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Abstract

Objectives

To estimate the risk of radiation-induced lung cancer mortality from three annual low-dose lung computed tomography (CT) screens before age 55 years (starting at age 30, 40 or 50) and the mortality reduction from screening (i.e. the efficacy) needed to outweigh these risks for never and current-smokers. The risk of radiation-induced breast cancer was also estimated for women.

Methods

The Biological Effectiveness of Ionizing Radiation VII committee's risk models were used to estimate radiation risk. Lung cancer mortality rates (based on the Bach model for current and the Cancer Prevention Study for never-smokers) were used to estimate the mortality reduction needed to outweigh this risk.

Results

For never-smokers, the estimated excess lifetime risk of radiation-induced lung cancer mortality from annual screening aged 40–42 was 1/10,000 (90% credibility interval: 0.4–3) for men and 3/10,000 (2–6) for women. For current-smokers, the estimated risks were approximately two-fold higher, with wider credibility intervals. Risks from screening age 30–32 or 50–52 years were of similar magnitude. The mortality reduction required to outweigh these risks was, for female never-smokers: 125% (40–300%) age 30–32 years, 70% (30–190%) age 40–42 years and 25% (10–70%) age 50–52 years, and for male current-smokers: 70% (20–120%) age 30–32 years, 10% (3–20%) age 40–42 years and 2% (1–4%) age 50–52 years. These figures were two to three times higher for females because of the higher radiation risks. The risk of radiation-induced breast cancer was in the range of three to six cases/10,000 females screened.

Conclusion

Before age 50, the mortality reduction from lung CT screening that is required to outweigh the radiation risk may be substantial, and in some cases unattainable (i.e. >100%).

INTRODUCTION

The risk of radiation-induced cancer from lung computed tomography (CT) screening is frequently described as small, for example, by comparison with the typical spontaneous fatal cancer risk of about one in four.¹ The most relevant comparison though is the magnitude of the potential absolute benefit from such screening. Brenner² previously estimated the risk of radiation-induced lung cancer mortality for smokers aged 50+ and suggested that lung cancer mortality would need to be reduced by at least 5% to outweigh these risks. It is not widely appreciated though that this figure is likely to be higher for screening at younger ages because the radiation risks will be higher, due to the longer time available to develop a radiation-induced cancer,³ while the absolute benefit will be lower because lung cancer incidence rates are lower.

A recent US survey found that a high proportion of never-smokers would be willing to consider lung CT screening.⁴ Screening studies of low-dose lung CT are already recruiting smokers and non-smokers as young as age 40.^5,6 Radiation-induced cancer risk estimates from lung CT screening are not currently available for never-smokers, or for screening before age 50.

There are no empirical data at present from which to estimate the cancer risks from CT scans directly and generally such studies are considered infeasible,⁷ but the risks can be estimated by extrapolating risk models from studies of subjects exposed to a wider range of radiation doses, such as the Japanese atomic bomb survivors.³ In this paper, we estimate the potential risk of radiation-induced lung cancer from three annual lung CT screens for asymptomatic individuals starting at age 30, 40 and 50 years. As the efficacy of lung CT screening is also not yet established, we then estimated the level of screening efficacy that would be required to outweigh these risks. For women, we also estimated the risk of radiation-induced breast cancer. The risk estimates were developed for never-smokers and for current-smokers.

MATERIALS AND METHODS

A synopsis of the methods is presented here and further details are provided in the on-line supplement.

Radiation risk models

We used the Biological Effects of Ionizing Radiation (BEIR) VII committee's risk model for radiation-induced incident lung cancer incidence to estimate risks according to smoking status. The model was based on the data from the study of the Japanese atomic bomb survivors.³ There is considerable uncertainty surrounding the form of the joint effect of smoking and radiation, hence the BEIR VII committee recommended the use of a risk model that was a weighted average of the estimate from an excess relative risk (ERR) model (equivalent to assuming a multiplicative effect of radiation and smoking on lung cancer risk) and an excess absolute risk (EAR) model (equivalent to assuming an additive effect of radiation and smoking on lung cancer risk). The BEIR VII committee recommended that greater weight be given to the EAR model (weight = 0.7) than to the ERR model (weight = 0.3) because a detailed analysis of smoking and radiation in the Japanese atomic bomb survivors found evidence that the effects appeared to be additive rather than multiplicative.⁸ However, these weights were allowed to vary in the uncertainty analysis (see below).

