(Sunday, 7/29/2018) 3:00 PM - 6:00 PM
Room: Exhibit Hall
Purpose: Traditional linear quadratic (LQ) model (Lea, 1942) describes cell survive curve under irradiation. However, since dose per fraction escalates in hypo-fractionation radiotherapy, several extended LQ models were proposed to evaluate the biological effective dose. The aim of this study is to summarize these extended LQ models and compare their characteristics.
Methods: Five extended LQ models, including KN (Kavanagh, 2008), MA-LQ (McKenna, 2008), g-LQ (Wang, 2010), USC (Park, 2008) and LQ-L (Astrahan, 2008), were enrolled, which have been proposed since 2008. The fitting characteristics of each model to different cell lines were summarized in terms of parameters fitting method, model structures, adaptive cell lines type and accuracy at describing cell survival curves.
Results: Five extended LQ models were separated into two categories according to their characteristics: In the first category, the cell survival curves were fitted with a single equation during the whole dose range, including KN, MA-LQ and g-LQ model. These models parameters can be estimated with less iterations during the fitting optimization process. Furthermore, these models have better continuity, smoothness and sturdy theoretical basis but simple calculation formula. In the second category, the cell survival curves were divided into low dose and high dose regions, which are described with two equations respectively, including USC and LQ-L model. These models provide higher fitting accuracy and better adaptability to different cell lines.
Conclusion: All of the five extended LQ models present higher accuracy at describing cell survival curves in hypo-fractionation compared to the traditional LQ model. The USC model is superior to others in terms of fitting accuracy and capability to adapt different cell lines with sturdier theoretical basis and more simple equations for convenient clinical application.
Keywords
Bioeffect Dose, Linear Quadratic Model, Bipartition Models
Taxonomy
IM- Radiation dose and risk: Models
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