University of Groningen Pulmonary Nodules: 2D versus 3D evaluation in lung cancer screening Han, Daiwei

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Pulmonary Nodules: 2D versus 3D evaluation in lung cancer screening

Han, Daiwei

DOI:

10.33612/diss.172563513

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Publication date: 2021

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Han, D. (2021). Pulmonary Nodules: 2D versus 3D evaluation in lung cancer screening. University of Groningen. https://doi.org/10.33612/diss.172563513

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559233-l-bw-Han 559233-l-bw-Han 559233-l-bw-Han 559233-l-bw-Han Processed on: 20-5-2021 Processed on: 20-5-2021 Processed on: 20-5-2021

Processed on: 20-5-2021 PDF page: 17PDF page: 17PDF page: 17PDF page: 17 Daiwei Han Marjolein A. Heuvelmans Rozemarijn Vliegenthart Mieneke Rook Monique D. Dorrius Matthijs Oudkerk

Published, Journal of Thoracic Imaging 2019

An Update on the European

Lung Cancer Screening Trials and

Comparison of Lung Cancer

Screening Recommendations in Europe

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Processed on: 20-5-2021 PDF page: 18PDF page: 18PDF page: 18PDF page: 18

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ABSTRACT

While lung cancer screening has been implemented in the United States, it is still under consideration in Europe. So far, lung cancer screening trials in Europe were not able to replicate the results of the National Lung Screening Trial, but they do show a stage shift in the lung cancers that were detected. Whilst eagerly awaiting the final result of the only lung cancer screening trial with sufficient statistical power, the NELSON trial, a number of European countries and medical societies have published recommendations for lung cancer screening using computed tomography. However, there is still a debate regarding the design of future lung cancer screening programs in Europe. This review summarizes the latest evidence of European lung cancer screening trials, and gives an overview of the essence of recommendations from the different European medical societies and countries.

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INTRODUCTION

Lung cancer is currently the leading cause of cancer-related death worldwide (1). In Europe, it accounts for 20% of the total cancer death toll (2). Despite advancements in treatment of lung cancer over the past decades, the overall survival of lung cancer remains poor, with overall survival mainly depending on stage at lung cancer diagnosis. The U.S. National Lung Screening Trial (NLST) has demonstrated a 20% reduction of lung cancer mortality by early lung cancer detection using annual low-dose computed tomography (LDCT) of the chest, compared to annual chest radiography (3). This was followed by recommendations for implementation of annual low-dose chest CT screening by the US Preventive Service Task Force (4), and organizations such as the International Association for the Study of Lung Cancer (IASLC) (5), and the National Comprehensive Cancer Network (NCCN) (6). As a consequence, voluntary lung cancer screening has now been implemented in the United States for high-risk population.

In Europe, multiple lung cancer screening studies have been completed or are still on-going, including the Multi-centric Italian lung detection (MILD) (7), the Italian lung study (ITALUNG) (8), Detection and screening of early lung cancer with novel imaging technology (DANTE) (9), Danish lung cancer screening trial (DLCST) (10,11), the Dutch-Belgian lung cancer screening trial (NELSON) (12,13), the German lung cancer screening intervention trial (LUSI) (14,15), and the UK lung cancer screening (UKLS) (16,17). None of the studies that published their final mortality results so far were able to replicate the mortality benefit as described by the NLST (3). This might have been caused by the fact that these studies did not have enough statistical power to show any mortality benefit. Therefore, the final mortality result of the only European lung cancer screening study that has sufficient statistical power, the NELSON trial, is eagerly awaited.

In the past few years, a number of European medical societies, related to diagnosis and treating lung cancer, has supported the implementation of LDCT lung cancer screening by publishing recommendations and policy statements, including the European Respiratory Society (ERS) (18), European Society of Radiology (ESR) (18), European Society of Thoracic Surgeons (ESTS) (19), European Alliance for Personalized Medicine (EAPM) (20), European Society of Medical Oncology (ESMO) (21), and Swiss University Hospitals (22). Furthermore, a consensus statement for lung cancer detection in Poland (23), and a joint protocol for LDCT lung cancer screening in the Nordic countries (24) have been published, implying that lung cancer screening will soon be implemented in the Nordic countries and Poland. Recently, the EU position statement (EUPS) was published, containing recommendations for the planning of lung cancer screening before the start of implementation in Europe, proposed by a European expert group on

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lung cancer screening (25).

The aim of this review is to summarize the latest evidence of European lung cancer screening trials, and to give an overview of the essence of recommendations from the different societies.

