Lung cancer is a major public health problem in the United Kingdom and worldwide. There were 35 000 lung cancer deaths in the United Kingdom in 2010 ( Although there have been considerable improvements in prognosis of other cancers in recent decades, lung cancer is still characterised by poor survival, <10% at 5 years, partly because it tends to be diagnosed at a late stage ( Although the primary strategy for control of lung cancer is to reduce the prevalence of the main risk factor (i.e., smoking), it should be noted that many lung cancers occur in ex-smokers, and there may be a secondary role for early detection to diagnose the disease while it is successfully treatable (Raji et al, 2012). The National Lung Screening Trial (NLST) in the United States showed a 20% reduction in lung cancer mortality with the offer of annual low-dose CT screening for lung cancer as compared with annual chest X-ray (Aberle et al, 2011). As a consequence, there is considerable interest in the possibility of a lung cancer screening service in the United Kingdom (Field and Duffy, 2008; Field et al, 2013). Lung cancer is unusual in that there is a single identified risk factor, cigarette smoking, that accounts for the majority of cases of the disease. This has two implications in terms of lung cancer screening: first, tobacco control activities should continue, with screening services linked to smoking cessation programmes; second, the target population for screening should be selected on the basis of estimation of risk of lung cancer, a strong component of which is smoking.

The NLST recruited subjects aged 55–74 with at least 30 pack-years of smoking history, including ex-smokers only if they had given up during the past 15 years. The study group (26 722 individuals) was offered three annual screening episodes using low-dose CT over 2 years and the control group (26 732 individuals) three annual screens with chest X-ray. The outcome was based on lung cancer deaths within 6 years from randomisation, using deaths from all lung cancers diagnosed after randomisation, that is, including cancers diagnosed in both arms after the screening stopped. There were 356 such deaths in the study arm and 443 in the control, a 20% relative reduction in the study arm, amounting to 87 deaths avoided in absolute terms.

The relative and absolute benefits would not necessarily be reproduced in a UK programme. The chest X-ray intervention in the control group may have prevented some deaths, and hence the effect of low-dose CT screening compared with usual care in the United Kingdom might be larger. A UK programme might target a different risk group that would affect the absolute baseline risk and potentially the absolute benefit. The compliance rates might be different (in the United Kingdom, there is 75% compliance with breast screening and 60% with bowel screening). A future UK lung cancer screening programme might adopt a longer interscreening period than 1 year, which would imply a lower benefit, but would be likely to offer a longer period of screening, which might be expected to confer a higher absolute benefit.

The choice of which population to screen for lung cancer is an important one. Clearly, the target population should be at a notably higher risk than the general population. This is not simply a matter of cost effectiveness. In addition to the benefits, there are risks associated with screening, notably the procedures and anxiety associated with the investigation of suspicious imaging findings in those who do not have lung cancer (Seigneurin et al, 2013). Thus, the target population should be at a sufficiently high risk to yield a favourable benefit–harm balance of the intervention.

In this paper, we draw on the results of the NLST and of a number of other studies to estimate the likely effects, favourable and unfavourable, of a low-dose CT screening programme in the United Kingdom (Chien and Chen, 2008; Infante et al, 2009; Aberle et al, 2011; Pastorino et al, 2012; Saghir et al, 2012; Seigneurin et al, 2013; McRonald et al, 2014).

Materials and methods

Target populations, likely compliance and eligibility

We consider two possible risk groups: that recruited by NLST and that selected by the UK Lung Screening (UKLS) pilot trial. The former has empirically observed rates of lung cancer as reported in the trial, and the latter was chosen to have at least 5% risk of a diagnosis of lung cancer in the next 5 years, based on a validated risk model for lung cancer incorporating age, sex, smoking, family history, asbestos exposure and personal medical history (McRonald et al, 2014).

For compliance, the situation is slightly more complicated than in the usual screening programme, as the subjects must first be willing to supply the information necessary for risk assessment and, secondly, be willing to undergo screening should their personal risk meet the eligibility criteria. For simplicity, we combine these into a single compliance rate, of those willing to comply at both stages. We consider two possible scenarios: firstly, that this combined rate is 30%, as approximately observed in the UKLS pilot trial (Field et al, 2013) and, secondly, that following the publicity and heightened awareness that would accompany a national programme, a compliance rate of 60% would be observed.

