Department of Radiology and Center for Cell Signaling, New Jersey Medical School, Rutgers University, Newark, New Jersey, USA.
Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Med Phys. 2024 Jan;51(1):637-649. doi: 10.1002/mp.16650. Epub 2023 Aug 21.
Predicting biological responses to mixed radiation types is of considerable importance when combining radiation therapies that use multiple radiation types and delivery regimens. These may include the use of both low- and high-linear energy transfer (LET) radiations. A number of theoretical models have been developed to address this issue. However, model predictions do not consistently match published experimental data for mixed radiation exposures. Furthermore, the models are often computationally intensive. Accordingly, there is a need for efficient analytical models that can predict responses to mixtures of low- and high-LET radiations. Additionally, a general formalism to calculate equieffective dose (EQDX) for mixed radiations is needed.
To develop a computationally efficient analytical model that can predict responses to complex mixtures of low- and high-LET radiations as a function of either absorbed dose or EQDX.
The Zaider-Rossi model (ZRM) was modified by replacing the geometric mean of the quadratic coefficients in the interaction term with the arithmetic mean. This modified ZRM model (mZRM) was then further generalized to any number of radiation types and its validity was tested against published experimental observations. Comparisons between the predictions of the ZRM and mZRM, and other models, were made using two and three radiation types. In addition, a generalized formalism for calculating EQDX for mixed radiations was developed within the context of mZRM and validated with published experimental results.
The predictions of biological responses to mixed-LET radiations calculated with the mZRM are in better agreement with experimental observations than ZRM, especially when high- and low-LET radiations are mixed. In these situations, the ZRM overestimated the surviving fraction. Furthermore, the EQDX calculated with mZRM are in better agreement with experimental observations.
The mZRM is a computationally efficient model that can be used to predict biological response to mixed radiations that have low- and high-LET characteristics. Importantly, interaction terms are retained in the calculation of EQDX for mixed radiation exposures within the mZRM framework. The mZRM has application in a wide range of radiation therapies, including radiopharmaceutical therapy.
当结合使用多种辐射类型和治疗方案的放射疗法时,预测混合辐射类型对生物反应具有重要意义。这些可能包括使用低和高线性能量传递(LET)辐射。已经开发了许多理论模型来解决这个问题。然而,模型预测并不总是与混合辐射暴露的已发表实验数据一致。此外,这些模型通常计算量很大。因此,需要能够预测低和高 LET 辐射混合物反应的高效分析模型。此外,还需要一种通用的形式来计算混合辐射的等效剂量(EQDX)。
开发一种计算效率高的分析模型,能够预测低和高 LET 混合辐射的复杂混合物作为吸收剂量或 EQDX 的函数的反应。
通过用交互项中的二次系数的算术平均值替换几何平均值,对 Zaider-Rossi 模型(ZRM)进行了修改。然后,将修改后的 ZRM 模型(mZRM)进一步推广到任意数量的辐射类型,并根据已发表的实验观察结果对其有效性进行了测试。使用两种和三种辐射类型比较了 ZRM 和 mZRM 以及其他模型的预测。此外,在 mZRM 的背景下开发了用于计算混合辐射 EQDX 的通用形式,并使用已发表的实验结果进行了验证。
用 mZRM 计算的混合 LET 辐射生物反应的预测与实验观察结果更一致,尤其是当高和低 LET 辐射混合时。在这些情况下,ZRM 高估了存活分数。此外,mZRM 计算的 EQDX 与实验观察结果更一致。
mZRM 是一种计算效率高的模型,可用于预测具有低和高 LET 特性的混合辐射的生物反应。重要的是,在 mZRM 框架内计算混合辐射暴露的 EQDX 时保留了相互作用项。mZRM 在广泛的放射治疗中具有应用,包括放射性药物治疗。