Lung Cancer Screening by Low-Dose Computed Tomography – Part 1: Expected Benefits, Possible Harms, and Criteria for Eligibility and Population Targeting

Abstract

Background Trials in the USA and Europe have convincingly demonstrated the efficacy of screening by low-dose computed tomography (LDCT) as a means to lower lung cancer mortality, but also document potential harms related to radiation, psychosocial stress, and invasive examinations triggered by false-positive screening tests and overdiagnosis. To ensure that benefits (lung cancer deaths averted; life years gained) outweigh the risk of harm, lung cancer screening should be targeted exclusively to individuals who have an elevated risk of lung cancer, plus sufficient residual life expectancy.

Methods and Conclusions Overall, randomized screening trials show an approximate 20 % reduction in lung cancer mortality by LDCT screening. In view of declining residual life expectancy, especially among continuing long-term smokers, risk of being over-diagnosed is likely to increase rapidly above the age of 75. In contrast, before age 50, the incidence of LC may be generally too low for screening to provide a positive balance of benefits to harms and financial costs. Concise criteria as used in the NLST or NELSON trials may provide a basic guideline for screening eligibility. An alternative would be the use of risk prediction models based on smoking history, sex, and age as a continuous risk factor. Compared to concise criteria, such models have been found to identify a 10 % to 20 % larger number of LC patients for an equivalent number of individuals to be screened, and additionally may help provide security that screening participants will all have a high-enough LC risk to balance out harm potentially caused by radiation or false-positive screening tests.

Key Points: 

• LDCT screening can significantly reduce lung cancer mortality

• Screening until the age of 80 was shown to be efficient in terms of cancer deaths averted; in terms of LYG relative to overdiagnosis, stopping at a younger age (e. g. 75) may have greater efficiency

• Risk models may improve the overall net benefit of lung cancer screening

Citation Format

• Kaaks R, Delorme S. Lung Cancer Screening by Low-Dose Computed Tomography – Part 1: Expected Benefits, Possible Harms, and Criteria for Eligibility and Population Targeting. Fortschr Röntgenstr 2021; 193: 527 – 536

Zusammenfassung

Hintergrund Zahlreiche Studien in den USA und Europa haben zeigen können, dass durch Screening mit Niedrigdosis-Computertomografie (Low-Dose-CT, LDCT) der Lunge die Sterblichkeit an Lungenkrebs gesenkt werden kann, haben aber auch damit verbundene Risiken aufgezeigt, die sich durch ionisierende Strahlung, emotionalen Stress, Eingriffe infolge falsch positiver Befunde oder Überdiagnose ergeben. Um zu gewährleisten, dass die Risiken durch den möglichen Nutzen (abgewendeter Tod durch Lungenkrebs, Gewinn an Lebensjahren) aufgewogen werden, sollte Lungenkrebs-Screening auf Personen zielen, deren Lungenkrebsrisiko erhöht ist und deren verbleibende Lebenserwartung ausreichend hoch ist.

Methoden und Schlussfolgerungen Im Ganzen beträgt die Senkung der Lungenkrebssterblichkeit durch LDCT-Screening ca. 20 %. In Anbetracht der mit dem Alter abnehmenden Lebenserwartung, insbesondere bei langjährigen aktiven Rauchern, nimmt das Risiko der Überdiagnose jenseits des 75. Lebensjahres deutlich zu. Vor dem 50. Lebensjahr ist die Lungenkrebsinzidenz hingegen zu gering, als dass Risiken und auch Kosten ein angemessener Nutzen gegenübersteht. Konzise Kriterien, wie in den NLST- und NELSON-Studien angewendet, stellen einen grundlegenden Anhalt für geeignete Einschlusskriterien dar. Ihnen stehen Modelle zur Risikoprädiktion gegenüber, die neben dem Geschlecht das Alter und die Rauchanamnese als kontinuierliche Variablen verwenden. Verglichen mit konzisen Kriterien konnten mithilfe dieser Modelle 10–20 % mehr Patienten mit Lungenkrebs bei gleicher Gesamtzahl gescreenter Personen identifiziert werden. Zugleich können sie zu einer größeren Sicherheit beitragen, dass die Screening-Teilnehmer ein ausreichend hohes Lungenkrebsrisiko haben, sodass die Risiken aufgrund von Strahlung und den Folgen falsch positiver Screening-Ergebnisse aufgewogen werden.