However, it is unreasonable to expect that both populations would have the same proportions of eligible subjects. We therefore assume that the first of these, the 30% compliant, has a subgroup of 20% fulfilling the NLST criteria, that is, 6% of the overall population, and 10% fulfilling the UKLS criteria (3% of the overall population, as approximately observed in UKLS). We assume that the 60% compliant have 16% eligible under the NLST criteria and 8% eligible under the UKLS (10 and 5% of the overall population). We assume that those who attend the prevalence screen also attend all scheduled incidence screens. There is a high correlation between successive screen attendance in other cancer screening programmes, but clearly the assumption of perfect correlation is at best an approximation.

The UKLS age range was 50–75 years and for NLST it was 55–74 years. Of the 30% compliant in UKLS, 10% satisfied the inclusion criteria. This may not necessarily generalise to the remaining 70%. It is estimated that 15% of the US population in the age group of 55–74 years satisfy the NLST criteria (Ma et al, 2013).

Screen detection and interval cancer rates

The detection rate of cancers at screening is dependent on whether this is a first or subsequent screen, the incidence rate of disease in the population screened, the mean sojourn time (the average time spent in the presymptomatic, screen-detectable phase) and the sensitivity of the screening test (Weedon-Faekjer et al, 2010). If I denotes incidence, M the mean sojourn time and S the screening sensitivity, it has been shown (Launoy et al, 1998) that the expected prevalence of cancers detected at a first screen will be

For cancers detected at subsequent screens, the rate will additionally depend on the length of the interscreening interval. For a short interval, there will be relatively few interval cancers (cancers arising symptomatically between screens) and relatively few subsequent screen-detected cancers, as there is little time since the previous screen for new presymptomatic cancers to arise. For longer intervals, both interval cancer rates and subsequent screen detection rates will be higher. Calculation of these can be complicated, but Launoy et al (1998) showed that in steady state, in a population attending for screening, the expected proportion of cancers detected at subsequent screens (as opposed to arising in the interval between screens) can be estimated as

where r is the interval between screens, λ=1/M is the transition rate from presymptomatic to symptomatic disease and S is the test sensitivity. This assumes an exponential distribution of the duration of the presymptomatic screen-detectable period. The expected rate of detection of cancers at a subsequent screen will be

The expected absolute rate of cancers arising in the interval between two screens will be

We take our estimates of S and M (and therefore λ) from Chien and Chen (2008), who estimated these quantities from a synthesis of published results from studies of lung cancer screening. We take two estimates of I, the underlying incidence in the population. For recruitment by the NLST criteria, we use the empirical annual incidence from the NLST (Aberle et al, 2011). For recruitment by the UKLS criteria, we use the estimated average incidence from the LLP risk model in 2848 positive responders in UKLS who met or exceeded the 5% risk criterion.

From the above, we estimate the numbers of cancers detected at screening and the numbers arising in the intervals between screens for different screening frequencies.

Further investigation rates

A proportion of those screened will have a suspicious finding, and will be recalled for further investigation. Of these, a further proportion will actually have lung cancer. The numbers of screenees recalled for further investigation will depend on the frequency of screening. For example, for some imaging findings, a repeat scan at 1 year is indicated. In an annual programme, this would not mean any additional diagnostic activity, whereas in a 2-yearly screening programme, it would mean at least one extra scan.

For annual screening, we estimate the numbers recalled for further investigation from NLST. Although in NLST positive findings were observed in 27%, 28% and 17% at first, second and third screens, respectively, not all of those required further diagnostic investigation, other than a repeat screen at 1 year. The percentage positive and requiring further investigation at first screen was 24%. The corresponding average percentage over the second and third screens was 13%. We therefore assume for annual screening a further investigation rate of 24% at first screen and 13% at subsequent. For 2-yearly screening, we assume 24% at all screens.

