***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: BACKGROUND US guidelines now recommend lung cancer screening with low-dose CT for high-risk individuals . Reports of new nodules after baseline screening have been scarce and are inconsistent because of differences in definitions used . We aim ed to identify the occurrence of new solid nodules and their probability of being lung cancer at incidence screening rounds in the Dutch-Belgian R and omized Lung Cancer Screening Trial ( NELSON ) . METHODS In the ongoing , multicentre , r and omised controlled NELSON trial , between Dec 23 , 2003 , and July 6 , 2006 , 15 822 participants who had smoked at least 15 cigarettes a day for more than 25 years or ten cigarettes a day for more than 30 years and were current smokers , or had quit smoking less than 10 years ago , were enrolled and r and omly assigned to receive either screening with low-dose CT ( n=7915 ) or no screening ( n=7907 ) . From Jan 28 , 2004 , to Dec 18 , 2006 , 7557 individuals underwent baseline screening with low-dose CT ; 7295 participants underwent second and third screening rounds . We included all participants with solid non-calcified nodules , registered by the NELSON radiologists as new or smaller than 15 mm(3 ) ( study detection limit ) at previous screens . Nodule volume was generated semiautomatically by software . We calculated the maximum volume doubling time for nodules with an estimated percentage volume change of 25 % or more , representing the minimum growth rate for the time since the previous scan . Lung cancer diagnosis was based on histology , and benignity was based on histology or stable size for at least 2 years . The NELSON trial is registered at trialregister.nl , number IS RCT N63545820 . FINDINGS We analysed data for participants with at least one solid non-calcified nodule at the second or third screening round . In the two incidence screening rounds , the NELSON radiologists registered 1222 new solid nodules in 787 ( 11 % ) participants . A new solid nodule was lung cancer in 49 ( 6 % ) participants with new solid nodules and , in total , 50 lung cancers were found , representing 4 % of all new solid nodules . 34 ( 68 % ) lung cancers were diagnosed at stage I. Nodule volume had a high discriminatory power ( area under the receiver operating curve 0·795 [ 95 % CI 0·728 - 0·862 ] ; p<0·0001 ) . Nodules smaller than 27 mm(3 ) had a low probability of lung cancer ( two [ 0·5 % ] of 417 nodules ; lung cancer probability 0·5 % [ 95 % CI 0·0 - 1·9 ] ) , nodules with a volume of 27 mm(3 ) up to 206 mm(3 ) had an intermediate probability ( 17 [ 3·1 % ] of 542 nodules ; lung cancer probability 3·1 % [ 1·9 - 5·0 ] ) , and nodules of 206 mm(3 ) or greater had a high probability ( 29 [ 16·9 % ] of 172 nodules ; lung cancer probability 16·9 % [ 12·0 - 23·2 ] ) . A volume cutoff value of 27 mm(3 ) or greater had more than 95 % sensitivity for lung cancer . INTERPRETATION Our study shows that new solid nodules are detected at each screening round in 5 - 7 % of individuals who undergo screening for lung cancer with low-dose CT . These new nodules have a high probability of malignancy even at a small size . These findings should be considered in future screening guidelines , and new solid nodules should be followed up more aggressively than nodules detected at baseline screening . FUNDING Zorgonderzoek Nederl and Medische Wetenschappen and Koningin Wilhelmina Fonds Kankerbestrijding BACKGROUND Results from retrospective studies indicate that selecting individuals for  low-dose CT lung cancer screening  on the basis of a highly predictive risk model is superior to using criteria similar to those used in the National Lung Screening Trial ( NLST ; age , pack-year , and smoking quit-time ) . We design ed the Pan-Canadian Early Detection of Lung Cancer ( PanCan ) study to assess the efficacy of a risk prediction model to select c and i date s for lung cancer screening , with the aim of determining whether this approach could better detect patients with early , potentially curable , lung cancer . METHODS We did this single-arm , prospect i ve study in eight centres across Canada . We recruited participants aged 50 - 75 years , who had smoked at some point in their life ( ever-smokers ) , and who did not have a self-reported history of lung cancer . Participants had at least a 2 % 6-year risk of lung cancer as estimated by the PanCan model , a precursor to the vali date d PLCOm2012 model . Risk variables in the model were age , smoking duration , pack-years , family history of lung cancer , education level , body-mass index , chest x-ray in the past 3 years , and history of chronic obstructive pulmonary disease . Individuals were screened with low-dose CT at baseline ( T0 ) , and at 1 ( T1 ) and 4 ( T4 ) years post-baseline . The primary outcome of the study was incidence of lung cancer . This study is registered with Clinical Trials.gov , number NCT00751660 . FINDINGS 7059 queries came into the study coordinating centre and were screened for PanCan risk . 