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基于线性二次和通用生存曲线模型在大剂量立体定向体放射治疗中重新审视等效均匀剂量的形式。

Revisiting the formalism of equivalent uniform dose based on the linear-quadratic and universal survival curve models in high-dose stereotactic body radiotherapy.

机构信息

Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Essen, Germany.

Department of Radiation Oncology, Karl-Lennert-Krebscentrum Nord, University Medical Center Schleswig-Holstein, Campus Kiel, Arnold-Heller-Straße 3, Haus 50, 24105, Kiel, Germany.

出版信息

Strahlenther Onkol. 2021 Jul;197(7):622-632. doi: 10.1007/s00066-020-01713-w. Epub 2020 Nov 27.

DOI:10.1007/s00066-020-01713-w
PMID:33245378
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8219592/
Abstract

PURPOSE

To examine the equivalent uniform dose (EUD) formalism using the universal survival curve (USC) applicable to high-dose stereotactic body radiotherapy (SBRT).

MATERIALS AND METHODS

For nine non-small-cell carcinoma cell (NSCLC) lines, the linear-quadratic (LQ) and USC models were used to calculate the EUD of a set of hypothetical two-compartment tumor dose-volume histogram (DVH) models. The dose was varied by ±5%, ±10%, and ±20% about the prescription dose (60 Gy/3 fractions) to the first compartment, with fraction volume varying from 1% and 5% to 30%. Clinical DVHs of 21 SBRT treatments of NSCLC prescribed to the 70-83% isodose lines were also considered. The EUD of non-standard SBRT dose fractionation (EUD) was further converted to standard fractionation of 2 Gy (EUD) using the LQ and USC models to facilitate comparisons between different SBRT dose fractionations. Tumor control probability (TCP) was then estimated from the LQ- and USC-EUD.

RESULTS

For non-standard SBRT fractionation, the deviation of the USC- from the LQ-EUD is largely limited to 5% in the presence of dose variation up to ±20% to fractional tumor volume up to 30% in all NSCLC cell lines. Linear regression with zero constant yielded USC-EUD = 0.96 × LQ-EUD (r = 0.99) for the clinical DVHs. Converting EUD into standard 2‑Gy fractions by the LQ formalism produced significantly larger EUD than the USC formalism, particularly for low [Formula: see text] ratios and large fraction dose. Simplified two-compartment DVH models illustrated that both the LQ- and USC-EUD values were sensitive to cold spot below the prescription dose with little volume dependence. Their deviations were almost constant for up to 30% dose increase above the prescription. Linear regression with zero constant yielded USC-EUD = 1.56 × LQ-EUD (r = 0.99) for the clinical DVHs. The clinical LQ-EUD resulted in median TCP of almost 100% vs. 93.8% with USC-EUD.

CONCLUSION

A uniform formalism of EUD should be defined among the SBRT community in order to apply it as a single metric for dose reporting and dose-response modeling in high-dose-gradient SBRT because its value depends on the underlying cell survival model and the model parameters. Further investigations of the optimal formalism to derive the EUD through clinical correlations are warranted.

摘要

目的

利用适用于高剂量立体定向体放射治疗(SBRT)的通用生存曲线(USC)来研究等效均匀剂量(EUD)形式。

材料与方法

对于九种非小细胞肺癌(NSCLC)细胞系,使用线性二次(LQ)和 USC 模型来计算一组假设的两室肿瘤剂量-体积直方图(DVH)模型的 EUD。在第一室中,将处方剂量(60Gy/3 次分割)的±5%、±10%和±20%进行剂量变化,分次体积从 1%到 5%到 30%不等。还考虑了 21 例 NSCLC SBRT 治疗的临床 DVH,处方剂量为 70-83%等剂量线。进一步将非标准 SBRT 剂量分割的 EUD(EUD)转换为 2Gy 的标准分割(EUD),使用 LQ 和 USC 模型来方便比较不同的 SBRT 剂量分割。然后从 LQ-USC-EUD 估计肿瘤控制概率(TCP)。

结果

对于非标准 SBRT 分割,在所有 NSCLC 细胞系中,当剂量变化高达±20%,分次肿瘤体积高达 30%时,USC-EUD 与 LQ-EUD 的偏差主要限制在 5%以内。对于临床 DVH,线性回归具有零常数,得出 USC-EUD=0.96×LQ-EUD(r=0.99)。通过 LQ 形式将 EUD 转换为标准 2Gy 分数会产生明显大于 USC 形式的 EUD,尤其是对于低[公式:见正文]比和大分次剂量。简化的两室 DVH 模型表明,LQ-USC-EUD 值对低于处方剂量的冷点都很敏感,而对体积的依赖性很小。在处方剂量增加 30%以上时,它们的偏差几乎保持不变。对于临床 DVH,线性回归具有零常数,得出 USC-EUD=1.56×LQ-EUD(r=0.99)。临床 LQ-EUD 导致中位 TCP 接近 100%,而 USC-EUD 为 93.8%。

结论

为了在高剂量梯度 SBRT 中应用 EUD 作为剂量报告和剂量反应建模的单一指标,SBRT 界应定义一个统一的 EUD 形式,因为其值取决于潜在的细胞存活模型和模型参数。需要进一步通过临床相关性研究来确定推导 EUD 的最佳形式。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/984f/8219592/7be9561d5dc9/66_2020_1713_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/984f/8219592/61cb4124aea5/66_2020_1713_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/984f/8219592/17bbafbe00ff/66_2020_1713_Fig2_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/984f/8219592/34a8fabaaac9/66_2020_1713_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/984f/8219592/7be9561d5dc9/66_2020_1713_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/984f/8219592/61cb4124aea5/66_2020_1713_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/984f/8219592/17bbafbe00ff/66_2020_1713_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/984f/8219592/f8c75333b4ac/66_2020_1713_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/984f/8219592/34a8fabaaac9/66_2020_1713_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/984f/8219592/7be9561d5dc9/66_2020_1713_Fig5_HTML.jpg

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