Screening for lung cancer using low-dose computed tomography: concerns about the application in low-risk individuals

Introduction

Lung cancer is the leading cause of cancer mortality, accounting for approximately 28% of all cancer-related deaths (1). The current estimate suggests that approximately 7% of the population born today will be diagnosed with lung cancer in their lifetime, and approximately 6% will die of it (SEER Cancer Statistics Review, 2014). According to GLOBOCAN 2012, 35.78% of all newly diagnosed lung cancer cases and 37.56% deaths of lung cancer occur in China. Moreover, the disease is projected to be the sixth leading cause of death worldwide and the third in high-income countries in 2030 (2). Thus, it is a major public health problem.

Despite the development of new therapeutic agents and technologies, the 5-year survival rate of 6% to 18% for lung cancer (3) has not improved significantly over the past 20 years (4). Nevertheless, when the disease is diagnosed at an early stage, its 5-year survival rate is up to 67% (5). However, only 16% of lung cancer patients are diagnosed at early stages (1), whereas 61% of women with breast cancer and 91% of men with prostate cancer are diagnosed at early stage, owing to improvements in early detection and treatment. Consequently, the mortality rates for breast and prostate cancers have decreased from their peaks by 34% and 45%, respectively (1). Therefore, reliable detection and treatment of lung cancer in its earlier stages is a promising approach to improving the prognosis of lung cancer.

Screening for lung cancer dates back to 1968 in the United Kingdom, and several screening methods were tried, including chest X-ray (CXR), CXR with sputum cytology, serum biomarker testing, and fiber optic examination of the bronchial passages. However, these methods yielded limited efficacy in survival improvement, possible owing to the disease’s clinical and pathologic heterogeneity (6). In 2011, the initial results of the National Lung Screening Trial (NLST) (7) were published, reporting a relative 20% reduction in lung cancer-specific deaths among high-risk participants undergoing low-dose computed tomography (LDCT) compared to those receiving CXR. The acquisition variables of LDCT were chosen to reduce exposure to an average effective dose of 1.5 mSv. The criteria for high-risk participants of the NLST included patients aged between 55 to 74 years, those currently smoking 30 pack-year, or former smokers who quit within the past 15 years.

The NLST trial was acclaimed as a major breakthrough in lung cancer screening. As a result, lung cancer screening using LDCT was recommended by various organizations, including the American Association of Thoracic Surgery (8,9), American College of Chest Physicians, American Society of Clinical Oncology, American Thoracic Society (10), National Comprehensive Cancer Network, American Lung Association, American Cancer Society (11), and the United States Preventive Services Task Force. Although a major push for primary care providers to incorporate lung cancer screening using LDCT into their practices is expected, clinicians inevitably encounter patients who are interested in screening but do not meet the previously described high-risk criteria. Thus, the question remains whether it is rational to screen as many people as quickly as possible or how these screening candidates should be wisely selected. As it is uncertain if people who do not meet the NLST inclusion criteria have a low risk of developing lung cancer and whether they benefit from screening, the potential harms associated with the procedure and its balance among cost, risks, and benefits should be carefully considered. In the present article, we systematically review the practical aspects of lung cancer screening using LDCT to provide an evidence-based analysis for whether LDCT screening should be expanded to the low-risk population.

Is there sufficient evidence to support lung cancer screening using LDCT?

We conducted a systematic review of current literature on the harms and benefits of lung cancer screening using LDCT and found 107 relevant clinical trials. Mass lung cancer screening programs using CT have been active since the mid-nineties in Japan (12-14), and many uncontrolled studies were launched during the following years in western countries (15-20).

All these studies demonstrated that screening with spiral CT allowed the detection of a high proportion of early-stage lung cancer cases. Furthermore, the International Early Lung Cancer Action Project reported a 10-year survival rate of 92% in patients with resectable stage I disease, whereas that of the whole study cohort was 80% (21). However, it was not a randomized controlled trial (RCT), and thus inevitably affected by cofounders. Therefore, we decided to focus our search on RCTs, in which the benefit of screening in terms of mortality reduction was directly compared between the study and control groups. The following 11 RCTs were found and their data were reviewed.

