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利用新型肺纤维化指数模型定量评估辐射剂量和分割效应对正常组织的影响。

Quantitative assessment of radiation dose and fractionation effects on normal tissue by utilizing a novel lung fibrosis index model.

机构信息

German Cancer Consortium (DKTK), Translational Radiation Oncology, National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), INF 460, 69120, Heidelberg, Germany.

Department of Radiation Oncology, Heidelberg Ion-Beam Therapy Centre (HIT), University of Heidelberg Medical School, Heidelberg, Germany.

出版信息

Radiat Oncol. 2017 Nov 7;12(1):172. doi: 10.1186/s13014-017-0912-y.

Abstract

BACKGROUND

Normal lung tissue tolerance constitutes a limiting factor in delivering the required dose of radiotherapy to cure thoracic and chest wall malignancies. Radiation-induced lung fibrosis (RILF) is considered a critical determinant for late normal tissue complications. While RILF mouse models are frequently approached e.g., as a single high dose thoracic irradiation to investigate lung fibrosis and candidate modulators, a systematic radiobiological characterization of RILF mouse model is urgently needed to compare relative biological effectiveness (RBE) of particle irradiation with protons, helium-, carbon and oxygen ions now available at HIT. We aimed to study the dose-response relationship and fractionation effect of photon irradiation in development of pulmonary fibrosis in C57BL/6 mouse.

METHODS

Lung fibrosis was evaluated 24 weeks after single and fractionated whole thoracic irradiation by quantitative assessment of lung alterations using CT. The fibrosis index (FI) was determined based on 3D-segmentation of the lungs considering the two key fibrosis parameters affected by ionizing radiation i.e., a dose/fractionation dependent reduction of the total lung volume and increase of the mean lung density.

RESULTS

The effective dose required to induce 50% of the maximal possible fibrosis (ED ) was 14.55 ± 0.34Gy and 27.7 ± 1.22Gy, for single and five- fractions irradiation, respectively. Applying a deterministic model an α/β = 4.49 ± 0.38 Gy for the late lung radiosensitivity was determined. Intriguingly, we found that a linear-quadratic model could be applied to in-vivo log transformed fibrosis (FI) vs. irradiation doses. The LQ model revealed an α/β for lung radiosensitivity of 4.4879 Gy for single fraction and 3.9474 for 5-fractions. Our FI based data were in good agreement with a meta-analysis of previous lung radiosensitivity data derived from different clinical endpoints and various mouse strains. The effect of fractionation on RILF development was further estimated by the biologically effective dose (BED) model with threshold BED (BED ) = 30.33 Gy and BED  = 61.63 Gy, respectively.

CONCLUSION

The systematic radiobiological characterization of RILF in the C57BL/6 mouse reported in this study marks an important step towards precise estimation of dose-response for development of lung fibrosis. These radiobiological parameters combined with a large repertoire of genetically engineered C57BL/6 mouse models, build a solid foundation for further biologically individualized risk assessment of RILF and functional RBE prediction on novel of particle qualities.

摘要

背景

正常肺组织耐受量是治愈胸部和胸壁恶性肿瘤所需放射剂量的限制因素。放射性肺纤维化(RILF)被认为是晚期正常组织并发症的关键决定因素。虽然经常采用 RILF 小鼠模型,例如单次高剂量胸部照射,以研究肺纤维化和候选调节剂,但迫切需要对 RILF 小鼠模型进行系统的放射生物学特征描述,以比较现在在 HIT 可用的质子、氦、碳和氧离子的相对生物效应(RBE)。我们旨在研究 C57BL/6 小鼠中光子照射引起肺纤维化的剂量反应关系和分割效应。

方法

通过 CT 对全胸单次和分割照射后 24 周的肺改变进行定量评估,评价肺纤维化。纤维化指数(FI)是基于对受电离辐射影响的两个关键纤维化参数(即总肺体积的剂量/分割依赖性降低和平均肺密度增加)的 3D 分割来确定的。

结果

引起最大可能纤维化 50%所需的有效剂量(ED )分别为 14.55 ± 0.34Gy 和 27.7 ± 1.22Gy,用于单次和五次分割照射。应用确定性模型确定晚期肺放射敏感性的 α/β 为 4.49 ± 0.38Gy。有趣的是,我们发现线性二次模型可应用于体内纤维化(FI)与照射剂量的对数转换。LQ 模型显示,单次分割的肺放射敏感性的 α/β 为 4.4879Gy,五次分割的为 3.9474Gy。我们的 FI 数据与来自不同临床终点和不同小鼠品系的先前肺放射敏感性数据的荟萃分析非常吻合。通过生物有效剂量(BED)模型进一步估计分割对 RILF 发展的影响,阈值 BED(BED )分别为 30.33Gy 和 61.63Gy。

结论

本研究对 C57BL/6 小鼠中 RILF 的系统放射生物学特征进行了描述,这是对肺纤维化发展的剂量反应进行精确估计的重要一步。这些放射生物学参数与大量遗传工程 C57BL/6 小鼠模型相结合,为进一步对 RILF 进行生物学个体化风险评估和对新型粒子质量进行功能 RBE 预测奠定了坚实的基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4159/5678815/068d569129c0/13014_2017_912_Fig1_HTML.jpg

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