15 were duplicates , so 7044 participants were considered for enrolment . Between Sept 24 , 2008 , and Dec 17 , 2010 , we recruited and enrolled 2537 eligible ever-smokers . After a median follow-up of 5·5 years ( IQR 3·2 - 6·1 ) , 172 lung cancers were diagnosed in 164 individuals ( cumulative incidence 0·065 [ 95 % CI 0·055 - 0·075 ] , incidence rate 138·1 per 10 000 person-years [ 117·8 - 160·9 ] ) . There were ten interval lung cancers ( 6 % of lung cancers and 6 % of individuals with cancer ) : one diagnosed between T0 and T1 , and nine between T1 and T4 . Cumulative incidence was significantly higher than that observed in NLST ( 4·0 % ; p<0·0001 ) . Compared with 593 ( 57 % ) of 1040 lung cancers observed in NLST , 133 ( 77 % ) of 172 lung cancers in the PanCan Study were early stage ( I or II ; p<0·0001 ) . INTERPRETATION The PanCan model was effective in identifying individuals who were subsequently diagnosed with early , potentially curable , lung cancer . The incidence of cancers detected and the proportion of early stage cancers in the screened population was higher than observed in previous studies . This approach should be considered for adoption in lung cancer screening programmes . FUNDING Terry Fox Research Institute and Canadian Partnership Against Cancer The burden of lung cancer in the world remains extremely high : The International Agency for Research on Cancer estimated 1.6 million new diagnoses in 2008 ( 12.7 % of total cases of cancer ) and 1.4 million deaths ( 18.2 % of total cancer mortality ) ( 1 ) . In the United States and Canada , incidence ( per 100000 ) is 48.5 for men and 35.8 for women ; mortality ( per 100000 ) is 37.9 and 24.2 , respectively ; and cumulative risk ( to age 74 years ) of dying of lung cancer is 3 % in women and 4.6 % in men . In the United States , 228000 new cases of lung cancer and about 160000 deaths are estimated for 2013 ( 2 ) . Despite substantial reductions in smoking prevalence in the United States , which translated into an approximately 32 % reduction in lung cancer mortality between 1975 and 2000 at the population level ( 3 ) , lung cancer remains the leading cause of cancer death . Recently , the National Lung Screening Trial ( NLST ) demonstrated that in a volunteer population of current and former smokers who were aged 55 to 74 years at entry , had at least 30 pack-years of cigarette smoking history , and had quit no more than 15 years previously ( for former smokers ) , 3 annual computed tomography ( CT ) screening examinations reduced lung cancerspecific mortality by 20 % relative to 3 annual chest radiography screening examinations at a median follow-up of 6.5 years ( 4 ) . This trial did not directly address the effects of additional rounds of screening , long-term benefits or harms , or multiple alternative screening policies with different screening intervals and different eligibility criteria . Moreover , long-term outcomes must be quantified to underst and the tradeoffs between benefits and potential harms involved with alternative screening strategies ( 5 ) . In this study , we estimate future harms and benefits of lung cancer screening and identify a set of possible efficient lung cancer screening policies by using 5 separately developed microsimulation models calibrated to the 2 largest r and omized , controlled trials on lung cancer screening . This work was initiated by the U.S. Preventive Services Task Force ( USPSTF ) to inform its recommendations on lung cancer screening . Methods Calibration of 5 Models to Deidentified Lung Cancer Screening Data From the NLST and Prostate , Lung , Colorectal , and Ovarian Cancer Screening Trial We used 5 models calibrated to individual-level , de-identified data from the NLST ( 6 ) and the PLCO ( Prostate , Lung , Colorectal , and Ovarian Cancer Screening ) trial ( 7 ) . The NLST enrolled 