Lung Cancer Screening: Evidence, Risks, and Opportunities for Implementation

Abstract

Background Lung cancer is the most common cause of cancer death worldwide. Several trials with different screening approaches have recognized the role of lung cancer screening with low-dose CT for reducing lung cancer mortality. The efficacy of lung cancer screening depends on many factors and implementation is still pending in most European countries.

Methods This review aims to portray current evidence on lung cancer screening with a focus on the potential for opportunities for implementation strategies. Pillars of lung cancer screening practice will be discussed according to the most updated literature (PubMed search until November 16, 2020).

Results and Conclusion The NELSON trial showed reduction of lung cancer mortality, thus confirming previous results of independent European studies, notably by volume of lung nodules. Heterogeneity in patient recruitment could influence screening efficacy, hence the importance of risk models and community-based screening. Recruitment strategies develop and adapt continuously to address the specific needs of the heterogeneous population of potential participants, the most updated evidence comes from the UK. The future of lung cancer screening is a tailored approach with personalized continuous stratification of risk, aimed at reducing costs and risks.

Key Points: 

• Secondary prevention of lung cancer by low-dose computed tomography showed a reduction of lung cancer mortality.

• Semi-automated volume measurement and use of volume doubling time should be the reference method for optimization of risks, namely controlling measurement variability and the false-positive rate.

• A conservative approach with surveillance of subsolid nodules can be one of the strategies to reduce the risk of overdiagnosis and overtreatment.

• The goal of a tailored approach with personalized risk stratification aims to reduce costs and risks. A longer interval between rounds is one option for participants at lower risk.

Citation Format

• Tringali G, Milanese G, Ledda RE et al. Lung Cancer Screening: Evidence, Risks, and Opportunities for Implementation. Fortschr Röntgenstr 2021; 193: 1153 – 1161

Zusammenfassung

Hintergrund Lungenkrebs ist weltweit die häufigste zum Tode führende Krebserkrankung. Mehrere Studien mit unterschiedlichen Screening-Ansätzen haben die Rolle des Screenings mit Niedrigdosis-CT zur Reduzierung der Lungenkrebs-Mortalität erkannt. Die Effektivität des Lungenkrebs-Screenings hängt von vielen Faktoren ab und dessen Implementierung steht in den meisten europäischen Ländern noch aus.

Methoden Ziel dieser Übersicht ist die Darstellung der aktuellen Evidenz des Lungenkrebs-Screenings mit Schwerpunkt auf den möglichen Chancen für Implementierungsstrategien. Die Säulen der Lungenkrebs-Vorsorge werden anhand der aktuellsten Literatur diskutiert (PubMed-Suche bis 16. November 2020).

Ergebnisse und Schlussfolgerungen Die NELSON-Studie zeigte eine Reduktion der Lungenkrebs-Mortalität und bestätigte damit frühere Ergebnisse unabhängiger europäischer Studien, insbesondere hinsichtlich des Volumens der LungenRundherde. Die Heterogenität bei der Patientenrekrutierung könnte die Effektivität des Screenings beeinflussen, daher sind Risikomodelle und Community-basiertes Screening von Bedeutung. Die Rekrutierungsstrategien werden kontinuierlich weiterentwickelt und angepasst, um den spezifischen Bedürfnissen der heterogenen Population potenzieller Teilnehmer gerecht zu werden. Die aktuellsten Erkenntnisse hierzu stammen aus Großbritannien. Das Lungenkrebs-Screening der Zukunft besteht aus einem maßgeschneiderten Ansatz mit personalisierter, kontinuierlicher Risikostratifizierung, das darauf abzielt, Kosten und Risiken zu reduzieren.

Kernaussagen: 

• Die Sekundärprävention von Lungenkrebs durch Niedrigdosis-Computertomografie zeigte eine Reduktion der Lungenkrebs-Mortalität.