Background lung cancer rates

We estimated lung cancer rates for never-smokers using recent results from the Cancer Prevention Study II (Appendix Table A).⁹ For current-smokers, we used the Bach lung cancer risk model,¹⁰ assuming a 40 cigarettes per day smoking history (Appendix Table A). The Bach lung cancer risk model, which predicts one-year lung cancer probabilities according to age, smoking status, amount and duration of smoking, was developed using data from the Carotene and Retinol Efficacy Trial, and has been recently validated using data from the Alpha-Tocopherol, Beta-Carotene trial.¹¹

Lifetime risk of radiation-induced lung cancer mortality and radiation dose

CT-Expo software was used to calculate sex-specific organ doses and effective doses based on the screening protocol used in the National Lung Screening Trial.¹² This protocol was optimized to ensure minimum radiation dose while maintaining appropriate image quality with a target CTDI_vol range of 2–3 mGy for regular size patients.¹³ The protocol specifies the use of multi-detector CT scanners with four or more channels, gantry rotation times of ≤0.8 seconds, pitch range from 0.98 to 2.0, nominal reconstruction thickness of ≤3.2 mm, X-ray tube potential of 120 kVp and tube current-time product in the range 25–80 mAs depending on the scanner type and patient size. The mean radiation dose to the lung from a helical low-dose lung CT was estimated to be 3.9 mGy (range 2.7–6.1 mGy) for women and 3.8 mGy (range 2.6–5.9 mGy) for men and the mean effective doses were 1.3 and 1.0 mSv, respectively.

The risk of radiation-induced lung cancer remains elevated indefinitely after radiation exposure.³ Therefore, the total excess risk of radiation-induced lung cancer was calculated as a cumulative lifetime risk starting from 10 years after exposure, which was the assumed lag period. To estimate radiation-induced lung cancer mortality, the cumulative risk of lung cancer incidence was multiplied by 90% (the estimated probability of dying from lung cancer in the absence of screening).¹⁴ Adjustments for competing causes of death were based on US all cause mortality rates and survival probabilities, which were adjusted for smoking status by assuming that current-smokers will have a median survival of approximately five years less than the general population and never-smokers will have a median survival of approximately five years more than the general population.^15,16

Risk estimation for other cancer sites

The doses to other organs were also estimated using CT-Expo and assuming the same CT parameter settings described above. The estimated breast dose was 4 mGy and the thymus, stomach, liver and red bone marrow were all estimated to receive doses of 1 mGy from a low-dose helical lung CT scan. We used the BEIR VII risk models combined with the site-specific cancer incidence rates for the US general population from the Surveillance Epidemiology and End Results cancer registries data for 2000–2004 to estimate the risk of radiation-induced cancer for each of these additional sites.^3,14 All the risk models, except breast cancer, were based on the data from the Japanese atomic bomb survivors. The BEIR VII breast cancer risk model was based on the pooled analysis of breast cancer cohorts by Preston et al. ¹⁷ All other aspects of the calculations were the same as for lung cancer.