OVERVIEW OF EUROPEAN LDCT LUNG CANCER

SCREENING TRIALS

In contrast to the results of NLST, European lung cancer screening trials were not able to demonstrate lung cancer related mortality benefits to this point (Table 1). The most recently published results of the ITALUNG study showed a statistically non-significant overall mortality reduction of 17% (RR=0.83; 95% CI 0.67 to 1.03; p=0.08) and a statistically non-significant lung cancer specific mortality reduction of 30% (RR=0.70; 95% CI 0.47 to 1.03; p=0.07) (8). The final results (10-year follow-up) of the DLCST and the DANTE trial have also been published. Although DLCST and DANTE were not able to show lung cancer related mortality differences, there was a significant stage shift with more early-stage cancers found in the screening arm than in the control arm (Table 1). Among other things, European studies differ from NLST in their design. As stated by Silva

et al, the consequence of multiple independent LDCT trials resulted in the study design

and recruitment in European trials to be heterogeneous (Table 1) (26). This complicates direct pooling of the data from the European LDCT trials to study the influence of LDCT screening on lung cancer specific mortality. However, the diversity of European trials also provides a great opportunity to study different cost-effective approaches in LDCT screening, such as annual or biennial screening intervals, volumetric or diameter-based nodule management, and the selection of screening population.

CURRENT VIEW OF EUROPEAN MEDICAL SOCIETIES ON

LUNG CANCER SCREENING

Recently, a number of European medical societies published recommendations on the implementation of lung cancer screening (Table 2). Issues addressed by these societies include definition of the target population, optimal length of the screen interval, nodule measurement strategy and nodule management approach.

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23 Defining the target population

Most European LDCT screening trials have used age and smoking history as the major criteria for selecting a high-risk population eligible for lung cancer screening. The UKLS trial used the Liverpool Lung Project, version 2 (LLP_v2) risk model to include participants with at least 5% risk of developing lung cancer in the next 5 years. This resulted in an almost two times higher lung cancer detection rate (2.1%) compared to the baseline lung cancer detection rate of other European trials, excluding UKLS results (mean, 1.2%) (10,14,27–30). Thereby, they demonstrated that the use of a risk model could be beneficial for the cost-effectiveness of lung cancer screening.

In the white paper on lung cancer screening from ESR/ERS, recommended inclusion criteria for lung cancer screening are age between 55 and 80 years, at least 30 pack-years of tobacco smoking history, and either current smoker or ex-smoker who quit within the last 15 years (18). To improve quality and cost effectiveness, both ESR/ERS and ESTS have recommended the use of a risk prediction model. The EUPS suggests implementing a risk stratification approach for the inclusion of high risk individuals for lung cancer screening, by using a risk prediction model. While no specific risk prediction model was recommended, validated risk stratification methods such as PLCO_m2012 and LLP_v2 were some of its suggestions (25). In the joint protocol of the Nordic countries and recommendation of Swiss University Hospitals the NLST inclusion criteria were recommended: age between 55-74 years, ≥30 pack-years, current or former smoker who quit smoking within previous 15 years (24). In the Nordic joint protocol, it was stated that the use of risk-prediction models should be considered once their cost-effectiveness benefits have been proven in pilot studies in the Nordic countries. In Poland, an initial inclusion scenario of age between 55 and 74, 20 pack-years, current smoker, and ex-smoker who has quit within 15 years, has been proposed (23). EAPM and ESMO have not given specific recommendation on selection of target group for LDCT lung cancer screening. Although there is a slight variation in the recommendation regarding inclusion criteria for lung cancer screening by the different societies, there is still an overall agreement that validated risk prediction models are promising and should be considered for the selection of eligible lung cancer screening participants in the near future.

Optimal lung cancer screen interval

Almost all European lung cancer screening trials have used an annual screen interval (Table 1). The Italian MILD trial compared annual and biennial screening interval by randomizing participants to either of those groups, or to the control group. They found that the distribution of cancer staging and interval cancer detection rates were similar between the annual and biennial screen intervals arms (7). The NELSON study

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2 24 TABLE 1 . Char act eris tics of Eur op ean lu ng c ancer scr eenin g trials Trial Year of public ation Coun tr y of study Selection crit eria Ag e (Y ear s) Selection crit eria Pack -y ear s Scr eening me thods Participan ts in CT arm Per cen tag e of s tag e 1 cancer s Haz ar d r atio be tw een scr eening and con tr ol arm (95% CI) IT AL UNG (8) 2017 Italy 55-69 20 Annual LDC T v s no scr een f or 4 y ear s 1613 36% in scr eening arm 11% in c on tr ol arm 0.70 (0.47-1.03) UKLS (17) 2016 The Unit ed King dom 50-75 LLP v2 risk model Single LDC T v s no scr een 2028 67% * N /A DL CS T (11) 2016 Denmark 50-70 20 Annual LDC T v s usual car e f or 5 y ear s 2052 50% f or the scr eening arm 16% in the c on tr ol arm 1.03 (0.66-1.6) DANTE (9) 2015 Italy 60-74 20 Annual LDC T v s no scr een f or 4 y ear s 1264 71% in the LDC T arm 22% in the c on tr ol arm 0.99 (0.69-1.43) LUSI (15) 2015 German y 50-69 15 Annual LDC T and advice

for smoking cessa

tion for 5 y ear s v s smoking cessa tion alone 2029 72%* N /A MILD (7) 2012 Italy >49 20 No scr een v s annual scr een v s biennial LDC T for 5 y ear s 1190 (annual) 1186 (biennial) 62% in the annual LDC T arm 70% in the biennial LDC T arm* 1.52 (0.63-3.65) (combined) NELSON (12) N /A

