U.S. Preventive Services Task Force banner
U.S. Preventive Services Task Force

You Are Here: U.S. Preventive Services Task Force > Topic Index > Screening for Lung Cancer > Benefits and Harms of Computed Tomography Lung Cancer Screening Strategies

Benefits and Harms of Computed Tomography Lung Cancer Screening Strategies

A Comparative Modeling Study for the U.S. Preventive Services Task Force

Release Date: December 31, 2013


By: Harry J. de Koning, MD, PhD; Rafael Meza, PhD; Sylvia K. Plevritis, PhD; Kevin ten Haaf, MSc; Vidit N. Munshi, MS; Jihyoun Jeon, PhD; S. Ayca Erdogan, PhD; Chung Yin Kong, PhD; Summer S. Han, PhD; Joost van Rosmalen, PhD; Sung Eun Choi, SM; Melicia C. Miller, MPH; Suresh Moolgavkar, MD, PhD; Paul F. Pinsky, PhD; Christine D. Berg, MD; Amy Berrington de Gonzalez, PhD; William C. Black, MD; C. Martin Tammemagi, PhD; William D. Hazelton, PhD; Eric J. Feuer, PhD; Pamela M. McMahon, PhD


This article was first published in Annals of Internal Medicine on 31 December 2013. Select for copyright and source information.

Contents

Abstract
Introduction
Methods
Results
Discussion
References

Abstract

Background: The optimal screening policy for lung cancer is unknown.

Objective: To identify efficient computed tomography (CT) screening scenarios in which relatively more lung cancer deaths are averted for fewer CT screening examinations.

Design: Comparative modeling study using 5 independent models.

Data Sources: The National Lung Screening Trial; the Prostate, Lung, Colorectal, and Ovarian trial; the Surveillance, Epidemiology, and End Results program; and the U.S. Smoking History Generator.

Target Population: U.S. cohort born in 1950.

Time Horizon: Cohort followed from ages 45 to 90 years.

Perspective: Societal.

Intervention: 576 scenarios with varying eligibility criteria (age, pack-years of smoking, years since quitting) and screening intervals.

Outcome Measures: Benefits included lung cancer deaths averted or life-years gained. Harms included CT examinations, false-positive results (including those obtained from biopsy/surgery), overdiagnosed cases, and radiation-related deaths.

Results of Best-Case Scenario: The most advantageous strategy was annual screening from ages 55 through 80 years for ever-smokers with a smoking history of at least 30 pack-years and ex-smokers with less than 15 years since quitting. It would lead to 50% (model ranges, 45% to 54%) of cases of cancer being detected at an early stage (stage I/II), 575 screening examinations per lung cancer death averted, a 14% (range, 8.2% to 23.5%) reduction in lung cancer mortality, 497 lung cancer deaths averted, and 5250 life-years gained per the 100,000-member cohort. Harms would include 67,550 false-positive test results, 910 biopsies or surgeries for benign lesions, and 190 overdiagnosed cases of cancer (3.7% of all cases of lung cancer [model ranges, 1.4% to 8.3%]).

Results of Sensitivity Analysis: The number of cancer deaths averted for the scenario varied across models between 177 and 862; the number of overdiagnosed cases of cancer varied between 72 and 426.

Limitations: Scenarios assumed 100% screening adherence. Data derived from trials with short duration were extrapolated to lifetime follow-up.

Conclusion: Annual CT screening for lung cancer has a favorable benefit-to-harm ratio for individuals ages 55 through 80 years with 30 or more pack-years' exposure to smoking.

Primary Funding Source National Cancer Institute.

Return to Contents

Introduction

The burden of lung cancer in the world remains extremely high: the International Agency for Research on Cancer estimated 1.6 million new diagnoses in 2008 (12.7% of total cases of cancer) and 1.4 million deaths (18.2% of total cancer mortality) (1). In the United States and Canada, incidence (per 100,000) is 48.5 for men and 35.8 for women; mortality (per 100,000) is 37.9 and 24.2, respectively; and cumulative risk (to age 74 years) of dying of lung cancer is 3% in women and 4.6% in men. In the United States, 228,000 new cases of lung cancer and about 160,000 deaths are estimated for 2013 (2). Despite substantial reductions in smoking prevalence in the United States, which translated into an approximately 32% reduction in lung cancer mortality between 1975 and 2000 at the population level (3), lung cancer remains the leading cause of cancer mortality.