Lung cancer mortality reduction required to outweigh the radiation risk

Direct estimates of the relative or absolute reduction in lung cancer mortality from lung CT screening are not yet available. Therefore, we used an indirect approach based on incidence-based mortality rates (mortality rates that are partitioned according to the age when the cancer was diagnosed) to estimate the absolute reduction in lung cancer mortality required to outweigh the radiation risk.¹⁸ Incidence-based mortality rates were estimated using the smoking and age-specific incidence rates for the relevant screening period (e.g. age 30–32 years) multiplied by 90% (the estimated probability of dying from lung cancer in the absence of screening).¹⁴ A two-year (range 1–4 years) mean sojourn time (the period during which the disease is pre-clinical but detectable by screening) was assumed based on the findings from a recent study that estimated this parameter using data from six lung CT screening studies.¹⁹ Hence, the cumulative incidence-based mortality rate for each three-year screening period was the sum of the rates for the screening ages plus rates for the following two years. The mean sojourn time was allowed to vary from one to four years in the uncertainty analysis (see below). The percentage reduction in this cumulative incidence-based mortality that would be necessary to outweigh the risk of radiation-induced lung cancer mortality was then calculated by dividing the estimated risk of radiation-induced lung cancer mortality by the cumulative incidence-based mortality rate for each screening period.

Uncertainty analysis

Monte Carlo simulation was used to quantify the effects of the uncertainty in the weights applied to the ERR and EAR models, the radiation risk model parameters,³ the dose estimates^12,13 and the mean sojourn time.¹⁹ Distributions for the parameters were specified (Appendix Table B) and Latin-hypercube sampling was conducted using Crystal Ball software to estimate the 5th and 95th percentile of the distribution of results, which is referred to as the 90% credibility interval.²⁰

RESULTS

For never-smokers, the excess lifetime risk of radiation-induced lung cancer mortality from annual lung CT screening from age 30 to 32 was estimated to be three deaths (90% credibility interval: 2–5) per 10,000 women screened and one (0.3–2) death per 10,000 men screened (Table 1). For current-smokers, the risks were approximately two-fold higher: five deaths (2–12) per 10,000 women and two deaths (1–4) per 10,000 men screened. For annual screening from age 40 to 42 or 50 to 52, the estimates were similar in magnitude to those for screening age 30–32 because of the relatively low background lung cancer rates before age 50.

Table 1

Cumulative radiation-induced lung cancer mortality (and 90% CI), cumulative risk of lung cancer mortality without screening and reduction in lung cancer mortality (%) required to outweigh radiation risk following three annual lung CT screens

		Radiation-induced lung cancer mortaily		Cumulative risk of lung cancer mortality without screening		Reducation in lung cancer mortality to outweigh radiation risks
Annual screening period	Smoking status	n (per 10,000)	(90 % CI)	n (per 10,000)	(90% CI)	%	(90% CI)
Females
Age 30–32 years	Never	3	(2–5)	0.8	(0.6–1.0)	375	(200–800)
	Current*	5	(2–12)	3	(2–4)	170	(100–500)
Age 410–42 years	Never	3	(2–6)	1.8	(1.5–2.3)	170	(100–300)
	Current*	5	(2–12)	17	(13–24)	30	(10–70)
Age 50–52 years	Never	3	(1–5)	4	(3–5)	75	(30–130)
	Current*	5	(2–12)	100	(80–140)	4	(2–10)
Meals
Age 30–32 years	Never	1	(0.3–2)	0.8	(0.6–1.0)	125	(40–300)
	Current*	2	(1–4)	3	(2–4)	70	(20–120)
Age 40–42 years	Never	1	(0.4–3)	1.5	(1–2)	70	(30–190)
	Current*	2	(1–4)	19	(15–26)	10	(3–20)
Age 50–52 years	Never	1	(0.4–3)	4	(3–5)	25	(10–70)
	Current*	2	(1–4)	110	(90–160)	2	(1–4)

*Forty cigarettes per day

The estimated cumulative risk of lung cancer mortality without screening was relatively low for each screening period because lung cancer incidence rates are still low at these ages, even for current heavy smokers (Table 1). Hence, the potential number of lung cancer deaths that could be prevented by screening at these younger ages was also relatively low and the mortality reduction required to outweigh the radiation risks was high. For male never-smokers, the estimates were: 125% (40–300%) age 30–32 years, 70% (30–190%) age 40–42 years and 25% (10–70%) age 50–52 years, and for male current-smokers: 70% (20–120%) age 30–32 years, 10% (3–20%) age 40–42 years and 2% (1–4%) age 50–52 years. These figures were 2–3 times higher for females because of the higher radiation risks.