The Netherlands and Belgium

50-75 15 LDC T in y ear 1, y ear 2, year 4 and y ear 6.5 v s no scr een 7915 N /A N /A *Only in forma tion for scr een ing arm w as p ro vid ed LDC T=lo w -d ose c omp ut ed tomogr ap hy , 95% CI=95% c on fiden ce in ter val, LLP= Liv erpool Lu ng P roject, v er sion 2, N/ A=u na vailab le,

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2 25 TABLE 2 . Curr en t vie w of Eur opean medic al socie ties on LDC T lu ng c ancer scr eenin g Rec ommenda tions Year of public ation

Use of risk prediction model

In ter val timing Me thod f or nodule siz e quan tific ation Nodule manag emen t pr ot oc ol In tegr ation of smoking cessa tion pr ogr amme Es tablishmen t of cen tr al na tional r egis tr y Poland (23) 2018

Has not been recommended

Annual, but c

an

be modified based on individual risk fact

or

s.

One dimensional measur

emen t is rec ommended Fleischner Socie ty Guideline Rec ommended Rec ommended EUPS (25) 2017 Rec ommended Curr en tly annual, but per sonalised appr oach should be

used in the futur

e. Semi-aut oma tic ally deriv ed v olume and volume-doubling time EUPS pr ot oc ol Rec ommended Rec ommended Join t pr ot oc ol of the Nor dic c oun tries (24) 2017 Rec ommends, once tes ted in pilot studies.

Baseline, then annual, ther

ea fter biennial Volume tric assessmen t should be included in the futur e scr eening s tudies. Pr ot oc ol partially based on B TS guidelines Rec ommended Rec ommended ESTS (19) 2017 Jus tified f or incr easing c os t-eff ectiv eness of lung c ancer scr eening N /A

Unidimensional, bidimensional or volume

tric assessmen t N /A Rec ommended Rec ommended ESR/ER S whit e paper (18) 2015 Rec ommended Curr en tly annual, per sonalised appr oach is sug ges ted for be tt er cos t-e fficiency Volume tric measur emen ts ar e pr ef err ed o ver diame ter measur emen ts N /A Rec ommended Rec ommended, also on a Eur opean le vel Swiss Univ er sity Hospit als (22) 2014

Has not been recommended

Annual Adv oc at es v olume tric assessmen t NCCN Guideline Rec ommended Rec ommended EUPS=Eu ropean Un ion p ositi on s ta temen t, BT S=Briti sh Th or acic Socie ty , ES TS=Eur op ean Socie ty of Th or acic Sur geon s, ESR=Eu rop ean Socie ty of Radiology , ER S=Eur opean R espir at or y Socie ty , N/ A=n ot a vailable

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used a different approach, with screen intervals varying between the screening rounds (one year between baseline and second screening round, two years between second and third screening round and 2.5 years between third and fourth screening round). A recently published article on the final 2.5-year screen interval of the NELSON study by Yousaf-Khan et al. reported that a 2.5 year screen interval led to a higher interval cancer rate compared to 1-year and 2-year intervals, and the proportion of lung cancers diagnosed at advanced stage was higher compared to shorter screen intervals (31). This implies that LDCT screen intervals should not exceed 2-years.

The use of personalized medicine by tailoring the length of screening interval to the risk of an individual patient could potentially reduce the costs, radiation exposure and the workload of radiologists. With this aim, Schreuder et al. compared various risk prediction models available for lung cancer screening including the Polynomial model, Patient characteristics model, Patz model, Diameter model, and Pan-Can model (32). The authors concluded that the Polynomial model, which included both patient characteristics and nodule morphology data is superior compared to all other models in the study, and that by implementing such a model 10.5% of 1 year follow-up scans could be avoided, without delaying cancer diagnosis.

However, as only two-year outcome was used for the modeling, their performance beyond two years is unknown. On the other hand, the length of screening interval could also be adjusted depending on the screening history of participants. In a recent study, Yousaf-Khan et al. evaluated three subgroups: negative, indeterminate, and positive, based on the results of the first three rounds, identified in the NELSON trial. They found that the intermediate subgroup had higher risk for detecting lung cancer than the negative subgroup in the fourth round of lung cancer screening (1.6% vs 0.6%, p=0.001) (33). This implies that risk prediction models and screening history can play a role in the optimization of screening follow-up.