• LSS: Lung Screening Study (22-24);
• NLST (7,25);
• DANTE: Detection and Screening of Early Lung Cancer by Novel Imaging Technology and Molecular Assays (26,27);
• DLCST: Danish Lung Cancer Screening Trial (28,29);
• MILD: Multicentric Italian Lung Detection study (30);
• NELSON: Nederlands-Leuvens Longkanker Screenings Onderzoek trial, or Dutch-Belgian Lung Cancer Screening Trial (31,32);
• ITALUNG: Italian Lung Cancer Computed Tomography Screening Trial (33,34);
• Depiscan: a French pilot lung screening RCT (35);
• LUSI: German lung cancer screening intervention study (36);
• UKLS: United Kingdom Lung Screening Trial (37);
• JECS: Japanese randomized trial for evaluating the Efficacy of low-dose thoracic CT screening for lung cancer in non-smokers and smokers of 30 pack-years aged 50-64 years (38).

The NLST (7,25) was the largest study, comparing LDCT with CXR for lung cancer screening. The results indicated a reduction of 20% [95% confidence interval (CI), 6.8-26.7; P=0.004] in lung cancer-specific mortality and reduction of 6.7% (95% CI, 1.2-13.6; P=0.02) in all-cause mortality. The chance of dying from lung cancer was 0.33% lower for the LDCT group over the study period, and that is 1 lung cancer-specific death was prevented for every 310 individuals being screened. The other 10 smaller RCTs on lung cancer screening using LDCT were conducted or are ongoing in the United States, Europe and Japan. To date, results from four studies, LSS, DANTE, DLCST, and MILD, have been reported. DANTE, DLCST, and MILD were conducted in Europe, whereas LSS was a pilot study of the subsequent NLST. Therefore, we only summarized results from NLST, DANTE, DLCST, and MILD in Table 1. However, all three European trials reported no mortality reduction benefit from LDCT lung cancer screening. The reasons for the inconsistent results have been previously discussed in several reviews (39,40). As shown in Table 1, a wide variability in the controls, sample size, demographic characteristics (sex and age), smoking history, tomogram thickness, screening intervals and duration, and follow-up duration might account for the different outcomes.

Table 1 Summary of previously published randomized clinical trials on low-dose computed tomography screening for lung cancer
Full table

In addition, most screening studies were conducted in academic institutes or large hospitals with the participation of specialized thoracic radiologists and certified thoracic surgeons, which raises concerns about the effectiveness of screening in the community or at smaller facilities. Therefore, although LDCT screening appears promising, it is also a means of clinical intervention in its infancy with many unanswered questions, including the optimal time for screening initiation, duration, and intervals. Furthermore, other issues such as overdiagnosis, risk definition, patient selection, and financial burden also need to be carefully addressed. Although ongoing randomized trials might help resolve some of these matters and validate the NLST results, future studies are warranted to provide a definitive answer regarding the impact of LDCT screening on lung cancer-specific mortality at the population level.

Is it rational to offer LDCT screening for lung cancer to low-risk individuals?

The NLST reported that three annual rounds of LDCT screening resulted in a 20% relative decrease in death from lung cancer among high-risk participants, as compared to CXR. Further stratification of the participants into five risk categories using a validated prediction model showed that those with the lowest risk (the first quintile) accounted for only 1% of the prevented lung cancer-specific deaths, whereas 88% of the death prevention was from participants with a higher risk (the third to fifth quintiles) (41). Such a result indicated that individuals with a lower risk might benefit less from LDCT screening. Furthermore, the following potential limitations and harms associated with LDCT screening needed to be recognized:

• False-positive results: the average nodular detection rate was 20% in the NLST, but varied from 3% (42) to 30% (ITALUNG) in RCTs and 5% (16,43-45) to 51% (20,46) in cohort studies. In the NLST, 96.4% of the positive results in the low-dose CT group were false positive results across the three rounds and other studies reported that more than 90% of the nodules were benign (7,10,22,23,28,29,31-35);
• Radiation exposure: the effective dose of radiation from LDCT is estimated to be 1.5 mSv per examination, but it may vary in clinical practice. Furthermore, nodule detection might require more imaging procedures, such as diagnostic chest CT (about 8 mSv) (47) or positron emission tomography-CT (about 14 mSv) (47-49), which increases the total exposure and accounts for most radiation doses in these screening studies;
• Additional invasive procedures: in the NLST, 73% of patients with benign lesions had invasive nonsurgical procedures (7); 1.2% underwent invasive procedure such as needle biopsy or bronchoscopy, and 0.7% underwent thoracoscopy or mediastinoscopy (7). The percentage of unnecessary surgeries was 13% in DANTE and around 2% in other studies (50);
• Death and complications: the frequency of death occurring within 2 months of a diagnostic evaluation of a detected finding was 8 per 10,000 individuals screened using LDCT and 5 per 10,000 of those who received chest radiography in the NLST. The majority of complications occurred after surgical procedures. In the total studied population, the risks of death and major complications following diagnostic events for benign nodules was higher in the LDCT group than in the chest radiography group (4.1 and 4.5 per 10,000 vs. 1.1 and 1.5 per 10,000, respectively) (50).
• Overdiagnosis: overdiagnosis is the detection of an extra quota of indolent tumors that would have no impact on patients’ life expectancy even if undiagnosed. Although early RCTs of CXR suggested that lung cancer screening resulted in an overdiagnosis rate of exceeding 25% (51,52), it is impossible to estimate the definitive magnitude of overdiagnosis from the NLST because of the study design comparing LDCT and CXR. More evidence from prospective RCTs may eventually provide an estimate of overdiagnosis rate;
• Stage detection shifting: there is considerable interest in shifting detection to stages during which intervention could be curative. Screening did not reduce lung cancer stages detected after the first round, and only a slight decrease was reported in stage III and IV diseases detected in the third round compared with those found after the first round (37.8% vs. 30.4%) (7);
• Psychological effect: false-positive findings and overdiagnosed tumors can cause anxiety. The NELSON trial results suggested that short-term lung cancer-specific distress was significantly high, but long-term evaluation indicated the resolution of such short-term anxiety; however, a second indeterminate finding was not associated with increased lung cancer-specific distress (53,54).

In addition to the above-mentioned limitations, there are other uncertainties regarding the harms and benefits of LDCT screening. An analysis of the NLST data using predicating models indicated that approximately 1 cancer death might be caused by radiation from imaging per 2,500 individuals screened (10), and the lung cancer-specific death prevention benefit was greater than the radiation risk that might manifest 10 to 20 years later. However, it would be less favorable for younger individuals or those with a low risk of developing lung cancer (10). Therefore, the NLST findings should be neither minimized nor overstated when more data on LDCT screening are due to be reported in the near future. Additionally, considerable risks must be overcome before LDCT can be widely offered as a preventative screening method to those at risk of developing lung cancer. Currently, LDCT may not be cost-effective when provided to individuals with a low risk of lung cancer development, especially in a setting that might involve higher frequency of unnecessary interventions and procedures. Thus, for optimal cost-effectiveness, individuals with a sufficiently high risk of developing lung cancer need to be identified so that the benefit-to-harm ratio of the screening can be maximized (55).

Is there a clear definition of high and low risk for lung cancer development?

As shown in the NLST, screening using LDCT prevented the greatest number of deaths among participants who were at the highest risk for the disease, whereas the number of prevented death was very limited for those at the lowest risk. These findings provided the empirical support for risk-based targeting for such screening. Although it is generally agreed that screening should be limited to high-risk individuals for whom the potential benefits of LDCT screening would outweigh its harms, the exact definition of such a high-risk population is unclear.

Most available recommendations are based on the NLST high-risk criteria, which focus only on patients’ age and smoking history. Although such a definition of risk was practical for the purpose of a clinical trial, it is not as useful for screening target selection because certain valuable predictors are omitted. Smoking accounts for 80% of the worldwide lung cancer burden in men and at least 50% of that in women (56); however, approximately 85% of heavy smokers do not develop lung cancer (57). Moreover, the NLST enrolled a younger and healthier population with only 8.8% of the study participants in the oldest category (70-74 years) (7), which might challenge the generalization of the study results to that age group. This particular point cannot be overstated, as the average age of lung cancer diagnosis is 70 years, and it should make clinicians wary of applying the mortality statistics for surgery to individuals in the oldest group.

In addition, other risk factors associated with lung cancer includes second-hand smoking (58); exposure to solid fuel smoke (59) or outdoor particulate matter (60); occupational exposures (61); family history (62); genetic polymorphisms such as those in tumor protein p53, excision repair cross-complementation group 1, or methylenetetrahydrofolate reductase (63-65); radon (66); other pulmonary diseases (67); and sex differences (68). Furthermore, it would be politically problematic to offer publicly funded medical interventions solely to heavy smokers, when non-/light smokers might also be at a high risk owing to other environmental, occupational, and genetic factors. Meanwhile, the low detection rate for prevalent and especially incident lung cancers are key elements in explaining the high cost-effectiveness ratio of lung cancer screening using LDCT alone. This clearly indicates the need for multidimensional integrated strategies to increase the rate of screen-detected lung cancers with LDCT, possibly via the inclusion of subjects with other risk factors besides smoking history and age.

