tumorlet cell-loss factor and growth fraction. Although the model appears to reproduce behavior consistent with response to standard fractionation, it does not adequately explain response to single- or few-fraction delivery, where hypoxic cell survival is predicted to be orders-of-magnitude too high. It is unlikely that a too-high value of alpha/beta by itself explains the discrepancy as the ratio is expected to increase under hypoxic conditions by a factor equal to the oxygen enhancement ratio. These results are in agreement with previously published calculations by Brown et al. (IJROBP (2010) 78: 323-327) using a different model of tumor response. We will discuss uncertainties in the model, including effects that have been left out, such as variations in vascular delivery.

Learning objectives:
1. Understanding factors expected to impact radiotherapy response, including radiosensitivity, repair, reoxygenation, the cell loss factor, the growth fraction, and hypoxia.
2. Understand basic mathematical formulations that can capture radiosensitivity and repair.


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