Recently, the National Lung Screening Trial (NLST) demonstrated that in a volunteer population of current and former smokers who were 55 to 74 years of age at entry, had at least 30 pack-years of cigarette smoking history, and were no more than 15 years since quitting (for former smokers), three annual computed tomography (CT) screening examinations reduced lung cancer–specific mortality by 20% relative to three annual chest radiography screening examinations at a median follow-up of 6.5 years (4). This trial did not directly address the effects of additional rounds of screening, long-term benefits or harms, or multiple alternative screening policies with different screening intervals and different eligibility criteria. Moreover, long-term outcomes must be quantified to understand the tradeoffs between benefits and potential harms involved with alternative screening strategies (5). In this study, we estimate future harms and benefits of lung cancer screening and identify a set of possible efficient lung cancer screening policies by using 5 separately developed microsimulation models calibrated to the 2 largest randomized controlled trials on lung cancer screening. This work was initiated by the U.S. Preventive Services Task Force (USPSTF) to inform its recommendations on lung cancer screening.

Return to Contents

Methods

Calibration of 5 Models to De-Identified Lung Cancer Screening Data From the NLST and Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial

We used 5 models calibrated to individual-level, de-identified data from the NLST (6) and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening (PLCO) trial (7). The NLST enrolled 53,452 persons at high risk for lung cancer at 33 U.S. centers from August 2002 through April 2004. Participants were randomly assigned to undergo 3 annual screening examinations with low-dose CT (26,722 participants) or single-view posterior–anterior chest radiography (26,730 participants). The PLCO trial randomly assigned 154,901 participants age 55 through 74 years at entry, 77,445 of whom were assigned to annual chest radiography and 77,456 to usual care between November 1993 and July 2001. There was no eligibility requirement concerning smoking. Although the PLCO trial compared chest radiography with no screening, it provided information on the natural history of lung cancer.

Groups of investigators at the following 5 institutions independently developed the models: Erasmus Medical Center in Rotterdam, the Netherlands (model E); Fred Hutchinson Cancer Research Center in Seattle, Washington (model F); the Massachusetts General Hospital in Boston, Massachusetts (model M); Stanford University in Stanford, California (model S); and the University of Michigan in Ann Arbor, Michigan (model U). Each model estimates screening effectiveness on the basis of a different set of assumptions that are key in predicting the effects of earlier treatment, and each model uses different mathematical formalisms and model structures. In essence, all the models account for the individual's age-specific smoking-related risk for lung cancer, date and stage of lung cancer diagnosis, the corresponding lung cancer mortality, and the individual's life expectancy in the presence and absence of screening (Appendix Figure 1).

For correct extrapolation of different possible screening scenarios, one must get the best estimates on several key parameters, including the duration of the screening-detectable preclinical period (by test, age, histologic characteristics, and sex), sensitivity (by test, age, and sex), and improvement in prognosis by earlier detection and treatment. All models were first set to mimic the design of both trials (for example, setting the numbers of screening examinations and screening modality, ages at screening, smoking history and sex of enrollees, and screening intervals). The models are validly calibrated when the key parameters—which may differ by model—can be estimated or adjusted to replicate the trial data closely. After calibration, the models reproduce the observed cumulative incidence of lung cancer (by stage, histologic characteristics, sex, age, type of detection, and round of screening) and lung cancer mortality in both groups of the trials. Close calibration to the 19% (95% CI, 7% to 25%) lung cancer mortality difference between groups of the NLST at 6 years of follow-up was prioritized (see Appendix Figure 2 and Appendix Table for key similarities and differences among the 5 models in calibration targets.)

Choosing Screening Programs and Expressing Harms and Benefits

The modeling groups standardized input data on smoking histories and non–lung cancer mortality to simulate life histories of the U.S. cohort born in 1950 by using an updated version of the National Cancer Institute's Smoking History Generator (8–11). All models included other-cause mortality to differ by sex, age, smoking status, and smoking intensity. A set of 576 programs that varied frequency of CT screening for lung cancer (1-, 2-, or 3-year intervals), ages of starting (45, 50, 55, or 60 years) and stopping (75, 80, or 85 years) screening (assuming that a last screening examination is included at this age), and eligibility based on smoking history (10, 20, 30, or 40 pack-years; having quit smoking 10, 15, 20, or 25 years previously) was examined, with analyses run separately for men and women.

In all scenarios, perfect screening adherence was assumed. Once a person's characteristics did not satisfy the eligibility criteria (such as passing the limit of years since smoking cessation), he or she would not be invited for future screenings. Potential benefits are expressed as lung cancer deaths averted and life-years gained. Potential harms are expressed as the number of screening examinations plus follow-up imaging examinations, number of false-positive results (including findings on surgery and biopsy), number of overdiagnosed lung cancer cases, and number of radiation-related lung cancer deaths. Follow-up procedures were assumed to be consistent with the observed rate of examinations per positive screening examination in the NLST; 2 models used explicit follow-up algorithms based on nodule size thresholds. False-positive results are estimated as a direct proportion to the number of CT screening examinations, as based on the average in 3 rounds of the NLST; we assumed that a false-positive result in a given round does not influence the probability of a false-positive result in subsequent rounds.

