Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden.
Technol Cancer Res Treat. 2013 Apr;12(2):183-92. doi: 10.7785/tcrt.2012.500306. Epub 2012 Oct 19.
For many years the linear-quadratic (LQ) model has been widely used to describe the effects of total dose and dose per fraction at low-to-intermediate doses in conventional fractionated radiotherapy. Recent advances in stereotactic radiosurgery (SRS) and stereotactic radiotherapy (SRT) have increased the interest in finding a reliable cell survival model, which will be accurate at high doses, as well. Different models have been proposed for improving descriptions of high dose survival responses, such as the Universal Survival Curve (USC), the Kavanagh-Newman (KN) and several generalizations of the LQ model, e.g. the Linear-Quadratic-Linear (LQL) model and the Pade Linear Quadratic (PLQ) model. The purpose of the present study is to compare a number of models in order to find the best option(s) which could successfully be used as a fractionation correction method in SRT. In this work, six independent experimental data sets were used: CHOAA8 (Chinese hamster fibroblast), H460 (non-small cell lung cancer, NSLC), NCI-H841 (small cell lung cancer, SCLC), CP3 and DU145 (human prostate carcinoma cell lines) and U1690 (SCLC). By detailed comparisons with these measurements, the performance of nine different radiobiological models was examined for the entire dose range, including high doses beyond the shoulder of the survival curves. Using the computed and measured cell surviving fractions, comparison of the goodness-of-fit for all the models was performed by means of the reduced χ (2)-test with a 95% confidence interval. The obtained results indicate that models with dose-independent final slopes and extrapolation numbers generally represent better choices for SRT. This is especially important at high doses where the final slope and extrapolation numbers are presently found to play a major role. The PLQ, USC and LQL models have the least number of shortcomings at all doses. The extrapolation numbers and final slopes of these models do not depend on dose. Their asymptotes for the cell surviving fractions are exponentials at low as well as high doses, and this is in agreement with the behaviour of the corresponding experimental data. This is an important improvement over the LQ model which predicts a Gaussian at high doses. Overall and for the highlighted reasons, it was concluded that the PLQ, USC and LQL models are theoretically well-founded. They could prove useful compared to the other proposed radiobiological models in clinical applications for obtaining uniformly accurate cell surviving fractions encountered in stereotactic high-dose radiotherapy as well as at medium and low doses.
多年来,线性二次(LQ)模型已被广泛用于描述常规分割放疗中低至中等剂量的总剂量和剂量分割的影响。立体定向放射外科(SRS)和立体定向放射治疗(SRT)的最新进展增加了对寻找可靠细胞存活模型的兴趣,该模型在高剂量下也将是准确的。已经提出了不同的模型来改善高剂量存活反应的描述,例如通用存活曲线(USC)、Kavanagh-Newman(KN)和 LQ 模型的几种推广,例如线性二次线性(LQL)模型和 Pade 线性二次(PLQ)模型。本研究的目的是比较多种模型,以找到最佳选择,这些选择可以成功地用作 SRT 的分割校正方法。在这项工作中,使用了六个独立的实验数据集:CHOAA8(中国仓鼠成纤维细胞)、H460(非小细胞肺癌,NSLC)、NCI-H841(小细胞肺癌,SCLC)、CP3 和 DU145(人前列腺癌细胞系)和 U1690(SCLC)。通过与这些测量结果的详细比较,检查了九种不同放射生物学模型在整个剂量范围内的性能,包括存活曲线肩部以外的高剂量。使用计算出的和测量的细胞存活分数,通过使用带有 95%置信区间的简化 χ(2)检验对所有模型的拟合优度进行比较。结果表明,剂量独立的最终斜率和外推数的模型通常是 SRT 的更好选择。这在高剂量下尤为重要,因为目前发现最终斜率和外推数在高剂量下起着主要作用。PLQ、USC 和 LQL 模型在所有剂量下的缺点最少。这些模型的外推数和最终斜率不依赖于剂量。它们的细胞存活分数渐近线在低剂量和高剂量下都是指数,这与相应的实验数据的行为一致。这是对 LQ 模型的重要改进,LQ 模型在高剂量下预测高斯分布。总体而言,出于突出的原因,PLQ、USC 和 LQL 模型在理论上是合理的。与其他提出的放射生物学模型相比,它们在临床应用中可能更有用,可用于获得立体定向高剂量放疗以及中低剂量时遇到的均匀准确的细胞存活分数。