Kernaussagen: 

• Durch LDCT-Screening kann die Lungenkrebssterblichkeit signifikant gesenkt werden.

• Um Überdiagnose zu beschränken, sollte nach dem 75. Lebensjahr kein LDCT-Screening mehr erfolgen.

• Durch den Einsatz von Risikomodellen kann der Nettonutzen des Lungenkrebs-Screenings verbessert werden.

Key words

lung neoplasms - radiation risks - overdiagnosis - false-positive findings - eligibility criteria - screening

Publikationsverlauf

Eingereicht: 22. Juli 2020

Angenommen: 29. September 2020

Artikel online veröffentlicht:
19. November 2020

• References

• 1 Barnes B, Kraywinkel K, Nowossadeck E. et al Bericht zum Krebsgeschehen in Deutschland 2016. In: Robert Koch-Institut Gesundheitsberichterstattung-Hefte 2016 DOI: 10.17886/rkipubl-2016-014
• 2 Goldstraw P, Chansky K, Crowley J. et al The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer. Journal of Thoracic Oncology: official publication of the International Association for the Study of Lung Cancer 2016; 11: 39-51 DOI: 10.1016/j.jtho.2015.09.009.
• 3 Aberle DR, Berg CD, Black WC. et al The National Lung Screening Trial: overview and study design. Radiology 2011; 258: 243-253 DOI: 10.1148/radiol.10091808.
• 4 Aberle DR, Adams AM, Berg CD. et al Reduced lung-cancer mortality with low-dose computed tomographic screening. The New England Journal of Medicine 2011; 365: 395-409 DOI: 10.1056/NEJMoa1102873.
• 5 de Koning HJ, van der Aalst CM, de Jong PA. et al Reduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial. The New England Journal of Medicine 2020; 382: 503-513 DOI: 10.1056/NEJMoa1911793.
• 6 Paci E, Puliti D, Lopes Pegna A. et al Mortality, survival and incidence rates in the ITALUNG randomised lung cancer screening trial. Thorax 2017; 72: 825-831 DOI: 10.1136/thoraxjnl-2016-209825.
• 7 Infante M, Cavuto S, Lutman FR. et al Long-Term Follow-up Results of the DANTE Trial, a Randomized Study of Lung Cancer Screening with Spiral Computed Tomography. American Journal of Respiratory and Critical Care Medicine 2015; 191: 1166-1175 DOI: 10.1164/rccm.201408-1475OC.
• 8 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. European Journal of Cancer Prevention: the official Journal of the European Cancer Prevention Organisation 2012; 21: 308-315 DOI: 10.1097/CEJ.0b013e328351e1b6.
• 9 Pastorino U, Silva M, Sestini S. et al Prolonged lung cancer screening reduced 10-year mortality in the MILD trial: new confirmation of lung cancer screening efficacy. Annals of Oncology: Official Journal of the European Society for Medical Oncology/ESMO 2019; 30: 1672 DOI: 10.1093/annonc/mdz169.
• 10 Becker N, Motsch E, Trotter A. et al Lung cancer mortality reduction by LDCT screening – results from the randomised German LUSI trial. International Journal of Cancer Journal International du Cancer 2019; DOI: 10.1002/ijc.32486.
• 11 Wille MM, Dirksen A, Ashraf H. et al Results of the Randomized Danish Lung Cancer Screening Trial with Focus on High-Risk Profiling. American Journal of Respiratory and Critical Care Medicine 2016; 193: 542-551 DOI: 10.1164/rccm.201505-1040OC.
• 12 Pinsky PF, Church TR, Izmirlian G. et al The National Lung Screening Trial: results stratified by demographics, smoking history, and lung cancer histology. Cancer 2013; 119: 3976-3983 DOI: 10.1002/cncr.28326.
• 13 Ten Haaf K, van Rosmalen J, de Koning HJ. Lung cancer detectability by test, histology, stage, and gender: estimates from the NLST and the PLCO trials. Cancer Epidemiology, Biomarkers & Prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 2015; 24: 154-161 DOI: 10.1158/1055-9965.Epi-14-0745.