Effect on lung cancer mortality

We posit two possible absolute mortality effects. The first is simply a translation of the 20% reduction observed in NLST to an absolute effect. This was observed in the group offered CT screening in NLST, in association with three annual screens and high compliance rates. Although three other trials have published the effect on lung cancer mortality (Infante et al, 2009; Pastorino et al, 2012; Saghir et al, 2012) these trials were very small and a meta-analysis including all four trials gives a 19% reduction, very close to that of NLST (Field et al, 2013). We therefore use the NLST figure. The NLST figure may be conservative in any case, as the mortality includes deaths from tumours diagnosed after the first 3 years when both study and control arms were receiving usual care. In addition, the control arm in NLST was offered annual chest X-ray that may confer some mortality benefit, although a small one (Doria-Rose and Szabo, 2010; Oken et al, 2011). It is therefore both cautious and reasonable to assume a benefit of the order observed in NLST in association with annual CT screening. For brevity, we refer to this as the intent-to-treat (ITT) estimate as it is based on the randomised comparison in NLST. For 2-yearly screening, we assume the 20% to be attenuated to a 16% reduction, as the MST of 2.06 years and sensitivity of 97% would imply that the proportion of screen-detected cancers would be reduced by 20% with 2-yearly screening. It is not guaranteed that the mortality benefit would be attenuated by the same proportion as the number of screen-detected cancers, but the assumption would appear to be reasonable, firstly because the screening is only expected to change the prognosis of the screen-detected cancers and, secondly, because the phenomenon appears to hold in screening for other cancers, as evidenced by the differences by age in the proportions screen detected and the corresponding differences in mortality reductions in a breast screening trial (Tabar et al, 1992) and the differences between annual and biennial screening in a colorectal screening trial (Mandel et al, 1993).

The second estimate is based directly on the number of screen-detected cancers. The 20% reduction in lung cancer mortality in NLST corresponded to an absolute benefit of 13.4 deaths prevented per 100 screen-detected cancers (as there were 87 deaths prevented and 649 screen-detected cancers in the CT arm of NLST). The median follow-up time in NLST was 6.5 years, corresponding to an approximate 5.5 year follow-up of cancers from the time of diagnosis, and the fatality rate observed in the control group was 47%. With longer term follow-up of the order of 10 years, one might reasonably expect at least 85% fatality as observed for 10-year fatality in SEER ( Our first estimate of potential lives saved for annual screening is derived by applying the 20% reduction to an expected combined fatality rate of 85% in the absence of screening from all cancers diagnosed in the programme. As noted above, with 47% case fatality in the control arm of NLST, there were 443 lung cancer deaths in the control arm and 356 in the CT arm. As 85% is 1.8 times 47%, we estimate that in the long term, there would be 1.8 × 443=797 deaths in the control arm of NLST and 1.8 × 356=641 deaths in the study arm, a difference of 156 deaths. Therefore, our second estimate of potential lives saved is 156 out of 649=24 deaths prevented per 100 screen-detected cancers. We refer to this estimate as the per-protocol (PP) estimate.


Overdiagnosis is usually defined as the diagnosis as a result of screening of cancer (generally histologically confirmed cancer) that would not have arisen in the host’s lifetime if screening had not taken place. It is notoriously difficult to estimate, but an approximation can be obtained by considering that any excess incidence observed in the study group will be the sum of overdiagnosed cancers and early-diagnosed cancers, those whose diagnosis has been brought forward by the lead time conferred by screening. We therefore estimate the proportion of cancers with lead time that would extend beyond the period of observation. This in turn gives an estimate of the excess expected from lead time alone. This can be subtracted from the excess incidence observed in the study group to give an estimate of overdiagnosis. The proportion of screen-detected cancers that would be expected to have occurred after the period of observation in the absence of screening is in the notation

as the median time from diagnosis of screen-detected cancers to the end of follow-up was 5.5 years. This is calculated by substituting the estimate of λ from Chien and Chen (2008) and converted to an absolute expected excess in the study group. This is subtracted from the observed excess to give an estimate of overdiagnosis.


Clearly, there are numerous sources of uncertainty in this estimation process. However, the uncertainty in the most important positive outcome, prevented deaths, is driven by the uncertainty in the mortality reduction of 20% observed in the NLST. We therefore estimated a range of uncertainty around our estimates of prevented deaths by applying the end points of the 95% confidence interval reported on this 20% (6.8–26.7%), and translating the end points of this interval to the estimated absolute numbers of deaths prevented.