Both the EUPS and ESR/ERS currently recommend annual screening for individuals who meet the inclusion criteria. However, they recommend future LDCT lung cancer screening programs to adopt a risk-based approach for increased quality and cost-effectiveness of lung cancer screening (18,25). The Nordic European protocol recommends baseline screening, followed by an annual screening, and thereafter biennial screening for participants without pulmonary nodules, whereas participants with nodules should be followed annually (24). The recommendation from Poland and Swiss university hospitals is annual screening. ESTS, ESMO, EAPM have not made a specific recommendation for the choice of screening interval to be adopted in European lung cancer screenings.

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27 Nodule measurement method and management protocol

A major challenge in lung cancer screening is to adequately manage the enormous number of small-to-intermediate sized pulmonary nodules, majority of them are benign. About half of screening participants have at least one such nodule, and 25% have more than one (34). Accurate assessment of nodule size is crucial to improve sensitivity and specificity of lung cancer detection. Both the NELSON trial and the UKLS trial have shown the benefit of using semi-automatic volumetric assessment instead of manual diameter measurements for the quantification of CT detected pulmonary nodules (27,35). This has presumably led to a reduction in the number of false positive test results, and hence, the number of follow-up and diagnostic examinations, with a comparable negative predictive value as in the NLST (13). The EUPS, the ESR/ERS, and the Nordic countries recommend the use of volumetric assessment of pulmonary nodules in lung cancer screening. However, volumetric analysis has not been established as part of radiological standard of care in Europe, and disagreement regarding its implementation still exists. On one hand, volumetric analysis has been shown to be more reliable and has better risk stratification than diameter analysis (36). Especially as it allows the calculation of volume doubling time, which is a valuable tool to differentiate malignant from benign nodules (37). On the other hand, the adoption of volumetric analysis also has its challenges. Firstly, as nodule segmentation is dependent on the segmentation algorithm, the performance of nodule segmentation between vendors may not be equal. Secondly, image reconstruction has been shown to influence nodule segmentation (38). As the algorithm for image reconstruction may differ between CT vendors, standardization of CT scanning protocol for nodule volumetric analysis may prove to be challenging. Currently, the ESTS has not provided recommendation for either diameter-based or volume-based nodule assessment approach. The consensus statement from a team of experts in Poland recommends the use of one-dimensional measurement of the nodule diameter, and awaits further evaluation of volume measurement and volume doubling time (VDT) before their consideration. Other European medical societies have not given recommendations on the approach of lung nodule assessment.

Nodule growth has been one of the main indicators for nodule malignancy. This has also been adopted in the guidelines of the American College of Radiology (LUNG-RADS) and British Thoracic Society (BTS) guidelines, where the growth cutoff was set as a fixed increase of 1.5 mm in diameter, and a 25% increase in nodule volume, respectively (39,40). Lung cancers appear to grow with an exponential growth pattern (41). Accumulating evidence indicate that semi-automatic volume measurements have higher reliability, as well as better sensitivity for nodule growth than diameter measurements (38,39,43). At present, there is a large discrepancy on lung nodule management strategy within

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Europe. While ESR/ERS and ESTS did not provide clear recommendations for nodule management, EUPS, Nordic countries, Poland, and Swiss University Hospitals have each recommended a different nodule management protocol for LDCT lung cancer screening (Table 2). It is interesting to note that while Poland and Swiss University Hospitals have preferred separate guidelines, Fleischner Society Guidelines and the NCCN Guidelines have both harmonized their nodule management protocol with Lung-RADS.

Challenges and future improvements

Europe is currently at a crossroad for the implementation of lung cancer screening. Decisions made now can have long lasting influence on the future quality and cost-effectiveness of lung cancer screening programs in Europe. Between the different European societies, there is a general preference for volumetric nodule assessment strategy (Table 2). Application of the optimal volume protocol is still underway, since the EUPS guideline has been just recently published. Agreement in the design and implementation of lung cancer screening between European countries and medical societies is needed to prevent heterogeneity in lung cancer screening programs between different European countries. As many experts who authored EUPS are also authors for previous European lung cancer screening recommendations, we expect that a gradual harmonization of European lung cancer screening recommendations will occur. This will support quality assurance and development of data registry at a European level. Considering the potential number of eligible participants in Europe, the ionizing radiation from CT needs to be strictly regulated. Current LDCT scanning protocols are performed at about approximately 1.5 mSv, which is approximately half the radiation dose of a standard thoracic CT. In the near future, it is possible to perform lung cancer screening down to ultra-low dose, about 1/10th_{of radiation dose of LDCT, by using spectral shaping}

and iterative reconstruction. There is mounting evidence showing that image quality and nodule detection are sufficient for this technology to be implemented in lung cancer screening programs in the near future (44–49).