• 14 Gonzalez Maldonado S, Motsch E, Trotter A. et al Overdiagnosis in lung cancer screening – estimates from the German Lung Cancer Screening Intervention Trial. International Journal of Cancer Journal International du Cancer 2020 [in press]
• 15 National Research Council. Health Risks from Exposure to Low Levels of Ionizing Radiation: BEIR VII Phase 2. Washington, DC: The National Academies Press; 2006 DOI: 10.17226/11340
• 16 Brenner DJ. Radiation risks potentially associated with low-dose CT screening of adult smokers for lung cancer. Radiology 2004; 231: 440-445 DOI: 10.1148/radiol.2312030880.
• 17 Berrington de Gonzalez 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. Journal of Medical Screening 2008; 15: 153-158 DOI: 10.1258/jms.2008.008052.
• 18 Bach PB, Mirkin JN, Oliver TK. et al Benefits and harms of CT screening for lung cancer: a systematic review. JAMA: The Journal of the American Medical Association 2012; 307: 2418-2429 DOI: 10.1001/jama.2012.5521.
• 19 Rampinelli C, De Marco P, Origgi D. et al Exposure to low dose computed tomography for lung cancer screening and risk of cancer: secondary analysis of trial data and risk-benefit analysis. BMJ 2017; 356: j347 DOI: 10.1136/bmj.j347.
• 20 Personal communication Dr Elke Nekolla, Bundesamt für Strahlenschutz. In 2020
• 21 Huber A, Landau J, Ebner L. et al Performance of ultralow-dose CT with iterative reconstruction in lung cancer screening: limiting radiation exposure to the equivalent of conventional chest X-ray imaging. European Radiology 2016; 26: 3643-3652 DOI: 10.1007/s00330-015-4192-3.
• 22 Hassani C, Ronco A, Prosper AE. et al Forward-Projected Model-Based Iterative Reconstruction in Screening Low-Dose Chest CT: Comparison With Adaptive Iterative Dose Reduction 3D. Am J Roentgenol American Journal of Roentgenology 2018; 211: 548-556 DOI: 10.2214/ajr.17.19245.
• 23 Lung RADs. Lung CT Screening Reporting & Data System. In: American College of Radiology 2019
• 24 Pinsky PF, Gierada DS, Black W. et al Performance of Lung-RADS in the National Lung Screening Trial: a retrospective assessment. Annals of Internal Medicine 2015; 162: 485-491 DOI: 10.7326/m14-2086.
• 25 Horeweg N, van der Aalst CM, Vliegenthart R. et al Volumetric computed tomography screening for lung cancer: three rounds of the NELSON trial. The European Respiratory Journal 2013; 42: 1659-1667 DOI: 10.1183/09031936.00197712.
• 26 van Klaveren RJ, Oudkerk M, Prokop M. et al Management of lung nodules detected by volume CT scanning. The New England Journal of Medicine 2009; 361: 2221-2229 DOI: 10.1056/NEJMoa0906085.
• 27 Pinsky PF, Bellinger CR, Miller Jr DP. False-positive screens and lung cancer risk in the National Lung Screening Trial: Implications for shared decision-making. Journal of Medical Screening 2018; 25: 110-112 DOI: 10.1177/0969141317727771.
• 28 Lung RADS. Lung CT Screening Reporting & Data System (Lung-RADS). In American College of Radiology Committee on Lung-RADS®. Lung-RADS Assessment Categories version 1.1. Available at HYPERLINK “https://www.acr.org/-/media/ACR/Files/RADS/Lung-RADS/LungRADSAssessmentCategoriesv1-1.pdf” https://www.acr.org/-/media/ACR/Files/RADS/Lung-RADS/LungRADSAssessmentCategoriesv1-1.pdf. Accessed on January 1, 2020
• 29 Tammemagi MC, Katki HA, Hocking WG. et al Selection criteria for lung-cancer screening. The New England Journal of Medicine 2013; 368: 728-736 DOI: 10.1056/NEJMoa1211776.
• 30 Byrne MM, Weissfeld J, Roberts MS. Anxiety, fear of cancer, and perceived risk of cancer following lung cancer screening. Medical decision making: An international Journal of the Society for Medical Decision Making 2008; 28: 917-925 DOI: 10.1177/0272989x08322013.