53452 persons at high risk for lung cancer at 33 U.S. centers from August 2002 through April 2004 . Participants were r and omly assigned to undergo 3 annual screening examinations with low-dose CT ( 26722 participants ) or single-view posterioranterior chest radiography ( 26730 participants ) . The PLCO trial r and omly assigned 154901 participants aged 55 through 74 years at entry , 77445 of whom were assigned to annual chest radiography and 77456 to usual care between November 1993 and July 2001 . There was no eligibility requirement concerning smoking . Although the PLCO trial compared chest radiography with no screening , it provided information on the natural history of lung cancer . Groups of investigators at the following 5 institutions independently developed the models : Erasmus Medical Center in Rotterdam , the Netherl and s ( model E ) ; Fred Hutchinson Cancer Research Center in Seattle , Washington ( model F ) ; the Massachusetts General Hospital in Boston , Massachusetts ( model M ) ; Stanford University in Stanford , California ( model S ) ; and the University of Michigan in Ann Arbor , Michigan ( model U ) . Each model estimates screening effectiveness on the basis of a different set of assumptions that are key in predicting the effects of earlier treatment , and each model uses different mathematical formalisms and model structures . In essence , all the models account for the individual 's age-specific smoking-related risk for lung cancer , date and stage of lung cancer diagnosis , the corresponding lung cancer mortality , and the individual 's life expectancy in the presence and absence of screening ( Appendix Figure 1 ) . Appendix Figure 1 . Diagram of how earlier detection ( followed by treatment ) may have an effect on reducing serious consequences of the disease and /or increasing life expectancy . All models account for the individual 's age-specific smoking-related risk for lung cancer , the date and stage of lung cancer diagnosis , the corresponding lung cancer mortality , and the individual 's life expectancy in the presence and absence of screening . By replicating trial detection , models estimate key parameters of the screening-detectable period and /or sensitivity and can subsequently estimate cancer detected in the screening scenarios . In essence , when a model incorporates the exact demographic characteristics of participants and the design of a trial , it should be able to reproduce cumulative incidence of lung cancer ( by stage , histologic features , sex , age , type of detection , and round ) and lung cancer mortality in both groups as closely as possible . The best fit is often defined as the lowest deviance between observed and model-expected numbers . For correct extrapolation of different possible screening scenarios , one must obtain the best estimates on several key parameters , including the duration of the screening-detectable pre clinical period ( by test , age , histologic characteristics , and sex ) , sensitivity ( by test , age , and sex ) , and improvement in prognosis by earlier detection and treatment . All models were first set to mimic the design of both trials ( for example , setting the numbers of screening examinations and screening method , ages at screening , smoking history and sex of enrollees , and screening intervals ) . The models are validly calibrated when the key parameterswhich may differ by modelcan be estimated or adjusted to replicate the trial data closely . After calibration , the models reproduce the observed cumulative incidence of lung cancer ( by stage , histologic characteristics , sex , age , type of detection , and round of screening ) and lung cancer mortality in both groups of the trials . Close calibration to the 19 % ( 95 % CI , 7 % to 25 % ) lung cancer mortality difference between groups of the NLST at 6 years of follow-up was prioritized ( see Appendix Figure 2 and Appendix Table , for key similarities and differences among the 5 models in calibration targets . ) Appendix Figure 2 . Percentage and 95 % CI of lung cancer mortality in chest radiography group compared with computed tomography group in the NLST , by follow-up duration and comparison with 5 model group results 
    Output:
    
RESULTS Annual lung cancer screening with LDCT is recommended for high risk individuals aged 50 - 74 years who have at least a 20 pack-year smoking history and who currently smoke or have quit within the past five years .