Two additional harms of lung cancer screening are false-positive screening results and overdiagnosis. Though NLST could provide promising reduction of lung cancer related mortality, a large fraction (24.2%) of screen results was positive, with a very high false positive rate (96.4%). Changing the definition of positive screening result and increasing the nodule size cutoff for a negative result from 4 mm to 6 mm can reduce false positive rates (50–52). However, the approach of excluding indolent nodules based on their volumetric growth rate, as well as initial nodule management based on semi-automated nodule volume measurements will lead to more efficient screening programs, by decreasing the rate of false positives and overdiagnosis (37,41,53–59).

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Although overdiagnosis in terms of a higher lung cancer detection rate in the screen arm is inherent to cancer screening programs, the ITALUNG reported a catch-up in lung cancer diagnosis in the control arm, with no significant difference in lung cancer incidence rate between screening arm and non-screening arm at the end of the study, after a 8 year follow-up period (8). In contrast, results of NLST showed overdiagnosis of 18% (60). However, this may be exaggerated as it is the result of only 7 years of follow-up, the real rate of overdiagnosis may be in the range of 2% to 10%, and probably completely related to the stage-shift in the screen arm (61).

In case lung cancer screening is implemented in Europe, an overwhelming number of nodules will be encountered daily at the screening centers. Semi-automated volumetric software can speed up the process of nodule assessment, but manual adjustment of nodule contour will also be required. Deep learning techniques, although still in its infancy, can potentially off-load a large portion of work from radiologists’ shoulders by identifying those cases with no abnormalities of the lung parenchyma (62–65). Furthermore, the use of genetic and molecular biomarkers may allow better identification of those who are susceptible for lung cancer (66–69).

CONCLUSION

Although the smaller European lung cancer screening trials that published their final screen results so far were not able to show significant reduction in lung cancer related mortality, they have demonstrated that lung cancer screening by LDCT can lead to a considerable shift to diagnosis of early-staged lung cancers. This, and the evidence already provided by the NLST, suggests that lung cancer screening can reduce lung cancer related mortality.

Heterogeneity in European health care is one of the largest challenges faced by Europe in the implementation of LDCT lung cancer screening. Although there is a great amount of support by experts of European member states for lung cancer screening, there is still some variation between the recommendations from European medical societies and countries. Regarding measurement and management of lung nodules, almost all European societies prefer volumetric assessment over manual diameter measurements.

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BIBLIOGRAPHY

1. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet Lond Engl. 2012 Dec 15;380(9859):2095–128.

2. Walters S, Maringe C, Coleman MP, Peake MD, Butler J, Young N, et al. Lung cancer survival and stage at diagnosis in Australia, Canada, Denmark, Norway, Sweden and the UK: a population-based study, 2004-2007. Thorax. 2013 Jun;68(6):551–64. 3. National Lung Screening Trial Research Team, Aberle DR, Adams AM, Berg CD,

Black WC, Clapp JD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011 Aug 4;365(5):395–409.

4. Moyer VA. Screening for Lung Cancer: U.S. Preventive Services Task Force Recommendation Statement. Ann Intern Med. 2014 Mar 4;160(5):330–8.

5. Field JK, Smith RA, Aberle DR, Oudkerk M, Baldwin DR, Yankelevitz D, et al. International Association for the Study of Lung Cancer Computed Tomography Screening Workshop 2011 report. J Thorac Oncol Off Publ Int Assoc Study Lung Cancer. 2012 Jan;7(1):10–9.

6. Wood DE. National Comprehensive Cancer Network (NCCN) Clinical Practice Guidelines for Lung Cancer Screening. Thorac Surg Clin. 2015 May;25(2):185–97. 7. 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 Off J Eur Cancer Prev Organ ECP. 2012 May;21(3):308–15. 8. Paci E, Puliti D, Lopes Pegna A, Carrozzi L, Picozzi G, Falaschi F, et al. Mortality,

survival and incidence rates in the ITALUNG randomised lung cancer screening trial. Thorax. 2017;72(9):825–31.

9. Infante M, Cavuto S, Lutman FR, Brambilla G, Chiesa G, Ceresoli G, 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 Sep 1;180(5):445–53. 10. Pedersen JH, Ashraf H, Dirksen A, Bach K, Hansen H, Toennesen P, et al. The

Danish randomized lung cancer CT screening trial--overall design and results of the prevalence round. J Thorac Oncol Off Publ Int Assoc Study Lung Cancer. 2009 May;4(5):608–14.

11. Wille MMW, Dirksen A, Ashraf H, Saghir Z, Bach KS, Brodersen J, et al. Results of the Randomized Danish Lung Cancer Screening Trial with Focus on High-Risk Profiling. Am J Respir Crit Care Med. 2016 Mar 1;193(5):542–51.