Overdiagnosed cases are the additional number of lung cancer cases detected in the screening scenarios compared with the estimated number of cases diagnosed in the absence of screening (12). All models simulate the underlying natural history of lung cancer (separately by histologic type) in individuals and include dose–response modules that relate a detailed cigarette smoking history over time to lung cancer risk. Each comparison is based on an identical underlying simulated cohort of individuals with the same smoking histories, sex composition, and potential times of other-cause death. Another scenario that reflects overdiagnosis is a person who has lung cancer that is expected to be clinically detected after death from other causes but whose cancer in the screening scenario is detected before death from other causes.

For all measures of benefits and harms, expressed per 100,000, a cohort of persons born in 1950 was followed from ages 45 to 90 years. We identified “efficient” scenarios as those that prevented the most lung cancer deaths for the same number of CT screening examinations (not including follow-up scans). Model results were compared by using the data envelopment analysis method (13), which is an engineering-based approach for selecting efficient scenarios from among a collection of alternatives. In simple terms, it finds programs that are near the efficient frontier, with consideration given to whether one is prioritizing maximizing benefits (that is, deaths averted [y-axis]) or minimizing harms (that is, CT screening examinations [x-axis]). For each model's results, we generated a rank score (decile of distance from the model's frontier) for each scenario not on the frontier. We identified scenarios on (score 0) or closest to (first three deciles) the frontier of at least 3 models. Two models (F and M) were used to estimate radiation-related lung cancer cases (see the Appendix). All results were averaged across all 5 models. Finally, an advantageous scenario was selected that was efficient and led to a substantial reduction in lung cancer mortality and life-years gained at reasonable harms. The number of screening examinations needed was similar to (a continuous) NLST scenario and to breast and colorectal cancer screening guidelines, given manpower and resources.

Role of the Funding Source

The National Cancer Institute supported the infrastructure for the Cancer Intervention and Surveillance Modeling Network models. The Agency for Healthcare Research and Quality funded this work and provided review. The authors worked with USPSTF members to specify the overall questions. These data did not include personally identifying information and were therefore exempt from institutional review board review.

Return to Contents

Results

Benefits and Harms of Efficient Scenarios

Of the 576 possible programs, 120 were on or close to the efficient frontier, where no alternative that provides more lung cancer deaths averted for fewer CT screening examinations exists. Table 1 shows the benefits of 26 top-ranked triennial, biennial, and annual scenarios, as well as benefits of a 27th program that was most similar to the NLST strategy but was not among the consensus efficient programs. None of the top-ranked scenarios has a starting age of 45 years. For the top-ranked triennial and biennial efficient programs, the starting age is 60 years and the minimum number of pack-years is 40 (with 1 exception). Triennial screening programs led to limited reductions in lung cancer mortality: from 4.6% to 6% in this cohort (range, 1.7% to 9.5% across models). Biennial programs led to 6.5% to 9.6% reductions in lung cancer mortality (range, 2.3% to 14.8% across models). When we compared the least intensive program (60-80-40-10, with values arranged per the following order: frequency–start age–stop age–minimum pack-years–maximum years since quitting smoking) of triennial to biennial screening, the additional percentage of lung cancer deaths averted is about 40%, at the expense of about 50% additional screening examinations (see Appendix Figure 3). Annual screening scenarios provide substantially more benefit, leading to 11% to 21.2% reductions in lung cancer mortality (range, 4.3% to 39.1% across models). In these scenarios, 48.1% to 56.9% of lung cancer cases are detected at stage I/II, compared with 37.4% without screening. The scenario most similar to the NLST criteria (A [annual]-55-75-30-15) leads to fewer lung cancer deaths averted but more screenings when compared with the next-most-intensive program (A-60-80-30-25).

Table 2 summarizes the most important harms associated with the scenarios. The number of follow-up imaging procedures and false-positive results increase proportionally to the number of CT screening examinations needed in each scenario, leading to 3.1 to 4.9 false-positive results per person screened. Decreasing the minimum pack-years eligibility criteria from 30 to 20 pack-years and to 10 pack-years in annual scenarios provides a relatively small increase in lung cancer deaths averted versus the large number of additional CT scans. Although these are still efficient scenarios, they require substantially more CT screening examinations (both overall and per person) and follow-up procedures, and false-positive results increase proportionally. Overdiagnosis ranges from 1.5% to 6.6% of all lung cancer cases, or 8.7% to 13.5% of screening-detected lung cancer cases.