Arguably, the most important negative end point is overdiagnosis, the estimate of which crucially depends on the estimated mean sojourn time of 2.06 years from the meta-analysis of Chien and Chen (2008). Accordingly, we estimated a range of uncertainties on the overdiagnosis based on the end points of the 95% confidence interval on this estimate (0.42–3.83 years).


Table 1 summarises screening and diagnostic results from the CT arm of NLST. There were 26 722 subjects randomised to the CT arm and 26 732 to the chest X-ray arm. In the average 6.5 years of observation, there were 1060 cancers diagnosed in the CT arm and 941 in the X-ray, a 13% excess (119 cancers) in the CT arm. On average, attendance at CT screening was 94%. The prevalence of lung cancers was 1.02% at first screen and 0.78% at the second and third screens combined.

Table 1 Screening and diagnostic outcomes in the US National Lung Screening Trial

Table 2 shows the estimated parameters from the procedures described in the Materials and Methods section. The risk criteria, eligibility, compliance and recall rates for further investigation are as explained above. The estimated mean sojourn time from the meta-analysis of Chien and Chen (2008) is 2.06 years, corresponding to λ =1/2.06=0.49 under the assumption of exponential sojourn time. The same meta-analysis estimates sensitivity of low-dose CT screening as 97%. The annual incidences are as observed in NLST, 0.6%, and as predicted from the LLP risk score in UKLS, 1.4% (Aberle et al, 2011; McRonald et al, 2014). The case fatality in the absence of screening is estimated from the US SEER ( The absolute mortality reductions are estimated from the NLST results, both directly and extrapolated to long term follow-up as described in the Materials and Methods section.

Table 2 Parameters used in modelling outcomes of low-dose CT screening for lung cancer

For overdiagnosis, we estimate that the proportion of screen-detected cancers in NLST brought forward from beyond the period of observation is

There were 270+168+211=649 screen-detected cancers (Table 1). Thus, we would expect an excess of 0.07 × 649=45 cancers in the CT arm from lead time. In fact, the excess observed was 119, giving an estimated overdiagnosis of 74 cancers. Thus, we assume overdiagnosis of 11% (74 out of 649) of screen-detected cancers. The 95% confidence interval on the mean sojourn time of 0.42–3.83 years inverts to give a 95% interval on λ of 0.26–2.38, giving a range for Q of almost zero to 0.26. This in turn gives a range of overdiagnosis values from 0 to 18% of screen-detected cancers.

Table 3 shows the predicted outcomes based on the parameter estimates in Table 2. Predictions are shown for the eight combinations of the following: annual and biennial screening; UKLS and NLST populations (incidences of 1.4% and 0.6% per year respectively); and 30% and 60% compliance. The results are shown for an initial population of 1 million, which is successively reduced by excluding those ineligible on the basis of risk, and by noncompliance. Thus, the actual numbers screened are 10% of the initial population. The interval cancers pertain to those actually screened. The deaths prevented are from those that would have occurred in an unscreened group within 10 years of diagnosis.

Table 3 Estimated outcomes per million population in the target age range, for 10 years of annual or biennial low-dose CT screening, based on the parameters in Table 2, with uncertainty intervals on the deaths prevented and overdiagnosed cancers

Table 4 summarises the results in terms of undesirable outcomes per death prevented. As expected, the outcomes for deaths prevented show a greater absolute benefit when using a higher-risk population. In annual screening of a population at UKLS risk, 1.4% per year, the ITT estimate gives 385 screens (330 000/857) and 54 subjects undergoing further investigation (46 200/857) per lung cancer death prevented. The corresponding figures for the NLST risk population, 0.6% per year, are 899 screens and 126 subjects undergoing further investigation per death prevented. The corresponding PP estimates are less conservative but show the same improved absolute benefit. Regardless of interval or target risk population, we estimate 1 overdiagnosed case per 2 deaths prevented using ITT estimates and 1 overdiagnosed case per 2.5 deaths prevented using PP estimates.