12. van Iersel CA, de Koning HJ, Draisma G, Mali WPTM, 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

(14)

multi-559233-l-bw-Han 559233-l-bw-Han 559233-l-bw-Han 559233-l-bw-Han Processed on: 20-5-2021 Processed on: 20-5-2021 Processed on: 20-5-2021

2

31

slice CT screening trial (NELSON). Int J Cancer J Int Cancer. 2007 Feb 15;120(4):868– 74.

13. 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 Dec 3;361(23):2221–9.

14. Becker N, Motsch E, Gross M-L, Eigentopf A, Heussel CP, Dienemann H, 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 Sep;138(9):1475–86.

15. Becker N, Motsch E, Gross M-L, Eigentopf A, Heussel CP, Dienemann H, et al. Randomized Study on Early Detection of Lung Cancer with MSCT in Germany: Results of the First 3 Years of Follow-up After Randomization. J Thorac Oncol. 2015 Jun 1;10(6):890–6.

16. Field JK, Duffy SW, Baldwin DR, Whynes DK, Devaraj A, Brain KE, et al. UK Lung Cancer RCT Pilot Screening Trial: baseline findings from the screening arm provide evidence for the potential implementation of lung cancer screening. Thorax. 2016 Feb;71(2):161–70.

17. Field JK, Duffy SW, Baldwin DR, Brain KE, Devaraj A, Eisen T, 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 Winch Engl. 2016 May;20(40):1–146.

18. ESR/ERS white paper on lung cancer screening | European Respiratory Society [Internet]. [cited 2018 Apr 2]. Available from: http://erj.ersjournals.com/content/ early/2015/04/29/09031936.00033015

19. Pedersen JH, Rzyman W, Veronesi G, D’Amico TA, Van Schil P, Molins L, et al. Recommendations from the European Society of Thoracic Surgeons (ESTS) regarding computed tomography screening for lung cancer in Europe. Eur J Cardio-Thorac Surg Off J Eur Assoc Cardio-Thorac Surg. 2017 Mar 1;51(3):411–20.

20. European Alliance for Personalised Medicine - PUBLICATIONS / RESEARCHES [Internet]. [cited 2018 Apr 2]. Available from: https://www.euapm.eu/news.html 21. Vansteenkiste J, Crinò L, Dooms C, Douillard JY, Faivre-Finn C, Lim E, et al. 2nd ESMO

Consensus Conference on Lung Cancer: early-stage non-small-cell lung cancer consensus on diagnosis, treatment and follow-up. Ann Oncol Off J Eur Soc Med Oncol. 2014 Aug;25(8):1462–74.

22. Frauenfelder T, Puhan MA, Lazor R, von Garnier C, Bremerich J, Niemann T, et al. Early detection of lung cancer: a statement from an expert panel of the Swiss university hospitals on lung cancer screening. Respir Int Rev Thorac Dis. 2014;87(3):254–64. 23. Rzyman W, Didkowska J, Dziedzic R, Grodzki T, Orłowski T, Szurowska E, et al.

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2

32

cancer in Poland. Adv Respir Med. 2018;86(1):53–74.

24. Pedersen JH, Sørensen JB, Saghir Z, Fløtten Ø, Brustugun OT, Ashraf H, et al. Implementation of lung cancer CT screening in the Nordic countries. Acta Oncol. 2017 Oct 3;56(10):1249–57.

25. European position statement on lung cancer screening - The Lancet Oncology [Internet]. [cited 2018 Apr 2]. Available from: http://www.thelancet.com/journals/ lanonc/article/PIIS1470-2045(17)30861-6/fulltext

26. Silva M, Pastorino U, Sverzellati N. Lung cancer screening with low-dose CT in Europe: strength and weakness of diverse independent screening trials. Clin Radiol. 2017 May;72(5):389–400.

27. Horeweg N, Aalst CM van der, 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 Dec 1;42(6):1659–67.

28. Lopes Pegna A, Picozzi G, Mascalchi M, Maria Carozzi F, Carrozzi L, Comin C, et al. Design, recruitment and baseline results of the ITALUNG trial for lung cancer screening with low-dose CT. Lung Cancer. 2009 Apr 1;64(1):34–40.

29. Infante M, Lutman FR, Cavuto S, Brambilla G, Chiesa G, Passera E, et al. Lung cancer screening with spiral CT: baseline results of the randomized DANTE trial. Lung Cancer Amst Neth. 2008 Mar;59(3):355–63.

30. 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 Off J Eur Cancer Prev Organ ECP. 2012 May;21(3):308–15. 31. Yousaf-Khan U, Aalst C van der, Jong PA de, Heuvelmans M, Scholten E, Lammers

J-W, et al. Final screening round of the NELSON lung cancer screening trial: the effect of a 2.5-year screening interval. Thorax. 2017 Jan 1;72(1):48–56.

32. Schreuder A, Schaefer-Prokop CM, Scholten ET, Jacobs C, Prokop M, Ginneken B van. Lung cancer risk to personalise annual and biennial follow-up computed tomography screening. Thorax. 2018 Mar 30;thoraxjnl-2017-211107.