Lung Cancer Deaths Averted and Life-Years Gained

Clinical concerns about the potential for increased operative mortality in older individuals with a history of heavy smoking, as well as increased comorbidity and reduced eligibility for surgery with curative intent at higher age limits, led us to focus on scenarios with stopping ages of at most 80 years. Figure 1 shows the effect of expanding the smoking eligibility in the age range of 55 through 80 years, beyond the criteria similar to those used in NLST: for example, to 25 years since quitting (A-55-80-30-25) or even 20 or fewer pack-years (A-55-80-20-25 or A-55-80-10-25). More lung cancer deaths may be averted with more CT screening examinations, but there are diminishing returns (although not at a single distinct point). The A-60-80-20-25 scenario, which extends eligibility to individuals with fewer pack-years but starting at a later age, is still efficient with respect to number of screening examinations and lung cancer deaths averted, but it provides fewer life-years gained than does A-55-80-30-15. For the 3 consecutive scenarios of A-55-80-30-15, A-60-80-20-25, and A-55-80-30-25, the number of screening examinations per lung cancer death averted increases progressively (550, 570, and 583 examinations), whereas the number of screening examinations per life-year gained is the highest (that is, the worst) for A-60-80-20-25 (52, 57, and 54 examinations). The A-60-80-20-25 scenario also results in the highest number and percentage of overdiagnosed cases.

Advantageous Scenario

Of the efficient scenarios, annual screening in the age range of 55 through 80 years had substantial benefits while maintaining a moderate level of harms. We judged a strategy that was similar to the NLST criteria—starting screening at age 55 years, but ending through age 80 years for ever-smokers with a smoking history of at least 30 pack-years, and no more than 15 years since quitting for former smokers (A-55-80-30-15)—as the advantageous scenario with an optimal balance of benefits and harms.

Table 3 summarizes the modeled data (14–19) about harms and benefits associated with that scenario expressed per 10,000 45-year-old people born in 1950 and followed through age 90 years. The upper- and lower-bound estimates presented in the table are ranges found across the 5 different models and not confidence intervals. The table illustrates that 19,300 of 100,000 individuals would be eligible for screening at some point in their lifetime. Without screening, lung cancer will be diagnosed in 5119 and 3719 will die of lung cancer. Assuming 100% adherence with screening, 50.5% of cases of lung cancer will be detected at an early (I/II) stage. There will be 497 fewer lung cancer deaths, and these persons will on average gain 10.6 life-years per death averted. They will also be prevented from experiencing advanced disease and its treatment. On the negative side, 67,550 false-positive results would be expected (19,300 individuals × 3.5 average false-positive results per individual), leading to 910 surgeries or biopsies for benign disease. There would be 1970 persons with a diagnosis of lung cancer made earlier than would have occurred if they had not been screened, and about 10% of these cancer cases would otherwise never have been diagnosed during their lifetime (190 cases).

The same scenario for persons with 20 pack-years or more increases the number of lung cancer deaths averted by 12.9%, but this means 33% more CT screening examinations in the population, with a proportional increase in false-positive test results (Table 1). Extending eligibility to 10 pack-years or more results in 8.6% more deaths averted for 23% more CT scans. Increasing the number of pack-years to 40, but extending the years since having quit smoking from 15 to 25, in the age range of 55 to 80 years, decreases the percentage eligible from 19.3% to 13.9%; however, these individuals would on average get one more screening examination during their lifetime, thereby increasing harms (Table 2).

Figure 2 shows the reductions in lung cancer mortality for the 8 (labeled) scenarios for each model group and the average of the 5 models. The reference scenario (A-60-80-40-25) leads to a 6.5% to 17.0% decrease in lung cancer mortality, and the A-55-80-30-15 scenario results in a 8.6% to 23.5% reduction. The most intensive scenario (A-55-80-10-25) leads to an estimated 12% to 34% decrease in lung cancer mortality. The percentage of overdiagnosed screening-detected cases varies between 5% and 17% almost uniformly across inclusion criteria.

Return to Contents

Discussion

Our models show that annual lung cancer screening of individuals with a smoking history of at least 30 pack-years from ages 55 through 80 years offers substantial benefits. There would be a 14% overall lung cancer mortality reduction and a 25% reduction in those eligible for screening, with relatively limited harms. Extending eligibility to individuals with fewer pack-years, although still efficient, leads to additional benefits along with relatively more additional harms. The models provide valuable tools to project trial results to different screening scenarios over the course of a lifetime and show the strategies that provide the greatest benefits for a specified level of resources.

