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加兰讲座。关于小剂量X射线致癌的问题。

Garland Lecture. On the question of cancer induction by small X-ray doses.

作者信息

Webster E W

出版信息

AJR Am J Roentgenol. 1981 Oct;137(4):647-66. doi: 10.2214/ajr.137.4.647.

Abstract

There is no proven body of fact that establishes an increase in human cancer after low doses of x or gamma radiation such as those received environmentally, occupationally, or from medical diagnostic procedures; that is, radiation levels below about 10 rad (0.1 Gy). This paper reviews the principal low dose epidemiologic studies that have investigated possible cancer increases. The results of these studies are negative, equivocal, or, when positive, invalidated by methodologic defects or by inconsistency with the feasible carcinogenic effect of background radiation. Despite the lack of direct evidence however, it will never be possible to exclude a very small cancer risk from even the lowest radiation levels, primarily because of statistical limitations in the design of epidemiologic studies. Estimates of cancer risk from low levels of x or gamma ray exposure are therefore based on assumptions regarding the relation between cancer increases and radiation dose. The statistical uncertainties of the meager human data at low doses do not permit unique relations to be established. Nevertheless recent radiobiologic investigations of dose/effect relations for neoplasms in animal populations, for chromosomal damage in human cells, and for malignant transformation in cultured mammalian cell lines suggest that a linear-quadratic relation, when fitted to the human data, provides a reasonable and conservative basis for risk estimation.

摘要

没有确凿的事实依据能证明,在受到低剂量的X射线或γ射线辐射后,如在环境中、职业环境中或医疗诊断过程中所受到的辐射,人体患癌几率会增加;也就是说,辐射剂量低于约10拉德(0.1戈瑞)时不会出现这种情况。本文回顾了主要的低剂量流行病学研究,这些研究探讨了患癌几率增加的可能性。这些研究结果均为阴性、不明确,或者即便结果为阳性,也因方法缺陷或与背景辐射的可行致癌效应不一致而无效。然而,尽管缺乏直接证据,但即便最低辐射剂量也无法排除极小的患癌风险,主要原因是流行病学研究设计存在统计局限性。因此,对低剂量X射线或γ射线辐射导致患癌风险的估计是基于癌症增加与辐射剂量之间关系的假设。低剂量时稀少的人体数据的统计不确定性使得无法确定唯一的关系。尽管如此,近期对动物群体肿瘤的剂量/效应关系、人体细胞染色体损伤以及培养的哺乳动物细胞系恶性转化的放射生物学研究表明,当与人体数据拟合时,线性二次关系为风险估计提供了合理且保守的基础。

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