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Effect of Hypoxic Volume On Tumor Control Probability in Hypoxia Targeted Dose Escalation

A Chvetsov*, J Rajendran , J Zeng , University of Washington School of Medicine, Seattle, WA

Presentations

(Sunday, 7/29/2018) 3:00 PM - 6:00 PM

Room: Exhibit Hall

Purpose: Dose painting techniques based on dose escalation to the hypoxic regions are usually governed by a map of Oxygen Enhancement Ratio (OER) derived from PET or MRI images. The goal of this article is to show that the relative hypoxic volume is also a parameter which affects the radiobiological effectiveness of dose escalation to the hypoxic regions.

Methods: Our analysis is done using the equation for Tumor Control Probability (TCP) derived from Poisson statistics. A tumor response model with two levels of oxygenated and hypoxic cells with the survival curves described by the Linear Quadratic (LQ) model is used. We introduce a parameter called average cell survival for a tumor which can be directly related to TCP. Dependence of average cell survival on the relative hypoxic volume and dose escalation is computed for a conventional dose regimen 30x2Gy in a model problem with radiobiological parameters for non-small cell lung cancer.

Results: We show that equal average cell survival and the TCP respectively for smaller relative hypoxic volumes can be achieved with smaller dose escalation for practically used dose escalation less than 50% of fractional dose. It is explained by the dependence of average cell survival only on the cellular response in the hypoxic volume. Smaller hypoxic volumes have smaller number of clonoges and smaller doses are needed to eradicate them.

Conclusion: The results of this research can be used for the dose de-escalation strategies in dose painting techniques which may reduce dose to normal tissue and normal tissue complication respectively without deterioration of tumor control if the hypoxic tumor fraction is small.

Keywords

Hypoxia, Radiobiology, Tumor Control

Taxonomy

TH- Radiobiology(RBio)/Biology(Bio): RBio- LQ/TCP/NTCP/outcome modeling

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