The advantageous scenario for lung cancer screening compares favorably to the USPSTF guidelines for breast and colorectal cancer screening. Applying current USPSTF breast cancer screening recommendations to a similar 1950 U.S. cohort translates to about 1.1 million screening examinations (per 100,000 women) and 700 breast cancer deaths averted through use of the Erasmus model (20). Applying current colorectal screening guidelines translate to about 227,000 screening colonoscopies and 1910 colorectal cancer deaths averted through use of the Erasmus model (21). If we examine eligibility of the advantageous scenario by age in 2013 for the 1950 birth cohort, 17% of the 55- to 64-year-old age group, 12.5% of the 65- to 74-year-old age group, and 7% of the 75- to 80-year-old age group would be eligible for lung cancer screening. Applying these percentages to the current U.S. population means that about 10.5 million people in the United States would be eligible for screening and that more than 18,000 lung cancer deaths per year might be avoided. That estimate is more optimistic than the recently reported estimates under the NLST criteria of about 8.6 million people eligible for screening and about 12,000 averted lung cancer deaths (22). Just simulating 3 screening examinations as was done in NLST would, in our cohort-based approach, have led to a lifetime 3.7% reduction in lung cancer mortality (not shown). This is notably different from the observed mortality reduction point estimate reported at 6 years' follow-up in the NLST because only eligible persons get screened in our cohort analysis (dilution) and we modeled a lifetime reduction rather than short-term follow-up.

Overdiagnosis is a general concern with screening. There are few estimates of the magnitude of overdiagnosis with CT lung cancer screening (23). We estimated overdiagnosis with CT screening to be less than 17% of screening-detected cases (upper range in Figure 2). Although most published reports describing overdiagnosis in breast cancer screening apply to populations (that is, multiple cohorts), our average of 10% of overdiagnosed screening-detected lung cancer cases in the advantageous scenario is equal to that for breast cancer screening in women age 50 to 74 years every 2 years in relatively low-referral programs (24) and far less than that for breast cancer screening in high-referral countries, such as the United States (25). Two groups explicitly modeled radiation risk and found the number of radiation-related lung cancer deaths to be very small, in line with earlier reports (26).

Several limitations that affect generalizability and certainty of findings are worth noting. First, models assumed 100% adherence with screening. Second, models extrapolated benefits and harms derived from trials with short-term duration to lifetime follow-up in the U.S. population. Although the models were calibrated and are consistent with the NLST and PLCO trial, extrapolations beyond those trials' time horizons, screening intervals, and eligibility criteria introduce uncertainty. Third, 5 models, with different structures and assumptions, showed some variability in their absolute predictions of benefits and harms (Table 3), although the ranking of strategies was consistent across models (Appendix Figure 4). Moreover, there is variance in the absolute level of lung cancer deaths averted between the models, ranging from 177 to 862, and variance in the overdiagnosis estimates, ranging from 72 to 426, for the A-55-80-30-15 scenario. Fourth, although extrapolations to the age span of 75 to 80 years seem reasonable (for example, the oldest participants in the NLST were screened until age 78 years), there are still limited observational data on screening in older individuals. We did note, however, that sicker individuals who were deemed less favorable candidates for possible surgical cure did not affect ranking of the strategies (data not shown).

Benefits were extrapolated from 1 large-scale trial in the United States with positive results, whereas 2 small fair-quality (27) European trials have published negative interim results (28, 29). However, these are not large enough to have statistical power to show a clinically plausible effect, in contrast to the Nederlands-Leuven Screening Onderzoek (NELSON) trial, which enrolled 15,822 individuals age 50 to 75 years and compared CT screening with no screening (30). Preliminary analyses showed that the percentage of lung cancer detected early is more favorable than in the NLST trial (31–33). Mortality results are still pending. The NELSON trial has primarily used volume-doubling times and volume measurements of lung nodules to define its referral strategy, thereby substantially reducing the number of positive and false-positive results: about 60% of referrals were for false-positive results, and the percentage of referrals was about 2% (32). It may therefore be feasible to reduce one of the important harms of lung cancer screening via changes in follow-up guidelines.

The criteria we simulated in our scenarios may not be ideal in clinical practice. Number of pack-years is a known moderate surrogate measure of risk (34); its use for inviting persons to participate in a program may lead to “screening desirable” answers. Use of a risk-prediction model in the PLCO trial, as compared with the NLST criteria, would have led to 41% fewer lung cancer cases being missed (35). In the coming years it may be possible to improve eligibility criteria for screening and adapt our models to incorporate broader eligibility criteria based on more complex measures of risk (36). It will also be important to investigate possible important differences between men and women. In general, studies demonstrate that women receive diagnoses at an earlier age and at a more favorable cancer stage, and more frequently are identified as having adenocarcinomas, compared with men (14, 37–41). Recently, subgroup analyses of NLST showed statistically significant reductions in lung cancer mortality in persons diagnosed with adenocarcinoma (relative risk, 0.75 [95% CI, 0.60 to 0.94]) and not for other histologic types. These results also showed borderline-significant interaction with sex (relative risk, 0.73 for women versus 0.92 for men; P = 0.08) (42).