Multiple lung cancer risk prediction models with good performance have been established to facilitate such strategies. An accurate risk prediction model is more efficient in identifying individuals who are likely to develop lung cancer and die from the disease than simple factors such as age and pack-years of smoking. Currently, there have been many lung cancer risk prediction models developed. Different models have included variable risk factors which could be categorized into epidemiology factors, clinical factors as well as genetic and molecular biomarkers. We picked out the most popularly studied models, and list the risk factors incorporated in each model (shown in Table 2).

Table 2 Risk factors incorporated in the risk models for lung cancer
Full table

Although the validation of these models shows moderate to high discrimination and calibration, there are certain limitations that can affect their accuracy and application. First, Maisonneuve et al. found that the common epidemiological risk factors had relatively low discriminatory power to predict the possibility of lung cancer development; therefore, clinical factors as well as genetic and molecular biomarkers were used to develop models, but their validation was insufficient (73). Second, the study participants selected to validate these models might not be adequately representative for generalization. For example, the participants in the Tammemagi model were 55-74 years of age at the time of enrollment and in general, were of higher socioeconomic status than the general population, possibly resulting in a healthy volunteer effect and limiting the model’s external generalizability (69). Third, categorization of continuous data could lead to loss of information and predictive ability, as used in the Bach and Liverpool Lung Project models which divided continuous smoking history data into four categories (71,75). Fourth, selection of predictive variables for entry into the multivariable models was based on a P value of less than 0.05 in univariate analysis, which could result in important predictors being left out more often than when a less stringent P value cut-off was used, such as in the Spitz model (78).

Currently, no prediction model is utilized in clinical settings. Among the clinical trials on lung cancer screening, only the UKLS applied a prediction model for selection criteria. The better understanding of lung cancer and identification of more potential risk factors could make screening for the disease more accurate and complex at the same time, and the current prediction models could certainly be improved. Therefore, it is difficult to provide an accurate definition of high or low risk for lung cancer. Only the screening guidelines issued by the NCCN in 2011 defined low-risk individuals as those aged <50 years and/or having a smoking history of <20 pack-years, and lung cancer screening was not recommended for these (6). The incorporation of other well-known risk factors has not been studied. Thus, there is currently no evidence to suggest a re-assessment of screening selection criteria. In order to identify individuals for whom the harm/benefit balance of LDCT screening is favorable, a good risk prediction model for lung cancer is certainly needed.

How should we address the current and future implementation of LDCT screening?

The international debate on whether to implement CT lung cancer screening programs is ongoing with unresolved issues. To date, screening programs have reported that 6-34% of all patients with benign lesions have undergone surgical treatment. Such variability is due primarily to the different protocols used and providers’ experience. Therefore, only the adoption of a shared protocol and experienced multidisciplinary teams may ensure the lowest possible rate of futile procedures.

An international review of lung cancer screening was conducted during a workshop convened by the International Association for the Study of Lung Cancer, and its report was published (84) after that of the NLST results. The workshop participants provided six recommendations for future priorities: to identify high-risk individuals for lung cancer CT screening programs, to develop radiological guidelines for use in developing national screening programs, to create guidelines for the clinical work-up of indeterminate nodules resulting from CT screening programs, to develop guidelines for pathology reporting of nodules from lung cancer CT screening programs, to make recommendations for surgical and therapeutic interventions of suspicious nodules identified through lung cancer CT screening programs, and to integrate smoking cessation practices into future national lung cancer CT screening programs. However, optimum resolutions of the issues are still awaited.

The ongoing RCTs are likely to provide further evidence for mortality reduction advantage of CT screening and its cost-effectiveness. They might also offer a better insight into risk stratification of the general population who need to be screened and a robust radiological protocol to reduce false-positive results and help with management decisions about indeterminate nodules. Additionally, in-depth data are now emerging from the use of minimally surgical approaches, especially video-assisted thoracoscopic surgery for small CT-identified nodules. All these factors will contribute greatly to reducing the harms and increasing the benefits of CT screening. In the meantime, we need to prepare for lung cancer screening with an integrated smoking cessation policy because this combined approach might save more lives than any other lung cancer intervention in the near future.