For annual screening of never-smokers from age 30 to 32, the estimated risk of radiation-induced breast cancer incidence was 6 (90% CI: 3–9) cases per 10,000 women screened (Table 2). The risk was slightly lower for current-smokers because of their higher competing risks, and the estimated risks decreased with age at screening to four (2–6) cases per 10,000 screened from age 50 to 52. The estimated radiation-induced risks for all other cancers were much lower than for lung or breast cancer (<0.5 cases for three annual screens per 10,000 females or males) (data not shown).

Table 2

Estimated lifetime risk of radiation-induced breast cancer incidence (and 90% CI) per 10,000 women screened annually with lung CT: according to age at screening and smoking status

Annual screening period	Smoking status	Cases	(90% CI)
Age 30–32 years	Never	6	(3–9)
	Current*	4	(2–6)
Age 40–42 years	Never	5	(3–8)
	Current*	4	(2–5)
Age 50–52 years	Never	4	(2–6)
	Current*	3	(1–4)

*Forty cigarettes per day

DISCUSSION

These estimates suggest that before age 50, the mortality reduction required to outweigh the radiation risk from lung CT screening may be substantial, or in some cases unattainable (>100%). For smokers aged about 50, the required mortality reduction is considerably lower (10%), but until results from the randomized screening trials are available, it is uncertain what the net benefit could be. The trials are powered to detect about a 20% reduction in lung cancer mortality due to screening. Estimates of the mortality reduction from observational studies depend upon the use of models to generate a theoretical control group of unscreened individuals. In an analysis of this type of the Mayo study, McMahon et al. ²¹ estimated that five annual screens with low-dose CT reduced lung cancer mortality by 28% at six years. No confidence bounds were provided, but given the small predicted difference in deaths in the study (n=11) they are likely to be wide. A pooled analysis of three observational studies (which included the Mayo study) found no evidence overall for a reduction in lung cancer mortality (RR = 1.0) but with a relatively wide CI (0.7–1.3).²²

Our calculations involve a number of assumptions. One of the most important in the radiation risk estimate is the assumed form of the joint effect of smoking and radiation. A recent analysis of the data from the Japanese atomic bomb survivors found that the joint effect of radiation and smoking appeared to be closer to additive than to multiplicative.⁸ Several previous studies of cancer patients treated with radiotherapy, however, found that the joint effect of the two exposures was consistent with a multiplicative model.^23–25 It is possible that the two exposures interact differently at low and at high doses of radiation exposure and further work is currently ongoing to try to understand these differences. We took account of this uncertainty by using a weighted average approach and allowed these weights to vary in the calculation of the credibility intervals.³ This source of uncertainty was one of the key determinants of the width of the credibility intervals. However, in general, the conclusions would not be altered materially even at the extremes of the credibility intervals. This suggests that the difference in the results for current and never-smokers is due to factors other than the assumed joint effect of radiation exposure and smoking. In particular, the fact that lung cancer rates appear to increase more rapidly with increasing age in current than in never-smokers (Appendix Table A).

Concern has been raised about the appropriateness of transferring risk estimates from the Japanese atomic bomb survivors to other populations exposed to fractionated low-dose exposures.³ These concerns are particularly relevant for lung cancer because the rates have been very low in Japan in comparison with the US. If the ERR model were not the correct biological model for radiation-induced lung cancer then using it to transfer risk estimates from the Japanese to the US could result in over-estimation of the risk. This is an additional reason that the BEIR VII committee recommended that a weighted average of the ERR and EAR models be used for estimating risks for the US population rather than using only the ERR.³