• Die semi-automatische Volumenmessung sowie der Einsatz der Volumenverdopplungszeit sollten die Referenzmethode zur Risikooptimierung sein, nämlich die Kontrolle der Messvariabilität und der falsch-positiven Rate.

• Ein konservativer Ansatz mit Überwachung von subsoliden Rundherden kann eine der Strategien sein, um das Risiko einer Überdiagnose und Überbehandlung zu reduzieren.

• Ziel eines maßgeschneiderten Ansatzes mit personalisierter Risikostratifizierung ist die Reduzierung von Kosten und Risiken. Ein längeres Intervall zwischen den Visiten ist eine Option für Teilnehmer mit geringerem Risiko.

Key words

pulmonary nodule - lung cancer - guidelines - lungRADS - screening

Publikationsverlauf

Eingereicht: 25. November 2020

Angenommen: 19. Januar 2021

Artikel online veröffentlicht:
26. März 2021

• References

• 1 ECIS – European Cancer Information System From From https://ecis.jrc.ec.europa.eu, accessed on 23/09/2020 © European Union 2020.
• 2 WHO/Europe |. European tobacco use: Trends report 2019. 2019 https://www.euro.who.int/en/health-topics/disease-prevention/tobacco/publications/2019/european-tobacco-use-trends-report-2019-2019 (accessed November 6, 2020)
• 3 Field JK, Duffy SW, Baldwin DR. et al The UK lung cancer screening trial: A pilot randomised controlled trial of low-dose computed tomography screening for the early detection of lung cancer. Health Technol Assess (Rockv) 2016; 20 DOI: 10.3310/hta20400.
• 4 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. Am J Respir Crit Care Med 2015; 191: 1166-1175 . https://doi.org/10.1164/rccm.201408-1475OC
• 5 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. Ann Oncol 2019; 30: 1162-1169 . https://doi.org/10.1093/annonc/mdz117
• 6 Wille MMW, Dirksen A, Ashraf H. et al Results of the Randomized Danish Lung Cancer Screening Trial with Focus on High-Risk Profiling. Am J Respir Crit Care Med 2016; 193: 542-551 . https://doi.org/10.1164/rccm.201505-1040OC
• 7 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 . https://doi.org/10.1136/thoraxjnl-2016-209825
• 8 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 . https://doi.org/10.1056/NEJMoa1102873
• 9 De Koning HJ, Van Der Aalst CM, De Jong PA. et al Reduced lung-cancer mortality with volume CT screening in a randomized trial. N Engl J Med 2020; 382: 503-513 . https://doi.org/10.1056/NEJMoa1911793
• 10 Becker N, Motsch E, Trotter A. et al Lung cancer mortality reduction by LDCT screening – Results from the randomized German LUSI trial. Int J Cancer 2020; 146: 1503-1513 . https://doi.org/10.1002/ijc.32486
• 11 Moyer VA. Screening for lung cancer: U. S. preventive services task force recommendation statement. Ann Intern Med 2014; 160: 330-338 . https://doi.org/10.7326/m13-2771
• 12 Scholten ET, Horeweg N, de Koning HJ. et al Computed tomographic characteristics of interval and post screen carcinomas in lung cancer screening. Eur Radiol 2015; 25: 81-88 . https://doi.org/10.1007/s00330-014-3394-4
• 13 Silva M, Prokop M, Jacobs C. et al Long-Term Active Surveillance of Screening Detected Subsolid Nodules is a Safe Strategy to Reduce Overtreatment. J Thorac Oncol 2018; 13: 1454-1463 . https://doi.org/10.1016/j.jtho.2018.06.013
• 14 National Lung Screening Trial Research Team T. Lung Cancer Incidence and Mortality with Extended Follow-up in the National Lung Screening Trial The National Lung Screening Trial Research Team* 2019. https://doi.org/10.1016/j.jtho.2019.05.044