Conclusions

The high incidence and mortality of lung cancer highlights the need for ongoing prevention and control strategy to reduce the disease burden. Although LDCT showed promising results in the NLST trial and has become a recommendation for lung cancer screening in many guidelines, there are still debates on its cost-effectiveness. The value of LDCT in lung cancer screening for high-risk individuals should be confirmed in more trials. Currently, the procedure is not recommended for low-risk patients, although some might be missed based on the current definition of risk factors. The accurate definition of risk factors and better predictive models are particularly important for future lung cancer screening trials. Further studies are urgently needed to solve the problems involved in lung cancer screening in order to improve the disease’s outcomes.

Acknowledgements

Disclosure: The authors declare no conflict of interest.

References

1. Siegel R, Ma J, Zou Z, et al. Cancer statistics, 2014. CA Cancer J Clin 2014;64:9-29. [PubMed]
2. Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med 2006;3:e442. [PubMed]
3. Youlden DR, Cramb SM, Baade PD. The International Epidemiology of Lung Cancer: geographical distribution and secular trends. J Thorac Oncol 2008;3:819-31. [PubMed]
4. Coleman MP, Rachet B, Woods LM, et al. Trends and socioeconomic inequalities in cancer survival in England and Wales up to 2001. Br J Cancer 2004;90:1367-73. [PubMed]
5. Mountain CF. Revisions in the International System for Staging Lung Cancer. Chest 1997;111:1710-7. [PubMed]
6. Xiang D, Zhang B, Doll D, et al. Lung cancer screening: from imaging to biomarker. Biomark Res 2013;1:4. [PubMed]
7. Aberle DR, Adams AM, Berg CD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 2011;365:395-409. [PubMed]
8. Jacobson FL, Austin JH, Field JK, et al. Development of The American Association for Thoracic Surgery guidelines for low-dose computed tomography scans to screen for lung cancer in North America: recommendations of The American Association for Thoracic Surgery Task Force for Lung Cancer Screening and Surveillance. J Thorac Cardiovasc Surg 2012;144:25-32. [PubMed]
9. Jaklitsch MT, Jacobson FL, Austin JH, et al. The American Association for Thoracic Surgery guidelines for lung cancer screening using low-dose computed tomography scans for lung cancer survivors and other high-risk groups. J Thorac Cardiovasc Surg 2012;144:33-8. [PubMed]
10. Bach PB, Mirkin JN, Oliver TK, et al. Benefits and harms of CT screening for lung cancer: a systematic review. JAMA 2012;307:2418-29. [PubMed]
11. Wender R, Fontham ET, Barrera E Jr, et al. American Cancer Society lung cancer screening guidelines. CA Cancer J Clin 2013;63:107-17. [PubMed]
12. Sobue T, Moriyama N, Kaneko M, et al. Screening for lung cancer with low-dose helical computed tomography: anti-lung cancer association project. J Clin Oncol 2002;20:911-20. [PubMed]
13. Nawa T, Nakagawa T, Kusano S, et al. Lung cancer screening using low-dose spiral CT: results of baseline and 1-year follow-up studies. Chest 2002;122:15-20. [PubMed]
14. Sone S, Li F, Yang ZG, et al. Results of three-year mass screening programme for lung cancer using mobile low-dose spiral computed tomography scanner. Br J Cancer 2001;84:25-32. [PubMed]
15. Bastarrika G, García-Velloso MJ, Lozano MD, et al. Early lung cancer detection using spiral computed tomography and positron emission tomography. Am J Respir Crit Care Med 2005;171:1378-83. [PubMed]
16. Diederich S, Wormanns D, Semik M, et al. Screening for early lung cancer with low-dose spiral CT: prevalence in 817 asymptomatic smokers. Radiology 2002;222:773-81. [PubMed]
17. Henschke CI, McCauley DI, Yankelevitz DF, et al. Early Lung Cancer Action Project: overall design and findings from baseline screening. Lancet 1999;354:99-105. [PubMed]
