University of Verona, Department of Computer Science, 37134 Verona, Italy.
Trento Institute for Fundamental Physics and Applications (TIFPA), 38123 Povo, Trento, Italy.
Radiat Res. 2022 Mar 1;197(3):218-232. doi: 10.1667/RADE-21-00098.1.
The current article presents the first application of the Generalized Stochastic Microdosimetric Model (GSM2) for computing explicitly a cell survival curve. GSM2 is a general probabilistic model that predicts the kinetic evolution of DNA damages taking full advantage of a microdosimetric description of a radiation energy deposition. We show that, despite the high generality and flexibility of GSM2, an explicit form for the survival fraction curve predicted by the GSM2 is achievable. We illustrate how several correction terms typically added a posteriori in existing radiobiological models to improve the prediction accuracy, are naturally included into GSM2. Among the most relevant features of the survival curve derived from GSM2 and presented in this article, is the linear-quadratic behavior at low doses and a purely linear trend for high doses. The study also identifies and discusses the connections between GSM2 and existing cell survival models, such as the Microdosimetric Kinetic Model (MKM) and the Multi-hit model. Several approximations to predict cell survival in different irradiation regimes are also introduced to include intercellular non-Poissonian behaviors.
本文首次应用广义随机微剂量模型（GSM2）来明确计算细胞存活曲线。GSM2 是一种通用的概率模型，它充分利用辐射能量沉积的微剂量描述来预测 DNA 损伤的动力学演化。我们表明，尽管 GSM2 具有很高的通用性和灵活性，但可以获得 GSM2 预测的存活分数曲线的显式形式。我们说明了在现有的放射生物学模型中通常事后添加的几个校正项如何自然地包含在 GSM2 中。本文提出的源自 GSM2 的存活曲线的最相关特征之一是在低剂量下的线性二次行为和在高剂量下的纯线性趋势。该研究还确定并讨论了 GSM2 与现有的细胞存活模型（如微剂量动力学模型（MKM）和多击中模型）之间的联系。还引入了几种预测不同辐照模式下细胞存活的近似值，以包括细胞间非泊松行为。