• 15 Yousaf-Khan U, Van Der Aalst C, De Jong PA. et al Final screening round of the NELSON lung cancer screening trial: The effect of a 2.5-year screening interval. Thorax 2017; 72: 48-56 . https://doi.org/10.1136/thoraxjnl-2016-208655
• 16 Pastorino U, Sverzellati N, Sestini S. et al Ten-year results of the Multicentric Italian Lung Detection trial demonstrate the safety and efficacy of biennial lung cancer screening. Eur J Cancer 2019; 118: 142-148 . https://doi.org/10.1016/j.ejca.2019.06.009
• 17 Kauczor HU, Baird AM, Blum TG. et al ESR/ERS statement paper on lung cancer screening. Eur Radiol 2020; 30: 3277-3294 . https://doi.org/10.1007/s00330-020-06727-7
• 18 Criss SD, Cao P, Bastani M. et al Cost-effectiveness analysis of lung cancer screening in the United States. Ann Intern Med 2019; 171: 796-804 . https://doi.org/10.7326/M19-0322
• 19 Maciosek MV, Coffield AB, Edwards NM. et al Priorities Among Effective Clinical Preventive Services. Results of a Systematic Review and Analysis. Am J Prev Med 2006; 31: 52-61 . https://doi.org/10.1016/j.amepre.2006.03.012
• 20 Pastorino U, Boffi R, Marchiano A. et al Stopping smoking reduces mortality in low-dose computed tomography screening participants. J Thorac Oncol 2016; 11: 693-699 . https://doi.org/10.1016/j.jtho.2016.02.011
• 21 Sverzellati N, Cademartiri F, Bravi F. et al Relationship and prognostic value of modified coronary artery calcium score, FEV 1, and emphysema in lung cancer screening population: The MILD trial. Radiology 2012; 262: 460-467 . https://doi.org/10.1148/radiol.11110364
• 22 Chiles C, Duan F, Gladish GW. et al Association of coronary artery calcification and mortality in the national lung screening trial: A comparison of three scoring methods. Radiology 2015; 276: 82-90 . https://doi.org/10.1148/radiol.15142062
• 23 Tammemägi MC, Katki HA, Hocking WG. et al Selection criteria for lung-cancer screening. N Engl J Med 2013; 368: 728-736 . https://doi.org/10.1056/NEJMoa1211776
• 24 Tammemagi MC, Schmidt H, Martel S. et al Participant selection for lung cancer screening by risk modelling (the Pan-Canadian Early Detection of Lung Cancer [PanCan] study): a single-arm, prospective study. Lancet Oncol 2017; 18: 1523-1531 . https://doi.org/10.1016/S1470-2045(17)30597-1
• 25 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. Eur J Epidemiol 2020; 35: 899-912 . https://doi.org/10.1007/s10654-020-00657-w
• 26 Field JK, Vulkan D, Davies MPA. et al Liverpool lung project lung cancer risk stratification model: Calibration and prospective validation. Thorax 2020; DOI: 10.1136/thoraxjnl-2020-215158.
• 27 McRonald FE, Yadegarfar G, Baldwin DR. et al The UK Lung Screen (UKLS): Demographic profile of first 88897 approaches provides recommendations for population screening. Cancer Prev Res 2014; 7: 362-371 . https://doi.org/10.1158/1940-6207.CAPR-13-0206
• 28 Patricia Rivera M, Aldrich MC, Henderson LM. et al Addressing disparities in lung cancer screening eligibility and healthcare access: An official American thoracic society statement. Am J Respir Crit Care Med 2020; 202: E95-E112 . https://doi.org/10.1164/rccm.202008-3053ST
• 29 Fedewa SA, Kazerooni MSEA, Studts JL. et al State Variation in Low-Dose CT Scanning for Lung Cancer Screening in the United States. J Natl Cancer Inst 2020; DOI: 10.1093/jnci/djaa170/5970481.