18. MacRedmond R, Logan PM, Lee M, et al. Screening for lung cancer using low dose CT scanning. Thorax 2004;59:237-41. [PubMed]
19. Pastorino U, Bellomi M, Landoni C, et al. Early lung-cancer detection with spiral CT and positron emission tomography in heavy smokers: 2-year results. Lancet 2003;362:593-7. [PubMed]
20. Swensen SJ, Jett JR, Hartman TE, et al. Lung cancer screening with CT: Mayo Clinic experience. Radiology 2003;226:756-61. [PubMed]
21. Henschke CI, Yankelevitz DF, Libby DM, et al. Survival of patients with stage I lung cancer detected on CT screening. N Engl J Med 2006;355:1763-71. [PubMed]
22. Gohagan J, Marcus P, Fagerstrom R, et al. Baseline findings of a randomized feasibility trial of lung cancer screening with spiral CT scan vs chest radiograph: the Lung Screening Study of the National Cancer Institute. Chest 2004;126:114-21. [PubMed]
23. Gohagan JK, Marcus PM, Fagerstrom RM, et al. 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. [PubMed]
24. Pinsky PF, Marcus PM, Kramer BS, et al. Diagnostic procedures after a positive spiral computed tomography lung carcinoma screen. Cancer 2005;103:157-63. [PubMed]
25. Aberle DR, DeMello S, Berg CD, et al. Results of the two incidence screenings in the National Lung Screening Trial. N Engl J Med 2013;369:920-31. [PubMed]
26. Infante M, Cavuto S, Lutman FR, et al. A randomized study of lung cancer screening with spiral computed tomography: three-year results from the DANTE trial. Am J Respir Crit Care Med 2009;180:445-53. [PubMed]
27. Infante M, Lutman FR, Cavuto S, et al. Lung cancer screening with spiral CT: baseline results of the randomized DANTE trial. Lung Cancer 2008;59:355-63. [PubMed]
28. Pedersen JH, Ashraf H, Dirksen A, et al. The Danish randomized lung cancer CT screening trial--overall design and results of the prevalence round. J Thorac Oncol 2009;4:608-14. [PubMed]
29. Saghir Z, Dirksen A, Ashraf H, 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. [PubMed]
30. Pastorino U, Rossi M, Rosato V, 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. [PubMed]
31. Baecke E, de Koning HJ, Otto SJ, et al. Limited contamination in the Dutch-Belgian randomized lung cancer screening trial (NELSON). Lung Cancer 2010;69:66-70. [PubMed]
32. Ru Zhao Y, Xie X, de Koning HJ, et al. NELSON lung cancer screening study. Cancer Imaging 2011;11 Spec No A:S79-84.
33. Lopes Pegna A, Picozzi G, Falaschi F, et al. Four-year results of low-dose CT screening and nodule management in the ITALUNG trial. J Thorac Oncol 2013;8:866-75. [PubMed]
34. Lopes Pegna A, Picozzi G, Mascalchi M, et al. Design, recruitment and baseline results of the ITALUNG trial for lung cancer screening with low-dose CT. Lung Cancer 2009;64:34-40. [PubMed]
35. Blanchon T, Bréchot JM, Grenier PA, et al. Baseline results of the Depiscan study: a French randomized pilot trial of lung cancer screening comparing low dose CT scan (LDCT) and chest X-ray (CXR). Lung Cancer 2007;58:50-8. [PubMed]
36. Becker N, Motsch E, Gross ML, et al. Randomized study on early detection of lung cancer with MSCT in Germany: study design and results of the first screening round. J Cancer Res Clin Oncol 2012;138:1475-86. [PubMed]
37. Baldwin DR, Duffy SW, Wald NJ, et al. UK Lung Screen (UKLS) nodule management protocol: modelling of a single screen randomised controlled trial of low-dose CT screening for lung cancer. Thorax 2011;66:308-13. [PubMed]
38. Kawamoto T, Nitta H, Murata K, et al. Rationale and study design of the Japan environment and children's study (JECS). BMC Public Health 2014;14:25. [PubMed]
39. Humphrey LL, Deffebach M, Pappas M, et al. Screening for lung cancer with low-dose computed tomography: a systematic review to update the US Preventive services task force recommendation. Ann Intern Med 2013;159:411-20. [PubMed]