33. Yousaf-Khan U, Aalst C van der, Jong PA de, Heuvelmans M, Scholten E, Walter J, et al. Risk stratification based on screening history: the NELSON lung cancer screening study. Thorax. 2017 Mar 30;thoraxjnl-2016-209892.

34. Heuvelmans MA, Walter JE, Peters RB, Bock GH de, Yousaf-Khan U, Aalst CM van der, et al. Relationship between nodule count and lung cancer probability in baseline CT lung cancer screening: The NELSON study. Lung Cancer. 2017 Nov 1;113:45–50. 35. Baldwin DR, Duffy SW, Wald NJ, Page R, Hansell DM, Field JK. 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 Apr;66(4):308–13. 36. Heuvelmans MA, Walter JE, Vliegenthart R, van Ooijen PMA, De Bock GH, de Koning

(16)

2

33

nodule size estimation in CT lung cancer screening. Thorax. 2017 Oct 22;

37. Revel M-P. Avoiding overdiagnosis in lung cancer screening: the volume doubling time strategy. Eur Respir J. 2013 Dec 1;42(6):1459–63.

38. Wang Y, de Bock GH, van Klaveren RJ, van Ooyen P, Tukker W, Zhao Y, et al. Volumetric measurement of pulmonary nodules at low-dose chest CT: effect of reconstruction setting on measurement variability. Eur Radiol. 2010 May;20(5):1180–7.

39. BTS Guidelines for the Investigation and Management of Pulmonary Nodules | British Thoracic Society | Better lung health for all [Internet]. [cited 2018 Apr 9]. Available from: https://www.brit-thoracic.org.uk/standards-of-care/guidelines/bts-guidelines-for-the-investigation-and-management-of-pulmonary-nodules/

40. Lung CT Screening Reporting and Data System (Lung-RADSTM_{) - American College}

of Radiology [Internet]. [cited 2016 Aug 10]. Available from: http://www.acr.org/ Quality-Safety/Resources/LungRADS

41. Heuvelmans MA, Vliegenthart R, de Koning HJ, Groen HJM, van Putten MJAM, Yousaf-Khan U, et al. Quantification of growth patterns of screen-detected lung cancers: The NELSON study. Lung Cancer Amst Neth. 2017;108:48–54.

42. Han D, Heuvelmans MA, Vliegenthart R, Rook M, Dorrius MD, de Jonge GJ, et al. Influence of lung nodule margin on volume- and diameter-based reader variability in CT lung cancer screening. Br J Radiol. 2017 Nov 8;20170405.

43. Han D, Heuvelmans MA, Oudkerk M. Volume versus diameter assessment of small pulmonary nodules in CT lung cancer screening. Transl Lung Cancer Res. 2017 Feb;6(1):52–61.

44. Sui X, Meinel FG, Song W, Xu X, Wang Z, Wang Y, et al. Detection and size measurements of pulmonary nodules in ultra-low-dose CT with iterative reconstruction compared to low dose CT. Eur J Radiol. 2016 Mar;85(3):564–70.

45. Den Harder AM, Willemink MJ, van Hamersvelt RW, Vonken E-JPA, Milles J, Schilham AMR, et al. Effect of radiation dose reduction and iterative reconstruction on computer-aided detection of pulmonary nodules: Intra-individual comparison. Eur J Radiol. 2016 Feb;85(2):346–51.

46. Nomura Y, Higaki T, Fujita M, Miki S, Awaya Y, Nakanishi T, et al. Effects of Iterative Reconstruction Algorithms on Computer-assisted Detection (CAD) Software for Lung Nodules in Ultra-low-dose CT for Lung Cancer Screening. Acad Radiol. 2017 Feb 1;24(2):124–30.

47. Kim H, Min Park C, Chae H-D, Lee SM, Goo JM. Impact of radiation dose and iterative reconstruction on pulmonary nodule measurements at chest CT: a phantom study. Diagn Interv Radiol. 2015;21(6):459–65.

48. Gordic S, Morsbach F, Schmidt B, Allmendinger T, Flohr T, Husarik D, et al. Ultralow-dose chest computed tomography for pulmonary nodule detection: first performance evaluation of single energy scanning with spectral shaping. Invest

(17)

2

34

Radiol. 2014 Jul;49(7):465–73.

49. Martini K, Higashigaito K, Barth BK, Baumueller S, Alkadhi H, Frauenfelder T. Ultralow-dose CT with tin filtration for detection of solid and sub solid pulmonary nodules: a phantom study. Br J Radiol. 2015 Oct 22;20150389.

50. Henschke CI, Yip R, Yankelevitz DF, Smith JP, International Early Lung Cancer Action Program Investigators*. Definition of a positive test result in computed tomography screening for lung cancer: a cohort study. Ann Intern Med. 2013 Feb 19;158(4):246–52.