• 30 Guo L, Chen Q, Shen Y. et al Evaluation of a Low-Dose Computed Tomography Lung Cancer Screening Program in Henan, China. 2020 3. 1-11 https://doi.org/10.1001/jamanetworkopen.2020.19039
• 31 Crosbie PA, Balata H, Evison M. et al Implementing lung cancer screening: Baseline results from a community-based “Lung Health Check” pilot in deprived areas of Manchester. Thorax 2019; 74: 405-409 . https://doi.org/10.1136/thoraxjnl-2017-211377
• 32 Bartlett EC, Kemp SV, Ridge CA. et al Baseline Results of the West London lung cancer screening pilot study – Impact of mobile scanners and dual risk model utilisation. Lung Cancer 2020; 148: 12-19 . https://doi.org/10.1016/j.lungcan.2020.07.027
• 33 Pastorino U, Sestini S. Plasma microRNA Profiling as first line screening test for lung cancer detection: a prospective study (bioMILD) 2014.
• 34 Gaga M, Chorostowska-Wynimko J, Horváth I. et al Validation of Lung EpiCheck ®, a novel methylation-based blood assay, for the detection of lung cancer in European and Chinese high-risk individuals. Eur Respir J 2021; 57: 2002682 DOI: 10.1183/13993003.02682-2020. . PMID: 33122336;
• 35 Sullivan FM, Mair FS, Anderson W. et al Earlier diagnosis of lung cancer in a randomised trial of an autoantibody blood test followed by imaging. Eur Respir J 2020; DOI: 10.1183/13993003.00670-2020.
• 36 Devaraj A. Missed cancers in lung cancer screening – more than meets the eye. Eur Radiol 2015; 25: 89-91 . https://doi.org/10.1007/s00330-014-3395-3
• 37 Huang P, Park S, Yan R. et al Added value of computer-aided CT image features for early lung cancer diagnosis with small pulmonary nodules: A matched case-control study. Radiology 2018; 286: 286-295 . https://doi.org/10.1148/radiol.2017162725
• 38 Matsumoto S, Ohno Y, Aoki T. et al Computer-aided detection of lung nodules on multidetector CT in concurrent-reader and second-reader modes: A comparative study. Eur J Radiol 2013; 82: 1332-1337 . https://doi.org/10.1016/j.ejrad.2013.02.005
• 39 Christe A, Leidolt L, Huber A. et al Lung cancer screening with CT: Evaluation of radiologists anddifferent computer assisted detection software (CAD) as first andsecond readers for lung nodule detection at different dose levels. Eur J Radiol 2013; 82 DOI: 10.1016/j.ejrad.2013.08.026.
• 40 Silva M, Schaefer-Prokop CM, Jacobs C. et al Detection of Subsolid Nodules in Lung Cancer Screening Complementary Sensitivity of Visual Reading and Computer-Aided Diagnosis. Investig Radiol 2004; DOI: 10.1097/RLI.0000000000000464.
• 41 Devaraj A, Van Ginneken B, Nair A. et al Use of volumetry for Lung nodule management: Theory and Practice 1 STATE OF THE ART: Volumetry for Lung Nodule Management Devaraj et al. Radiol n Radiol 2017; 284 DOI: 10.1148/radiol.2017151022.
• 42 Hwang EJ, Goo JM, Kim HY. et al Variability in interpretation of low-dose chest CT using computerized assessment in a nationwide lung cancer screening program: comparison of prospective reading at individual institutions and retrospective central reading. Eur Radiol 2020; DOI: 10.1007/s00330-020-07424-1.
• 43 British Thoracic Society Pulmonary Nodule Guideline Development Group. Pulmonary Nodules | British Thoracic Society | Better lung health for all. Thorax 2015; 70: 1188 DOI: 10.1136/thoraxjnl-2015-207168corr1.
• 44 Oudkerk M, Liu SY, Heuvelmans MA. et al Lung cancer LDCT screening and mortality reduction – evidence, pitfalls and future perspectives. Nat Rev Clin Oncol 2020; DOI: 10.1038/s41571-020-00432-6.