• 31 Brain K, Lifford KJ, Carter B. et al Long-term psychosocial outcomes of low-dose CT screening: results of the UK Lung Cancer Screening randomised controlled trial. Thorax 2016; 71: 996-1005 DOI: 10.1136/thoraxjnl-2016-208283.
• 32 Rasmussen JF, Siersma V, Malmqvist J. et al Psychosocial consequences of false positives in the Danish Lung Cancer CT Screening Trial: a nested matched cohort study. BMJ open 2020; 10: e034682 DOI: 10.1136/bmjopen-2019-034682.
• 33 Davies L, Petitti DB, Martin L. et al Defining, Estimating, and Communicating Overdiagnosis in Cancer Screening. Annals of Internal Medicine 2018; 169: 36-43 DOI: 10.7326/m18-0694.
• 34 Patz Jr EF, Pinsky P, Gatsonis C. et al Overdiagnosis in low-dose computed tomography screening for lung cancer. JAMA Internal Medicine 2014; 174: 269-274 DOI: 10.1001/jamainternmed.2013.12738.
• 35 Heleno B, Siersma V, Brodersen J. Estimation of Overdiagnosis of Lung Cancer in Low-Dose Computed Tomography Screening: A Secondary Analysis of the Danish Lung Cancer Screening Trial. JAMA Internal Medicine 2018; 178: 1420-1422 DOI: 10.1001/jamainternmed.2018.3056.
• 36 [Anonym]. Lung Cancer Incidence and Mortality with Extended Follow-up in the National Lung Screening Trial. Journal of thoracic oncology: official publication of the International Association for the Study of Lung Cancer 2019; 14: 1732-1742 DOI: 10.1016/j.jtho.2019.05.044.
• 37 de Koning HJ, Meza R, Plevritis SK. et al Benefits and harms of computed tomography lung cancer screening strategies: a comparative modeling study for the U.S. Preventive Services Task Force. Annals of Internal Medicine 2014; 160: 311-320 DOI: 10.7326/m13-2316.
• 38 Ten Haaf K, de Koning HJ. Overdiagnosis in lung cancer screening: why modelling is essential. Journal of Epidemiology and Community Health 2015; 69: 1035-1039 DOI: 10.1136/jech-2014-204079.
• 39 Treskova M, Aumann I, Golpon H. et al Trade-off between benefits, harms and economic efficiency of low-dose CT lung cancer screening: a microsimulation analysis of nodule management strategies in a population-based setting. BMC Medicine 2017; 15: 162 DOI: 10.1186/s12916-017-0924-3.
• 40 Knoke JD, Shanks TG, Vaughn JW. et al Lung cancer mortality is related to age in addition to duration and intensity of cigarette smoking: an analysis of CPS-I data. Cancer epidemiology, biomarkers & prevention: A ;publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 2004; 13: 949-957
• 41 Lubin JH, Caporaso N, Wichmann HE. et al Cigarette smoking and lung cancer: modeling effect modification of total exposure and intensity. Epidemiology 2007; 18: 639-648 DOI: 10.1097/EDE.0b013e31812717fe.
• 42 Vlaanderen J, Portengen L, Schüz J. et al Effect modification of the association of cumulative exposure and cancer risk by intensity of exposure and time since exposure cessation: a flexible method applied to cigarette smoking and lung cancer in the SYNERGY Study. American Journal of Epidemiology 2014; 179: 290-298 DOI: 10.1093/aje/kwt273.
• 43 Thomas DC. Invited commentary: is it time to retire the “pack-years” variable?. Maybe not! American Journal of Epidemiology 2014; 179: 299-302 DOI: 10.1093/aje/kwt274.
• 44 Peto R, Darby S, Deo H. et al Smoking, smoking cessation, and lung cancer in the UK since 1950: combination of national statistics with two case-control studies. BMJ 2000; 321: 323-329 DOI: 10.1136/bmj.321.7257.323.
• 45 Moyer VA. Force USPST. Screening for lung cancer: U.S. Preventive Services Task Force recommendation statement. Annals of Internal Medicine 2014; 160: 330-338 DOI: 10.7326/m13-2771.
• 46 Field JK, Aberle DR, Altorki N. et al The International Association Study Lung Cancer (IASLC) Strategic Screening Advisory Committee (SSAC) response to the USPSTF recommendations. Journal of Thoracic Oncology: Official publication of the International Association for the Study of Lung Cancer 2014; 9: 141-143 DOI: 10.1097/jto.0000000000000060.