Inviting asymptomatic individuals for screening and implementing a large-scale screening program should be considered only when the benefits clearly outweigh the harms. Our analysis provides a detailed account of the balance between harms and benefits of annual lung cancer screening to inform individuals, clinicians, and policymakers. But our predictions have some uncertainty and are contingent on high-quality screening, 100% adherence with screening, and closely coordinated follow-up and treatment protocols. Both future providers and possible recipients of lung cancer screening should be fully aware of this and opt for screening only after having been informed about these harms and benefits.

Return to Contents

References

1. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010;127:2893-917. [PMID: 21351269]
2. Siegel R, Naishadham D, Jemal A. Cancer statistics, 2013. CA Cancer J Clin. 2013;63:11-30. [PMID: 23335087]
3. Moolgavkar SH, Holford TR, Levy DT, Kong CY, Foy M, Clarke L, et al. Impact of reduced tobacco smoking on lung cancer mortality in the United States during 1975-2000. J Natl Cancer Inst. 2012;104:541-8. [PMID: 22423009]
4. Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, et al; National Lung Screening Trial Research Team. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365:395-409. [PMID: 21714641]
5. Heijnsdijk EA, Wever EM, Auvinen A, Hugosson J, Ciatto S, Nelen V, et al. Quality-of-life effects of prostate-specific antigen screening. N Engl J Med. 2012;367:595-605. [PMID: 22894572]
6. Aberle DR, Berg CD, Black WC, Church TR, Fagerstrom RM, Galen B, et al; National Lung Screening Trial Research Team. The National Lung Screening Trial: overview and study design. Radiology. 2011;258:243-53. [PMID: 21045183]
7. Oken MM, Hocking WG, Kvale PA, Andriole GL, Buys SS, Church TR, et al; PLCO Project Team. Screening by chest radiograph and lung cancer mortality: the Prostate, Lung, Colorectal, and Ovarian (PLCO) randomized trial. JAMA. 2011;306:1865-73. [PMID: 22031728]
8. Anderson CM, Burns DM, Dodd KW, Feuer EJ. Chapter 2: birth-cohort-specific estimates of smoking behaviors for the U.S. population. Risk Anal. 2012;32 Suppl 1:S14-24. [PMID: 22882884]
9. Rosenberg MA, Feuer EJ, Yu B, Sun J, Henley SJ, Shanks TG, et al. Chapter 3: cohort life tables by smoking status, removing lung cancer as a cause of death. Risk Anal. 2012;32 Suppl 1:S25-38. [PMID: 22882890]
10. Holford TR, Levy DT, McKay LA, Clarke L, Racine B, Meza R, et al. Birth-cohort-specific smoking histories: initiation, cessation, intensity, and prevalence patterns for the United States, 1965-2009. Am J Prev Med. 2013. [Forthcoming].
11. Jeon J, Meza R, Krapcho M, Clarke LD, Byrne J, Levy DT. Chapter 5: actual and counterfactual smoking prevalence rates in the U.S. population via microsimulation. Risk Anal. 2012;32 Suppl 1:S51-68. [PMID: 22882892]
12. Draisma G, Etzioni R, Tsodikov A, Mariotto A, Wever E, Gulati R, et al. Lead time and overdiagnosis in prostate-specific antigen screening: importance of methods and context. J Natl Cancer Inst. 2009;101:374-83. [PMID: 19276453]
13. Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. Eur J Oper Res. 1978;2:429-44.
14. Goldstraw P, Crowley J, Chansky K, Giroux DJ, Groome PA, Rami-Porta R, et al; International Association for the Study of Lung Cancer International Staging Committee. The IASLC Lung Cancer Staging Project: proposals for the revision of the TNM stage groupings in the forthcoming (seventh) edition of the TNM classification of malignant tumours. J Thorac Oncol. 2007;2:706-14. [PMID: 17762336]
15. Korfage IJ, de Koning HJ, Roobol M, Schröder FH, Essink-Bot ML. Prostate cancer diagnosis: the impact on patients' mental health. Eur J Cancer. 2006;42:165-70. [PMID: 16326098]
16. de Gelder R, van As E, Tilanus-Linthorst MM, Bartels CC, Boer R, Draisma G, et al. Breast cancer screening: evidence for false reassurance? Int J Cancer. 2008;123:680-6. [PMID: 18484587]
17. van der Aalst CM, van den Bergh KA, Willemsen MC, de Koning HJ, van Klaveren RJ. Lung cancer screening and smoking abstinence: 2 year follow-up data from the Dutch-Belgian randomised controlled lung cancer screening trial. Thorax. 2010;65:600-5. [PMID: 20627916]
18. van der Aalst CM, van Klaveren RJ, de Koning HJ. Does participation to screening unintentionally influence lifestyle behaviour and thus lifestyle-related morbidity? Best Pract Res Clin Gastroenterol. 2010;24:465-78. [PMID: 20833350]
19. van der Aalst CM, van Klaveren RJ, van den Bergh KA, Willemsen MC, de Koning HJ. The impact of a lung cancer computed tomography screening result on smoking abstinence. Eur Respir J. 2011;37:1466-73. [PMID: 21148233].
20. Mandelblatt JS, Cronin KA, Bailey S, Berry DA, de Koning HJ, Draisma G, et al; Breast Cancer Working Group of the Cancer Intervention and Surveillance Modeling Network. Effects of mammography screening under different screening schedules: model estimates of potential benefits and harms. Ann Intern Med. 2009;151:738-47. [PMID: 19920274]
21. Zauber AG, Lansdorp-Vogelaar I, Knudsen AB, Wilschut J, van Ballegooijen M, Kuntz KM. Evaluating test strategies for colorectal cancer screening: a decision analysis for the U.S. Preventive Services Task Force. Ann Intern Med. 2008;149:659-69. [PMID: 18838717]