• 45 Pinsky PF, Gierada DS, Black W. et al Performance of lung-RADS in the national lung screening trial: A retrospective assessment. Ann Intern Med 2015; 162: 485-491 . https://doi.org/10.7326/M14-2086
• 46 Gierada DS, Rydzak CE, Zei M. et al Improved Interobserver Agreement on Lung-RADS Classification of Solid Nodules Using Semiautomated CT Volumetry. Radiology 2020; DOI: 10.1148/radiol.2020200302.
• 47 Silva M, Milanese G, Sestini S. et al Lung cancer screening by nodule volume in Lung-RADS v1.1: negative baseline CT yields potential for increased screening interval. Eur Radiol 2020; DOI: 10.1007/s00330-020-07275-w.
• 48 Zhu Y, Yip R, You N. et al Management of Nodules Attached to the Costal Pleura at Low-Dose CT Screening for Lung Cancer. Radiology 2020; DOI: 10.1148/radiol.2020202388.
• 49 Hammer MM, Byrne SC, Kong CY. Factors Influencing the False Positive Rate in CT Lung Cancer Screening. Acad Radiol 2020; 1-5 DOI: 10.1016/j.acra.2020.07.040.
• 50 Welch HG, Black WC. Overdiagnosis in cancer. J Natl Cancer Inst 2010; 102: 605-613 . https://doi.org/10.1093/jnci/djq099
• 51 González Maldonado S, Motsch E, Trotter A. et al Overdiagnosis in lung cancer screening: Estimates from the German Lung Cancer Screening Intervention Trial. Int J Cancer 2020; DOI: 10.1002/ijc.33295.
• 52 Horst C, Dickson JL, Tisi S. et al Delivering low-dose CT screening for lung cancer: A pragmatic approach. Thorax 2020; 75: 831-832 . https://doi.org/10.1136/thoraxjnl-2020-215131
• 53 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: 347 . https://doi.org/10.1136/bmj.j347
• 54 NHS England – National Cancer Programme. Targeted Screening for Lung Cancer with Low Radiation Dose Computed Tomography 2019; 1–30.
• 55 4-IN THE LUNG RUN: towards INdividually tailored INvitations, screening INtervals, and INtegrated co-morbidity reducing strategies in lung cancer screening | 4-IN THE LUNG RUN Project | H2020 | CORDIS | European Commission n.d. https://cordis.europa.eu/project/id/848294 (accessed November 6, 2020)
• 56 Milanese G, Silva M, Frauenfelder T. et al Comparison of ultra-low dose chest CT scanning protocols for the detection of pulmonary nodules: a phantom study. Tumori 2019; 105: 394-403 . https://doi.org/10.1177/0300891619847271
• 57 Ye K, Zhu Q, Li M. et al A feasibility study of pulmonary nodule detection by ultralow-dose CT with adaptive statistical iterative reconstruction-V technique. Eur J Radiol 2019; 119 DOI: 10.1016/j.ejrad.2019.108652.
• 58 Ciompi F, Chung K, Van Riel SJ. et al Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Sci Rep 2017; 7 DOI: 10.1038/srep46479.
• 59 Hawkins S, Wang H, Liu Y. et al Predicting Malignant Nodules from Screening CT Scans. J Thorac Oncol 2016; 11: 2120-2128 . https://doi.org/10.1016/j.jtho.2016.07.002
• 60 Xu Y, Lu L, Lin-ning E. et al Application of Radiomics in Predicting the Malignancy of Pulmonary Nodules in Different Sizes. Am J Roentgenol 2019; 213: 1213-1220 . https://doi.org/10.2214/AJR.19.21490
• 61 Ardila D, Kiraly AP, Bharadwaj S. et al End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med 2019; 25: 954-961 . https://doi.org/10.1038/s41591-019-0447-x
• 62 Baldwin DR, Gustafson J, Pickup L. et al External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules. Thorax 2020; 75: 306-312 . https://doi.org/10.1136/thoraxjnl-2019-214104