40. Tammemagi MC, Lam S. Screening for lung cancer using low dose computed tomography. BMJ 2014;348:g2253. [PubMed]
41. Kovalchik SA, Tammemagi M, Berg CD, et al. Targeting of low-dose CT screening according to the risk of lung-cancer death. N Engl J Med 2013;369:245-54. [PubMed]
42. Garg K, Keith RL, Byers T, et al. Randomized controlled trial with low-dose spiral CT for lung cancer screening: feasibility study and preliminary results. Radiology 2002;225:506-10. [PubMed]
43. Diederich S, Thomas M, Semik M, et al. Screening for early lung cancer with low-dose spiral computed tomography: results of annual follow-up examinations in asymptomatic smokers. Eur Radiol 2004;14:691-702. [PubMed]
44. Diederich S, Wormanns D, Lenzen H, et al. Screening for asymptomatic early bronchogenic carcinoma with low dose CT of the chest. Cancer 2000;89:2483-4. [PubMed]
45. Novello S, Fava C, Borasio P, et al. Three-year findings of an early lung cancer detection feasibility study with low-dose spiral computed tomography in heavy smokers. Ann Oncol 2005;16:1662-6. [PubMed]
46. Swensen SJ, Jett JR, Sloan JA, et al. Screening for lung cancer with low-dose spiral computed tomography. Am J Respir Crit Care Med 2002;165:508-13. [PubMed]
47. Mettler FA Jr, Huda W, Yoshizumi TT, et al. Effective doses in radiology and diagnostic nuclear medicine: a catalog. Radiology 2008;248:254-63. [PubMed]
48. Aberle DR, Berg CD, Black WC, et al. The National Lung Screening Trial: overview and study design. Radiology 2011;258:243-53. [PubMed]
49. Smith-Bindman R, Lipson J, Marcus R, et al. Radiation dose associated with common computed tomography examinations and the associated lifetime attributable risk of cancer. Arch Intern Med 2009;169:2078-86. [PubMed]
50. Ruano-Ravina A, Pérez Ríos M, Fernández-Villar A. Lung cancer screening with low-dose computed tomography after the National Lung Screening Trial. The debate is still open. Arch Bronconeumol 2013;49:158-65. [PubMed]
51. Kubík AK, Parkin DM, Zatloukal P. Czech Study on Lung Cancer Screening: post-trial follow-up of lung cancer deaths up to year 15 since enrollment. Cancer 2000;89:2363-8. [PubMed]
52. Marcus PM, Bergstralh EJ, Fagerstrom RM, et al. Lung cancer mortality in the Mayo Lung Project: impact of extended follow-up. J Natl Cancer Inst 2000;92:1308-16. [PubMed]
53. van den Bergh KA, Essink-Bot ML, Borsboom GJ, et al. Long-term effects of lung cancer computed tomography screening on health-related quality of life: the NELSON trial. Eur Respir J 2011;38:154-61. [PubMed]
54. van den Bergh KA, Essink-Bot ML, Borsboom GJ, et al. Short-term health-related quality of life consequences in a lung cancer CT screening trial (NELSON). Br J Cancer 2010;102:27-34. [PubMed]
55. van Klaveren RJ, de Koning HJ, Mulshine J, et al. Lung cancer screening by spiral CT. What is the optimal target population for screening trials? Lung Cancer 2002;38:243-52. [PubMed]
56. Jemal A, Bray F, Center MM, et al. Global cancer statistics. CA Cancer J Clin 2011;61:69-90. [PubMed]
57. van Zandwijk N. Chemoprevention in lung carcinogenesis--an overview. Eur J Cancer 2005;41:1990-2002. [PubMed]
58. Sarna L. Enough is enough. Clin J Oncol Nurs 2014;18:141-2. [PubMed]
59. Kurmi OP, Arya PH, Lam KB, et al. Lung cancer risk and solid fuel smoke exposure: a systematic review and meta-analysis. Eur Respir J 2012;40:1228-37. [PubMed]
60. Hamra GB, Guha N, Cohen A, et al. Outdoor particulate matter exposure and lung cancer: a systematic review and meta-analysis. Environ Health Perspect 2014;122:906-11. [PubMed]
61. Field RW, Withers BL. Occupational and environmental causes of lung cancer. Clin Chest Med 2012;33:681-703. [PubMed]
62. Coté ML, Liu M, Bonassi S, et al. Increased risk of lung cancer in individuals with a family history of the disease: a pooled analysis from the International Lung Cancer Consortium. Eur J Cancer 2012;48:1957-68. [PubMed]