• 47 Kauczor HU, Bonomo L, Gaga M. et al ESR/ERS white paper on lung cancer screening. European Radiology 2015; 25: 2519-2531 DOI: 10.1007/s00330-015-3697-0.
• 48 Wood DE, Kazerooni EA, Baum SL. et al Lung Cancer Screening, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology. Journal of the National Comprehensive Cancer Network: JNCCN 2018; 16: 412-441 DOI: 10.6004/jnccn.2018.0020.
• 49 Canadian Task Force on Preventive Health Care. Recommendations on screening for lung cancer. CMAJ: Canadian Medical Association Journal = Journal de l’Association Medicale Canadienne 2016; 188: 425-432 DOI: 10.1503/cmaj.151421.
• 50 Kauczor HU, Baird AM, Blum TG. et al ESR/ERS statement paper on lung cancer screening. European Radiology 2020; DOI: 10.1007/s00330-020-06727-7.
• 51 AWMF. S3-Leitlinie Prävention, Diagnostik, Therapie und Nachsorge des Lungenkarzinoms. In, AWMF-Registernummer: 020/007OL 2018
• 52 Hüsing A, Kaaks R. Risk prediction models versus simplified selection criteria to determine eligibility for lung cancer screening – an analysis of German federal-wide survey and incidence data. Europen Journal of Epidemiology 2020 [in press]
• 53 Han SS, Ten Haaf K, Hazelton WD. et al The impact of overdiagnosis on the selection of efficient lung cancer screening strategies. International Journal of Cancer Journal international du cancer 2017; 140: 2436-2443 DOI: 10.1002/ijc.30602.
• 54 Ten Haaf K, Bastani M, Cao P. et al A comparative modeling analysis of risk-based lung cancer screening strategies. Journal of the National Cancer Institute 2019; DOI: 10.1093/jnci/djz164.
• 55 Ten Haaf K, Tammemagi MC, Bondy SJ. et al Performance and Cost-Effectiveness of Computed Tomography Lung Cancer Screening Scenarios in a Population-Based Setting: A Microsimulation Modeling Analysis in Ontario, Canada. PLoS Medicine 2017; 14: e1002225 DOI: 10.1371/journal.pmed.1002225.
• 56 Tomonaga Y, Ten Haaf K, Frauenfelder T. et al Cost-effectiveness of low-dose CT screening for lung cancer in a European country with high prevalence of smoking-A modelling study. Lung Cancer (Amsterdam, Netherlands) 2018; 121: 61-69 DOI: 10.1016/j.lungcan.2018.05.008.
• 57 Mascalchi M, Sali L. Lung cancer screening with low dose CT and radiation harm-from prediction models to cancer incidence data. Annals of Translational Medicine 2017; 5: 360 DOI: 10.21037/atm.2017.06.41.
• 58 Bach PB, Gould MK. When the average applies to no one: personalized decision making about potential benefits of lung cancer screening. Annals of Internal Medicine 2012; 157: 571-573 DOI: 10.7326/0003-4819-157-8-201210160-00524.
• 59 Katki HA, Kovalchik SA, Berg CD. et al Development and Validation of Risk Models to Select Ever-Smokers for CT Lung Cancer Screening. JAMA: The Journal of the American Medical Association 2016; 315: 2300-2311 DOI: 10.1001/jama.2016.6255.
• 60 Katki HA, Kovalchik SA, Petito LC. et al Implications of Nine Risk Prediction Models for Selecting Ever-Smokers for Computed Tomography Lung Cancer Screening. Annals of Internal Medicine 2018; 169: 10-19 DOI: 10.7326/m17-2701.
• 61 Ten Haaf K, Jeon J, Tammemagi MC. et al Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study. PLoS Medicine 2017; 14: e1002277 DOI: 10.1371/journal.pmed.1002277.
• 62 Li K, Husing A, Sookthai D. et al Selecting High-Risk Individuals for Lung Cancer Screening: A Prospective Evaluation of Existing Risk Models and Eligibility Criteria in the German EPIC Cohort. Cancer Prev Res (Phila) 2015; 8: 777-785 DOI: 10.1158/1940-6207.capr-14-0424.