Vetter D
Mayo Clinic, Rochester, MN.
Med Phys. 2012 Jun;39(6Part27):3952-3953. doi: 10.1118/1.4736131.
The lifetime attributable risk estimates from the National Academy of Sciences BEIR VII report have been used by a number of authors to estimate cancer mortality caused by radiation exposure from medical diagnostic radiology exams. This controversial practice assumes that the dose response relationship between radiation and cancer is linear with no threshold (LNT). For purposes of protecting public health, use of the LNT model is widely accepted. But is it appropriate for estimating risk to individuals exposed to low doses of radiation from medical procedures? Radiation biology research demonstrates that not all biological processes are linear. Italso has provided data that support not only LNT but supra linear and sub linear response models. Results from epidemiology studies can also be used to support the use of any of these models, but the confidence intervals are much larger. Since we can't prove which model is correct, for purposes of protecting patients we assume that any exposure has the potential for harm and we use optimization to keep exposures as low as reasonably achievable.Several areas of research are contributing insight into this dilemma, but they still leave several important questions unanswered: • How can we accurately extrapolate low-dose biological effects generated in the laboratory to risk in a human? • Is extrapolation from high dose, high dose rate, acute exposures appropriate when human exposures are primarily chronic low dose exposures. Epidemiology alone is unlikely to provide information that will resolve this dilemma. The numbers of individuals required in a sample are too large, and the homogeneity among subjects is lacking. Reliance on radiation biology research alone is problematic because the research is focused primarily on mechanisms and not risk. This paper will present an overview of the issues and suggest areas of research that may contribute to our understanding of the level of risk associated with low doses of medical radiation.
美国国家科学院BEIR VII报告中的终生归因风险估计值已被许多作者用于估算医学诊断放射检查辐射暴露导致的癌症死亡率。这种有争议的做法假定辐射与癌症之间的剂量反应关系是线性无阈值的(LNT)。为保护公众健康,LNT模型的使用已被广泛接受。但它适用于估算接受医疗程序低剂量辐射个体的风险吗?辐射生物学研究表明,并非所有生物过程都是线性的。它还提供了不仅支持LNT,还支持超线性和亚线性反应模型的数据。流行病学研究结果也可用于支持这些模型中的任何一种,但置信区间要大得多。由于我们无法证明哪种模型是正确的,为保护患者,我们假定任何暴露都有潜在危害,并采用优化措施将暴露保持在合理可达到的最低水平。几个研究领域正在为解决这一困境提供见解,但仍留下几个重要问题未得到解答:• 我们如何准确地将实验室产生的低剂量生物效应外推至人类风险?• 当人类暴露主要是慢性低剂量暴露时,从高剂量、高剂量率急性暴露进行外推是否合适。仅靠流行病学不太可能提供解决这一困境的信息。样本所需个体数量太大,且缺乏受试者之间的同质性。仅依赖辐射生物学研究也存在问题,因为该研究主要关注机制而非风险。本文将概述这些问题,并提出可能有助于我们理解低剂量医学辐射相关风险水平的研究领域。