Cell survival and radiosensitisation: modulation of the linear and quadratic parameters of the LQ model (Review).
نویسندگان
چکیده
The linear-quadratic model (LQ model) provides a biologically plausible and experimentally established method to quantitatively describe the dose-response to irradiation in terms of clonogenic survival. In the basic LQ formula, the clonogenic surviving fraction Sd/S₀ following a radiation dose d (Gy) is described by an inverse exponential approximation: Sd/S₀ = e-(αd+βd²), wherein α and β are experimentally derived parameters for the linear and quadratic terms, respectively. Radiation is often combined with other agents to achieve radiosensitisation. In this study, we reviewed radiation enhancement ratios of hyperthermia (HT), halogenated pyrimidines (HPs), various cytostatic drugs and poly(ADP-ribose) polymerase‑1 (PARP1) inhibitors expressed in the parameters α and β derived from cell survival curves of various mammalian cell cultures. A significant change in the α/β ratio is of direct clinical interest for the selection of optimal fractionation schedules in radiation oncology, influencing the dose per fraction, dose fractionation and dose rate in combined treatments. The α/β ratio may increase by a mutually independent increase of α or decrease of β. The results demonstrated that the different agents increased the values of both α and β. However, depending on culture conditions, both parameters can also be separately influenced. Moreover, it appeared that radiosensitisation was more effective in radioresistant cell lines than in radiosensitive cell lines. Furthermore, radiosensitisation is also dependent on the cell cycle stage, such as the plateau or exponentially growing phase, as well as on post-treatment plating conditions. The LQ model provides a useful tool in the quantification of the effects of radiosensitising agents. These insights will help optimize fractionation schedules in multimodality treatments.
منابع مشابه
Accelerated proliferation correction factors in linear-quadratic and multiple-component models
Background: Study in design to incorporate accelerated proliferation correction factors into linearquadratic and multiple-component models. Materials and Methods: Accelerated proliferation rate correction factor has been incorporated into the linearquadratic and the multiple component models by applying accelerated exponential cell growth to explain the tumor cell kinetics and estimates proper ...
متن کاملAssessment of in vitro radiosensitivity parameters of breast cancer cells following exposure to radiotherapy hospital-based facilities
Introduction: The aim of the present study was to assess the radiosensitivity parameters for SK-BR-3 (SKBR3) breast cancer cells that could be implemented in the cutting-edge treatment planning systems (TPS) for accelerated partial-breast irradiation (APBI). Materials and Methods: The cell survival fraction and its relevant radiosensitivity coefficients, namely α and β, in linear-quadratic (LQ)...
متن کاملThe Role of radiobiological parameters on Tumor control probability (TCP) in prostate cancer
Introduction: The aim of this study was to evaluation radiobiological modeling parameters on tumor control probability (TCP) for prostate cancer in three different models. These parameters included α⁄β ratios and cell surviving fraction at 2 Gy (SF2). Materials and Methods: The Poisson, equivalent uniform dose (EUD) and linear quadratic (LQ) models was used as...
متن کاملOn the analysis of clonogenic survival data: Statistical alternatives to the linear-quadratic model
BACKGROUND The most frequently used method to quantitatively describe the response to ionizing irradiation in terms of clonogenic survival is the linear-quadratic (LQ) model. In the LQ model, the logarithm of the surviving fraction is regressed linearly on the radiation dose by means of a second-degree polynomial. The ratio of the estimated parameters for the linear and quadratic term, respecti...
متن کاملComparison of Kullback-Leibler, Hellinger and LINEX with Quadratic Loss Function in Bayesian Dynamic Linear Models: Forecasting of Real Price of Oil
In this paper we intend to examine the application of Kullback-Leibler, Hellinger and LINEX loss function in Dynamic Linear Model using the real price of oil for 106 years of data from 1913 to 2018 concerning the asymmetric problem in filtering and forecasting. We use DLM form of the basic Hoteling Model under Quadratic loss function, Kullback-Leibler, Hellinger and LINEX trying to address the ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- International journal of oncology
دوره 42 5 شماره
صفحات -
تاریخ انتشار 2013