22. Ma J, Ward EM, Smith R, Jemal A. Annual number of lung cancer deaths potentially avertable by screening in the United States. Cancer. 2013;119:1381-5. [PMID: 23440730]
23. Bach PB, Mirkin JN, Oliver TK, Azzoli CG, Berry DA, Brawley OW, et al. Benefits and harms of CT screening for lung cancer: a systematic review. JAMA. 2012;307:2418-29. [PMID: 22610500]
24. de Gelder R, Heijnsdijk EA, van Ravesteyn NT, Fracheboud J, Draisma G, de Koning HJ. Interpreting overdiagnosis estimates in population-based mammography screening. Epidemiol Rev. 2011;33:111-21. [PMID: 21709144]
25. van Ravesteyn NT, Miglioretti DL, Stout NK, Lee SJ, Schechter CB, Buist DS, et al. Tipping the balance of benefits and harms to favor screening mammography starting at age 40 years: a comparative modeling study of risk. Ann Intern Med. 2012;156:609-17. [PMID: 22547470]
26. Berrington de González A, Kim KP, Berg CD. Low-dose lung computed tomography screening before age 55: estimates of the mortality reduction required to outweigh the radiation-induced cancer risk. J Med Screen. 2008;15:153-8. [PMID: 18927099]
27. Humphrey LL, Deffebach M, Pappas M, Baumann C, Artis K, Mitchell JP, et al. Screening for lung cancer with low-dose computed tomography: a systematic review to update the U.S. Preventive Services Task Force recommendation. Ann Intern Med. 2013;159:411-20. [PMID: 23897166]
28. Saghir Z, Dirksen A, Ashraf H, Bach KS, Brodersen J, Clementsen PF, et al. CT screening for lung cancer brings forward early disease. The randomised Danish Lung Cancer Screening Trial: status after five annual screening rounds with low-dose CT. Thorax. 2012;67:296-301. [PMID: 22286927]
29. Pastorino U, Rossi M, Rosato V, Marchianò A, Sverzellati N, Morosi C, et al. Annual or biennial CT screening versus observation in heavy smokers: 5-year results of the MILD trial. Eur J Cancer Prev. 2012;21:308-15. [PMID: 22465911]
30. van Iersel CA, de Koning HJ, Draisma G, Mali WP, Scholten ET, Nackaerts K, et al. Risk-based selection from the general population in a screening trial: selection criteria, recruitment and power for the Dutch-Belgian randomised lung cancer multi-slice CT screening trial (NELSON). Int J Cancer. 2007;120:868-74. [PMID: 17131307]
31. Horeweg N, van der Aalst CM, Vliegenthart R, Zhao Y, Xie X, Scholten ET, et al. Volumetric computed tomography screening for lung cancer: three rounds of the NELSON trial. Eur Respir J. 2013;42:1659-67. [PMID: 23845716]
32. Horeweg N, van der Aalst CM, Thunnissen E, Nackaerts K, Weenink C, Groen HJ, et al. Characteristics of lung cancers detected by computer tomography screening in the randomized NELSON trial. Am J Respir Crit Care Med. 2013;187:848-54. [PMID: 23348977]
33. van Klaveren RJ, Oudkerk M, Prokop M, Scholten ET, Nackaerts K, Vernhout R, et al. Management of lung nodules detected by volume CT scanning. N Engl J Med. 2009;361:2221-9. [PMID: 19955524]
34. Peto J. That the effects of smoking should be measured in pack-years: misconceptions 4 [Editorial]. Br J Cancer. 2012;107:406-7. [PMID: 22828655]
35. Tammemägi MC, Katki HA, Hocking WG, Church TR, Caporaso N, Kvale PA, et al. Selection criteria for lung-cancer screening. N Engl J Med. 2013;368:728-36. [PMID: 23425165]
36. Kovalchik SA, Tammemagi M, Berg CD, Caporaso NE, Riley TL, Korch M, et al. Targeting of low-dose CT screening according to the risk of lung-cancer death. N Engl J Med. 2013;369:245-54. [PMID: 23863051]
37. Radzikowska E, Glaz P, Roszkowski K. Lung cancer in women: age, smoking, histology, performance status, stage, initial treatment and survival. Population-based study of 20 561 cases. Ann Oncol. 2002;13:1087-93. [PMID: 12176788]
38. Sagerup CM, Småstuen M, Johannesen TB, Helland Å, Brustugun OT. Sex-specific trends in lung cancer incidence and survival: a population study of 40,118 cases. Thorax. 2011;66:301-7. [PMID: 21199818]
39. Caldarella A, Crocetti E, Comin CE, Janni A, Pegna AL, Paci E. Gender differences in non-small cell lung cancer: a population-based study. Eur J Surg Oncol. 2007;33:763-8. [PMID: 17306497]
40. Schwartz KL, Crossley-May H, Vigneau FD, Brown K, Banerjee M. Race, socioeconomic status and stage at diagnosis for five common malignancies. Cancer Causes Control. 2003;14:761-6. [PMID: 14674740]
41. Ringer G, Smith JM, Engel AM, Hendy MP, Lang J. Influence of sex on lung cancer histology, stage, and survival in a midwestern United States tumor registry. Clin Lung Cancer. 2005;7:180-2. [PMID: 16354312]
42. Pinsky PF, Church TR, Izmirlian G, Kramer BS. The National Lung Screening Trial: results stratified by demographics, smoking history, and lung cancer histology. Cancer. 2013. [PMID: 24037918]
43. Hazelton WD, Moolgavkar SH, Curtis SB, Zielinski JM, Ashmore JP, Krewski D. Biologically based analysis of lung cancer incidence in a large Canadian occupational cohort with low-dose ionizing radiation exposure, and comparison with Japanese atomic bomb survivors. J Toxicol Environ Health A. 2006;69:1013-38. [PMID: 16840251]
44. Cardis E, Gilbert ES, Carpenter L, Howe G, Kato I, Armstrong BK, et al. Effects of low doses and low dose rates of external ionizing radiation: cancer mortality among nuclear industry workers in three countries. Radiat Res. 1995;142:117-32. [PMID: 7724726]
45. Meza R, ten Haaf K, Kong CY, et al. Comparative analysis of five lung cancer natural history and screening models that reproduce outcomes of the NLST and PLCO trials. Cancer. [In press]

