Modeling Clinical Radiation Responses in the IMRT Era

17th International Conference on the Use of Computers in Radiation Therapy

物理学;计算机科学

Schwartz, J.L.^1 ; Murray, D.^2 ; Stewart, R.D.^1 ; Phillips, M.H.^1

University of Washington Medical Center, Seattle, WA, United States^1

Cross Cancer Institute, Edmonton, Alberta, Canada^2

关键词: Dose-response curves;    Dose-response models;    High-dose treatment;    Hypofractionation;    Linear-quadratic models;    Molecular analysis;    Radiation oncology;    Radiation response;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/489/1/012059/pdf
DOI  :  10.1088/1742-6596/489/1/012059

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

The purpose of this review is to highlight the critical issues of radiobiological models, particularly as they apply to clinical radiation therapy. Developing models of radiation responses has a long history that continues to the present time. Many different models have been proposed, but in the field of radiation oncology, the linear-quadratic (LQ) model has had the most impact on the design of treatment protocols. Questions have been raised as to the value of the LQ model given that the biological assumption underlying it has been challenged by molecular analyses of cell and tissue responses to radiation. There are also questions as to use of the LQ model for hypofractionation, especially for high dose treatments using a single fraction. While the LQ model might over-estimate the effects of large radiation dose fractions, there is insufficient information to fully justify the adoption of alternative models. However, there is increasing evidence in the literature that non-targeted and other indirect effects of radiation sometimes produce substantial deviations from LQ-like dose-response curves. As preclinical and clinical hypofractionation studies accumulate, new or refined dose-response models that incorporate high-dose/fraction non-targeted and indirect effects may be required, but for now the LQ model remains a simple, useful tool to guide the design of treatment protocols.