Table 4 Estimated outcomes and uncertainty intervals expressed as screening activity and undesired outcomes per lung cancer death prevented, stratified by incidence of target population, screening frequency and benefit estimate (ITT or PP)

Annual screening is estimated to be more effective in absolute terms, although the difference between the two regimens in terms of deaths prevented is modest. Annual screening incurs a lower cost per death prevented in terms of persons screened and in terms of numbers of further investigations, but a higher cost in terms of number of screening episodes. Absolute overdiagnosis rates are estimated to be lower in biennial screening, but rates of overdiagnosis per death prevented are the same in each regimen.


The above demonstrates the use of published figures to estimate the likely benefits and human costs of low-dose CT screening for lung cancer. The methodology is relatively simple, and the results are readily comprehensible. The predictions are based mainly on the largest published randomised trial of the intervention. We have included ranges of uncertainty on the numbers of deaths prevented and the overdiagnosed cases, based on published 95% confidence intervals on the mortality reduction and the mean sojourn time. However, one might consider the sensitivity of the outcomes to changes in the parameter estimates. If we assume the sensitivity recently reported by NLST of 93.8% (Church et al, 2013), instead of 97% from the meta-analysis, the numbers of screen-detected cancers and the numbers of deaths prevented would be attenuated by 3%. More conservatively, use of the combined mortality reduction from all trials that have so far published lung cancer mortality results (Field et al, 2013) would reduce the numbers of deaths prevented by 5%. This would reduce the absolute numbers of deaths prevented by between 30 and 80. In any case, the results based on NLST are likely to be conservative, as noted above, because of the inclusion of post-screening cancers and the X-ray intervention in the control group. The fatality rate of the control group cases in NLST was considerably higher than one would expect from the SEER data (Aberle et al, 2011; However, the range of possibilities from the alternative benefit estimates and postulated compliance is consistent with observations in a major review (Seigneurin et al, 2013).

Our own results here based on either NLST or UKLS are also likely to be conservative. In common with other modelling exercises to predict the effect of cancer screening, we have assumed an underlying Markov process model. This is a powerful tool for estimation and projection, but the Markov assumptions may not hold for all cancers. In this case, they appear to have underestimated incidence screen cancer detection rates, at 5 per 1000 screening episodes, in comparison with observed rates, 8 per 1000, in NLST (Aberle et al, 2011). Predicted interval cancer rates are correspondingly higher than observed in NLST. Thus, if anything, our PP estimates of lives saved are based on conservative estimates of the numbers of screen-detected cancers and therefore will be underestimates of the true numbers of deaths prevented.

We have not quoted uncertainty ranges for underlying incidence and mortality rates, or for rates of recall for further investigations, as these are generally based on very large numbers, and confidence intervals would consequently be narrow. It should, however, be acknowledged that incidence and mortality will depend on the population targeted and further investigation rates on the diagnostic algorithm adopted, and to some extent on the hardware and software used in the CT scanning.

The predictions suggest that the intervention effect could justify the human costs. A full cost-effectiveness analysis from NLST is awaited. As one might expect, the use of a higher-risk population is a more cost-effective option. That is, for either annual or biennial screening, the screening is more efficient using the UKLS criteria for eligibility than using the NLST criteria. Against this, there are the National Health Service’s considerations of equity and simplicity of the eligibility criteria.

Interestingly, the outcomes suggest that biennial screening, while being less effective in absolute terms than annual screening, may be similarly cost effective. The ITT estimate for the UKLS risk group is of 263 screening episodes and 63 further investigation episodes per death prevented, as compared with the figures of 385 and 54 above. However, it should be noted that the evidence base for low-dose CT screening for lung cancer pertains almost entirely to annual screening. The benefit of biennial screening was estimated by extrapolation from observed annual screening results, and hence is subject to additional uncertainty. This points up the limitations of modelling exercises, whether using simple deterministic approaches like this or more mathematical stochastic models (Chien and Chen, 2008; Weedon-Faekjer et al, 2010): the credibility of the model results rests crucially on the evidence base. For annual screening, there is already an experimental evidence base, whereas for biennial, the parameters have to be imputed from limited data. However, the results are sufficiently suggestive as to indicate that further empirical research on longer intervals than 1 year would be worthwhile.

The estimates of overdiagnosis suggest 1 overdiagnosed case for every 2 to 2.5 lives saved. These too are subject to considerable uncertainty and further follow-up of the screening trials is necessary to obtain more reliable estimates.