The use of this weighted average risk model is one of the explanations for why our risk estimates of the radiation-induced lung cancer mortality for a single lung CT screen at age 50 are approximately three times lower than estimates from an earlier publication.² Brenner used only the ERR component of the risk model from the Japanese atomic bomb survivors for his calculations.²⁶ Another reason that our risk estimates are lower is because our estimated absorbed lung dose was lower (3.9 compared with 5.2 mGy per screen) presumably due to the use of a more recent, optimized screening protocol.¹³

In the current paper, we assumed a linear no-threshold dose–response relationship for estimating radiation risk estimates at low doses. The BEIR VII committee recommended that these risk estimates be reduced by a dose and dose reduction effectiveness factor (DDREF) of 1.5 for exposures of 0.1 Gy or lower.³ There are considerable uncertainties surrounding the application of a DDREF though, and because there is also radiobiological evidence that supports down-turning dose–response curves at low doses we have not applied a DDREF to the current risk estimates.²⁷ Preston et al. ¹⁷ concluded that there was no evidence of departure from linearity at low doses for radiation-induced breast cancer, and so did not recommend that a DDREF should be applied to estimate breast cancer risk from fractionated radiation exposures. The evidence for the effects of dose fractionation on radiation-induced lung cancer risk is limited to the studies of patients with tuberculosis who received multiple fluoroscopies.^28,29 The risk per unit dose was lower in these studies than in the Japanese atomic bomb survivors. This could be due to the effect of dose fractionation, but could also be due to confounding with the underlying disease or smoking. If we had applied a DDREF to our calculations then it would have reduced our risk estimates for radiation-induced lung cancer by about 33%, but this would not have had a material impact upon our conclusions.

The radiation-induced lung cancer risks were about three times higher for women than for men because the radiation risk parameters in the Japanese atomic bomb survivors are estimated to be higher for females.³ This is also true for several other cancer sites, but the difference is larger for lung cancer, and does not seem to be explained by differences in smoking patterns.⁸ It is uncertain whether these differences reflect real biological variability in radiation sensitivity. Further research into this question is warranted as it has important implications for radiation protection.

Smoking-specific lung cancer rates are also uncertain. However, the estimated rates used here were broadly similar to those available from other populations.^9,10,30,31 Furthermore, because the lung cancer rates were used in both the calculation of the radiation risk and the estimation of the mortality rate in the absence of screening, increases or decreases in the rates would impact both estimates in a similar direction and hence the mortality reduction required to outweigh the risk (which is the ratio of these two estimates) should not be materially altered by this source of uncertainty.

The relatively short screening period of three years used in our calculations was chosen so that the strong age-dependence of the risk-benefit comparison would be evident. Also the results from lung CT screening trials such as the National Lung Screening Trial will provide estimates of the relative and absolute reduction in lung cancer mortality from similar periods of lung CT screening (three screens) for smokers aged about 50 and older. Indirect methods will still have to be used though to estimate the absolute reduction in lung cancer mortality for never or younger smokers. We used the method of incidence-based mortality rates previously to estimate the absolute reduction in breast cancer mortality from mammographic screening before age 50 in the UK. Assuming a 17% mortality reduction from screening our estimate of 0.6 deaths prevented per 1000 women screened from age 40 to 49 was similar to the observed reduction of 0.4 deaths per 1000 women screened from age 40 to 47 in the UK age trial.^32,33

Although we developed risk estimates for the general category of never-smokers, our results should be broadly applicable to never-smokers who have been exposed to other risk factors such as second-hand tobacco and asbestos. Typically, these factors are associated with about a two-fold higher risk of lung cancer than for a never-smoker,³⁴ and this would double the potential absolute benefit from screening but would also slightly increase the radiation risk. Similarly, although we conducted our calculations for current-smokers of 40 cigarettes per day the balance of the risks and benefits would be similar for current-smokers with different smoking histories.

As far as we are aware this is the first paper to estimate and highlight the risk of radiation-induced breast cancer incidence from lung CT screening. The radiation dose to the breast from a lung CT screen is similar in magnitude to the average dose from a two-view mammogram.³⁵ The risk of radiation-induced breast cancer is higher for pre-menopausal women and increases with decreasing age at exposure and so becomes comparatively more important for screening of younger women. Even if not fatal, a radiation-induced breast cancer is an important detrimental effect of radiation exposure and so these potential risks should also be take into account in the decision-making process.