51. McKee BJ, Regis SM, McKee AB, Flacke S, Wald C. Performance of ACR Lung-RADS in a Clinical CT Lung Screening Program. J Am Coll Radiol. 2015 Mar;12(3):273–6. 52. Pinsky PF, Gierada DS, Black W, Munden R, Nath H, Aberle D, et al. Performance of

Lung-RADS in the National Lung Screening Trial: a retrospective assessment. Ann Intern Med. 2015 Apr 7;162(7):485–91.

53. Larici AR, Farchione A, Franchi P, Ciliberto M, Cicchetti G, Calandriello L, et al. Lung nodules: size still matters. Eur Respir Rev. 2017 Dec 31;26(146):170025.

54. Prokop M. Lung Cancer Screening: The Radiologist’s Perspective. Semin Respir Crit Care Med. 2014 Feb;35(01):091–8.

55. Mikita K, Saito H, Sakuma Y, Kondo T, Honda T, Murakami S, et al. Growth rate of lung cancer recognized as small solid nodule on initial CT findings. Eur J Radiol. 2012 Apr 1;81(4):e548–53.

56. Heuvelmans MA, Oudkerk M, de Bock GH, de Koning HJ, Xie X, van Ooijen PMA, et al. Optimisation of volume-doubling time cutoff for fast-growing lung nodules in CT lung cancer screening reduces false-positive referrals. Eur Radiol. 2013 Jul;23(7):1836–45.

57. Walter JE, Heuvelmans MA, Jong PA de, Vliegenthart R, Ooijen PMA van, Peters RB, et al. Occurrence and lung cancer probability of new solid nodules at incidence screening with low-dose CT: analysis of data from the randomised, controlled NELSON trial. Lancet Oncol. 2016 Jul 1;17(7):907–16.

58. Ko JP, Berman EJ, Kaur M, Babb JS, Bomsztyk E, Greenberg AK, et al. Pulmonary Nodules: Growth Rate Assessment in Patients by Using Serial CT and Three-dimensional Volumetry. Radiology. 2012 Feb;262(2):662–71.

59. Horeweg N, Scholten ET, de Jong PA, van der Aalst CM, Weenink C, Lammers J-WJ, et al. Detection of lung cancer through low-dose CT screening (NELSON): a prespecified analysis of screening test performance and interval cancers. Lancet Oncol. 2014 Nov;15(12):1342–50.

60. Patz EF, Pinsky P, Gatsonis C, Sicks JD, Kramer BS, Tammemägi MC, et al. Overdiagnosis in Low-Dose Computed Tomography Screening for Lung Cancer. JAMA Intern Med. 2014 Feb 1;174(2):269–74.

(18)

2

35

in Screening for Lung Cancer Are Grossly Exaggerated. Acad Radiol. 2015 Aug 1;22(8):976–82.

62. Ciompi F, Chung K, Riel SJ van, Setio AAA, Gerke PK, Jacobs C, et al. Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Sci Rep. 2017 Apr 19;7:46479.

63. Song Q, Zhao L, Luo X, Dou X. Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images. J Healthc Eng [Internet]. 2017 [cited 2018 Apr 17];2017. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/ PMC5569872/

64. Huang X, Shan J, Vaidya V. Lung nodule detection in CT using 3D convolutional neural networks. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). 2017. p. 379–83.

65. Nithila EE, Kumar SS. Automatic detection of solitary pulmonary nodules using swarm intelligence optimized neural networks on CT images. Eng Sci Technol Int J. 2017 Jun 1;20(3):1192–202.

66. Silvestri GA, Vachani A, Whitney D, Elashoff M, Porta Smith K, Ferguson JS, et al. A Bronchial Genomic Classifier for the Diagnostic Evaluation of Lung Cancer. N Engl J Med. 2015 Jul 16;373(3):243–51.

67. Sestini S, Boeri M, Marchiano A, Pelosi G, Galeone C, Verri C, et al. Circulating microRNA signature as liquid-biopsy to monitor lung cancer in low-dose computed tomography screening. Oncotarget. 2015 Oct 20;6(32):32868–77.

68. Amos CI, Dennis J, Wang Z, Byun J, Schumacher FR, Gayther SA, et al. The OncoArray Consortium: A Network for Understanding the Genetic Architecture of Common Cancers. Cancer Epidemiol Biomark Prev Publ Am Assoc Cancer Res Cosponsored Am Soc Prev Oncol. 2017;26(1):126–35.

69. Leitner-Dagan Y, Sevilya Z, Pinchev M, Kremer R, Elinger D, Rennert HS, et al. Enzymatic MPG DNA repair assays for two different oxidative DNA lesions reveal associations with increased lung cancer risk. Carcinogenesis. 2014 Dec 1;35(12):2763–70.

(19)