Return to Contents

Copyright and Source Information

Source: This article was first published in Annals of Internal Medicine (Ann Intern Med 2013; 31 December).

Disclaimer: The contents of this report are solely the responsibility of the authors and do not necessarily represent the official views of the National Cancer Institute or the Agency for Healthcare Research and Quality.

Acknowledgment: The authors thank Melecia Miller, MPH (formerly of Massachusetts General Hospital), Suresh Moolgavkar (Fred Hutchinson Cancer Research Center), and Arry de Bruijn (Erasmus Medical Center).

Grant Support: This report is based on research conducted by the National Cancer Institute's CISNET through support from an Interagency Agreement with the Agency for Healthcare Research and Quality, Rockville, Maryland (Administrative Supplement to U01 CA152956).

Potential Conflicts of Interest: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M13-2316.

Requests for Single Reprints: Harry J. de Koning, MD, PhD, Department of Public Health, Erasmus Medical Center, P.O. Box 2040, 3000 CA Rotterdam, the Netherlands; e-mail, h.dekoning@erasmusmc.nl.

AHRQ Publication No. 13-05196-EF-5
Current as of December 2013

Return to Contents


Internet Citation:

de Koning HJ, Meza R, Plevritis SK, ten Haaf K, Munshi VN, Jeon J, Erdogan SA, Kong CY, Han SS, van Rosmalen J, Choi SE, Pinsky PF, Berrington de Gonzalez A, Berg CD, Black WC, Tammemagi CM, Hazelton WD, Feuer EJ, McMahon PM. Benefits and Harms of Computed Tomography Lung Cancer Screening Strategies: A Comparative Modeling Study for the U.S. Preventive Services Task Force. AHRQ Publication No. 13-05196-EF-5. December 2013. http://www.uspreventiveservicestaskforce.org/uspstf13/lungcan/lungcanmodelstudy.htm.


 

USPSTF Program Office   540 Gaither Road, Rockville, MD 20850