Our calculations are based on the radiation exposure received from the screening scans. Studies suggest that up to 20% of lung CT screens for current older smokers will reveal abnormalities suspicious for cancer, and these subjects are usually referred to receive additional follow-up CT scans.³⁶ The additional radiation dose will be highly dependent on the type and number of follow-up scans, in particular whether they are further low-dose or full diagnostic CT scans. Inclusion of these additional scans in the current estimates would have further increased radiation risks and therefore further increased the mortality reduction required to outweigh the risks. We are currently conducting additional research to evaluate the patterns of follow-up CT scans after lung CT screening and this will be the topic of a future publication.

Although the calculations depend on a number of uncertain assumptions we have based the assumptions on the best data that are currently available. We also calculated credibility intervals to quantify the effect of the uncertainties in the parameters and the assumptions; even when the extremes of these intervals are considered the conclusions are generally unaltered. The results suggest that before age 50 the mortality reduction from lung CT screening required to outweigh the radiation risk may be substantial, and in some cases unattainable (i.e. >100%). In the absence of direct data, these indirect approaches for comparing the risks and the benefits of screening provide valuable evidence to help inform those making screening decisions.

Footnotes

APPENDIX

References

Committee on the Medical Aspects of Radiation Exposure (COMARE). The Impact of Personally Initiated X-ray Computed Tomography for the Health Assessment of Asymptomatic Individuals. UK: Health Protection Agency, 2007

Brenner

. Radiation risks potentially associated with low-dose CT screening of adult smokers for lung cancer. Radiology 2004;231:440–5

National Research Council. Committee to assess health risks from exposure to low levels of ionizing radiation. Health Risks from Exposure to Low Levels of Ionizing Radiation: BEIR VII. Washington DC: National Academy of Sciences, 2005

Silvestri

, Nietert

, Zoller

, Attitudes towards screening for lung cancer among smokers and their non-smoking counterparts. Thorax 2007;62:126–30

International Early Lung Cancer Action Program Investigators; Henschke

, Yankelevitz

, Survival of patients with stage I lung cancer detected on CT screening. N Engl J Med 2006;355:1763–71

Toyoda

, Nakayama

, Kusunoki

, Sensitivity and specificity of lung cancer screening using chest low-dose computed tomography. Br J Cancer 2008;98:1602–7

Land

. Estimating cancer risks from low doses of ionizing radiation. Science 1980;46:868–73

Pierce

, Sharp

, Mabuchi

. Joint effects of radiation and smoking on lung cancer risk among atomic bomb survivors. Radiat Res 2003;159:511–20

Thun

, Henley

, Burns

, Lung cancer death rates in lifelong non-smokers. J Natl Cancer Inst 2006;98:691–9

10.

Bach

, Kattan

, Thornquist

, Variations in lung cancer risk among smokers. J Natl Cancer Inst 2003;95:470–8

11.

Cronin

, Gail

, Zou

, Validation of a model of lung cancer risk prediction among smokers. J Natl Cancer Inst 2006;98:637–40

12.

Stamm

, Nagel

. CT-expo – a novel program for dose evaluation in CT. Rofo 2002;174:1570–6

13.

Cagnon

, Cody

, McNitt-Gray

, Description and implementation of a quality control program in an imaging-based clinical trial. Acad Radiol 2006;13:1431–41

14.

National Cancer Institute, Surveillance, Epidemiology and End Results. See www.seer.cancer.gov (accessed 6 June 2007)

15.

Hoyert

, Kung

H-C

, Smith

. Deaths: preliminary data for 2003. CDC: National Vital Statistics Reports, 2005;53:1–48

16.

Doll

, Peto

, Boreham

, Mortality in relation to smoking: 50 years' observations on male British doctors. Br Med J 2004;328:1519–27

17.