63. Ye XH, Bu ZB, Feng J, et al. Association between the TP53 polymorphisms and lung cancer risk: a meta-analysis. Mol Biol Rep 2014;41:373-85. [PubMed]
64. Zhang XD, Li YT, Yang SY, et al. Meta-analysis on MTHFR polymorphism and lung cancer susceptibility in East Asian populations. Biomed Rep 2013;1:440-6. [PubMed]
65. Zhu J, Hua RX, Jiang J, et al. Association studies of ERCC1 polymorphisms with lung cancer susceptibility: a systematic review and meta-analysis. PLoS One 2014;9:e97616. [PubMed]
66. Torres-Durán M, Barros-Dios JM, Fernández-Villar A, et al. Residential radon and lung cancer in never smokers. A systematic review. Cancer Lett 2014;345:21-6. [PubMed]
67. Brenner DR, Boffetta P, Duell EJ, et al. Previous lung diseases and lung cancer risk: a pooled analysis from the International Lung Cancer Consortium. Am J Epidemiol 2012;176:573-85. [PubMed]
68. Yu Y, Liu H, Zheng S, et al. Gender susceptibility for cigarette smoking-attributable lung cancer: a systematic review and meta-analysis. Lung Cancer 2014;85:351-60. [PubMed]
69. Tammemagi CM, Pinsky PF, Caporaso NE, et al. Lung cancer risk prediction: Prostate, Lung, Colorectal And Ovarian Cancer Screening Trial models and validation. J Natl Cancer Inst 2011;103:1058-68. [PubMed]
70. Tammemägi MC, Katki HA, Hocking WG, et al. Selection criteria for lung-cancer screening. N Engl J Med 2013;368:728-36. [PubMed]
71. Bach PB, Kattan MW, Thornquist MD, et al. Variations in lung cancer risk among smokers. J Natl Cancer Inst 2003;95:470-8. [PubMed]
72. Cronin KA, Gail MH, Zou Z, et al. Validation of a model of lung cancer risk prediction among smokers. J Natl Cancer Inst 2006;98:637-40. [PubMed]
73. Maisonneuve P, Bagnardi V, Bellomi M, et al. Lung cancer risk prediction to select smokers for screening CT--a model based on the Italian COSMOS trial. Cancer Prev Res (Phila) 2011;4:1778-89. [PubMed]
74. Veronesi G, Maisonneuve P, Rampinelli C, et al. Computed tomography screening for lung cancer: results of ten years of annual screening and validation of cosmos prediction model. Lung Cancer 2013;82:426-30. [PubMed]
75. Cassidy A, Myles JP, van Tongeren M, et al. The LLP risk model: an individual risk prediction model for lung cancer. Br J Cancer 2008;98:270-6. [PubMed]
76. Etzel CJ, Kachroo S, Liu M, et al. Development and validation of a lung cancer risk prediction model for African-Americans. Cancer Prev Res (Phila) 2008;1:255-65. [PubMed]
77. Park S, Nam BH, Yang HR, et al. Individualized risk prediction model for lung cancer in Korean men. PLoS One 2013;8:e54823. [PubMed]
78. Spitz MR, Hong WK, Amos CI, et al. A risk model for prediction of lung cancer. J Natl Cancer Inst 2007;99:715-26. [PubMed]
79. Hoggart C, Brennan P, Tjonneland A, et al. A risk model for lung cancer incidence. Cancer Prev Res (Phila) 2012;5:834-46. [PubMed]
80. Hippisley-Cox J, Coupland C. Identifying patients with suspected lung cancer in primary care: derivation and validation of an algorithm. Br J Gen Pract 2011;61:e715-23. [PubMed]
81. Iyen-Omofoman B, Tata LJ, Baldwin DR, et al. Using socio-demographic and early clinical features in general practice to identify people with lung cancer earlier. Thorax 2013;68:451-9. [PubMed]
82. Spitz MR, Etzel CJ, Dong Q, et al. An expanded risk prediction model for lung cancer. Cancer Prev Res (Phila) 2008;1:250-4. [PubMed]
83. Raji OY, Agbaje OF, Duffy SW, et al. Incorporation of a genetic factor into an epidemiologic model for prediction of individual risk of lung cancer: the Liverpool Lung Project. Cancer Prev Res (Phila) 2010;3:664-9. [PubMed]
84. Field JK, Smith RA, Aberle DR, et al. International Association for the Study of Lung Cancer Computed Tomography Screening Workshop 2011 report. J Thorac Oncol 2012;7:10-9. [PubMed]