Preston

, Mattsson

, Holmberg

, Radiation effects on breast cancer risk: a pooled analysis of eight cohorts. Radiat Res 2002;158:220–35

18.

Chu

, Miller

, Feuer

, Hankey

. A method for partitioning cancer mortality trends by factors associated with diagnosis: an application to female breast cancer. J Clin Epidemiol 1994;47:1451–61

19.

Chien

, Chen

. Mean sojourn time and effectiveness of mortality reduction for lung cancer screening with computed tomography. Int J Cancer 2008;122:2594–9

20.

21.

McMahon

, Kong

, Johnson

, Estimating long-term effectiveness of lung cancer screening in the Mayo CT screening study. Radiology 2008;248:278–87

22.

Bach

, Jett

, Pastorino

, Tockman

, Swensen

, Begg

. Computed tomography screening and lung cancer outcomes. JAMA 2007;297:953–61

23.

Gilbert

, Stovall

, Gospodarowicz

, Lung cancer after treatment for Hodgkin's disease: focus on radiation effects. Radiat Res 2003;159:161–73

24.

Van Leeuwen

, Klokman

, Stovall

, Roles of radiotherapy and smoking in lung cancer following Hodgkin's disease. J Natl Cancer Inst 1995;87:1530–7

25.

Kaufman

, Jacobson

, Hershman

, Desai

, Neugut

. Effect of breast cancer radiotherapy and cigarette smoking on risk of second primary lung cancer. J Clin Oncol 2008;26:392–8

26.

National Cancer Institute. Report of the NCI-CDC working group to revise the 1985 radioepidemiological tables. Report NIH 03-5387. Bethesda MD: National Cancer Institute, 2004

27.

Brenner

, Doll

, Goodhead

, Cancer risks attributable to low doses of ionizing radiation: assessing what we really know. Proc Natl Acad Sci USA 2003;100:13761–6

28.

Davis

, Boice

Jr , Hrubec

, Cancer mortality in a radiation-exposed cohort of Massachusetts tuberculosis patients. Cancer Res 1989;49:6130–6

29.

Howe

. Lung cancer mortality between 1950 and 1987 after exposure to fractionated moderate-dose-rate ionizing radiation in the Canadian fluoroscopy cohort study and a comparison with lung cancer mortality in the Atomic Bomb survivors study. Radiat Res 1995;142:295–304

30.

Doll

, Peto

. Cigarette smoking and bronchial carcinoma: dose and time relationships among regular smokers and lifelong non-smokers. J Epidemiol Commun Health 1978;32:303–13

31.

Thun

, Day-Lally

, Myers

, Chapter 4: Trends in tobacco smoking and mortality from cigarette use in Cancer Prevention Studies I (1959 through 1965) and II (1982 through 1988). In: Changes in Cigarette-Related Disease Risks and their Implication for Prevention and Control: Smoking and Tobacco Control Monograph 8. Bethesda, MD: US Dept of Health and Human Services, Public Health Service, National Institutes of Health, National Cancer Institute, 1997:305–82

32.

Berrington de González

, Reeves

. Mammographic screening in the UK before age 50 years in the UK: comparison of the radiation risk with the mortality benefits. Br J Cancer 2005;93:590–6

33.

Moss

, Cuckle

, Evans

, Effect of mammographic screening from age 40 years on breast cancer mortality at 10 years' follow-up: a randomised controlled trial. Lancet 2006;368:2053–60

34.

Spitz

, Hong

, Amos

, A risk model for prediction of lung cancer. J Natl Cancer Inst 2007;99:715–26

35.

Spelic

. Updated trends in mammography dose 2nd image quality. MQSA Facility Score Card (FDA). See www.fda.gov/CDRH/mammography/scorecard-article5-update.html/

36.

Gohagan

JKP

, Marcus

, Fagerstrom

, Final results of the Lung Screening Study, a randomized feasibility study of spiral CT versus chest X-ray screening for lung cancer. Lung Cancer 2005;47:9–15