Literature DB >> 23425165

Selection criteria for lung-cancer screening.

Martin C Tammemägi1, Hormuzd A Katki, William G Hocking, Timothy R Church, Neil Caporaso, Paul A Kvale, Anil K Chaturvedi, Gerard A Silvestri, Tom L Riley, John Commins, Christine D Berg.   

Abstract

BACKGROUND: The National Lung Screening Trial (NLST) used risk factors for lung cancer (e.g., ≥30 pack-years of smoking and <15 years since quitting) as selection criteria for lung-cancer screening. Use of an accurate model that incorporates additional risk factors to select persons for screening may identify more persons who have lung cancer or in whom lung cancer will develop.
METHODS: We modified the 2011 lung-cancer risk-prediction model from our Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial to ensure applicability to NLST data; risk was the probability of a diagnosis of lung cancer during the 6-year study period. We developed and validated the model (PLCO(M2012)) with data from the 80,375 persons in the PLCO control and intervention groups who had ever smoked. Discrimination (area under the receiver-operating-characteristic curve [AUC]) and calibration were assessed. In the validation data set, 14,144 of 37,332 persons (37.9%) met NLST criteria. For comparison, 14,144 highest-risk persons were considered positive (eligible for screening) according to PLCO(M2012) criteria. We compared the accuracy of PLCO(M2012) criteria with NLST criteria to detect lung cancer. Cox models were used to evaluate whether the reduction in mortality among 53,202 persons undergoing low-dose computed tomographic screening in the NLST differed according to risk.
RESULTS: The AUC was 0.803 in the development data set and 0.797 in the validation data set. As compared with NLST criteria, PLCO(M2012) criteria had improved sensitivity (83.0% vs. 71.1%, P<0.001) and positive predictive value (4.0% vs. 3.4%, P=0.01), without loss of specificity (62.9% and. 62.7%, respectively; P=0.54); 41.3% fewer lung cancers were missed. The NLST screening effect did not vary according to PLCO(M2012) risk (P=0.61 for interaction).
CONCLUSIONS: The use of the PLCO(M2012) model was more sensitive than the NLST criteria for lung-cancer detection.

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Year:  2013        PMID: 23425165      PMCID: PMC3929969          DOI: 10.1056/NEJMoa1211776

Source DB:  PubMed          Journal:  N Engl J Med        ISSN: 0028-4793            Impact factor:   91.245


  23 in total

1.  A risk model for prediction of lung cancer.

Authors:  Margaret R Spitz; Waun Ki Hong; Christopher I Amos; Xifeng Wu; Matthew B Schabath; Qiong Dong; Sanjay Shete; Carol J Etzel
Journal:  J Natl Cancer Inst       Date:  2007-05-02       Impact factor: 13.506

2.  Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond.

Authors:  Michael J Pencina; Ralph B D'Agostino; Ralph B D'Agostino; Ramachandran S Vasan
Journal:  Stat Med       Date:  2008-01-30       Impact factor: 2.373

3.  Baseline chest radiograph for lung cancer detection in the randomized Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial.

Authors:  Martin M Oken; Pamela M Marcus; Ping Hu; Thomas M Beck; William Hocking; Paul A Kvale; Jill Cordes; Thomas L Riley; Stephen D Winslow; Steven Peace; David L Levin; Philip C Prorok; John K Gohagan
Journal:  J Natl Cancer Inst       Date:  2005-12-21       Impact factor: 13.506

4.  Interpreting incremental value of markers added to risk prediction models.

Authors:  Michael J Pencina; Ralph B D'Agostino; Karol M Pencina; A Cecile J W Janssens; Philip Greenland
Journal:  Am J Epidemiol       Date:  2012-08-08       Impact factor: 4.897

5.  Evidence of a healthy volunteer effect in the prostate, lung, colorectal, and ovarian cancer screening trial.

Authors:  P F Pinsky; A Miller; B S Kramer; T Church; D Reding; P Prorok; E Gelmann; R E Schoen; S Buys; R B Hayes; C D Berg
Journal:  Am J Epidemiol       Date:  2007-01-22       Impact factor: 4.897

6.  Design of the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial.

Authors:  P C Prorok; G L Andriole; R S Bresalier; S S Buys; D Chia; E D Crawford; R Fogel; E P Gelmann; F Gilbert; M A Hasson; R B Hayes; C C Johnson; J S Mandel; A Oberman; B O'Brien; M M Oken; S Rafla; D Reding; W Rutt; J L Weissfeld; L Yokochi; J K Gohagan
Journal:  Control Clin Trials       Date:  2000-12

7.  Validation of a model of lung cancer risk prediction among smokers.

Authors:  Kathleen A Cronin; Mitchell H Gail; Zhaohui Zou; Peter B Bach; Jarmo Virtamo; Demetrius Albanes
Journal:  J Natl Cancer Inst       Date:  2006-05-03       Impact factor: 13.506

8.  Variations in lung cancer risk among smokers.

Authors:  Peter B Bach; Michael W Kattan; Mark D Thornquist; Mark G Kris; Ramsey C Tate; Matt J Barnett; Lillian J Hsieh; Colin B Begg
Journal:  J Natl Cancer Inst       Date:  2003-03-19       Impact factor: 13.506

9.  Prediction of true positive lung cancers in individuals with abnormal suspicious chest radiographs: a prostate, lung, colorectal, and ovarian cancer screening trial study.

Authors:  Martin Carl Tammemagi; Matthew T Freedman; Paul F Pinsky; Martin M Oken; Ping Hu; Thomas L Riley; Lawrence R Ragard; Christine D Berg; Philip C Prorok
Journal:  J Thorac Oncol       Date:  2009-06       Impact factor: 15.609

10.  Dichotomizing continuous predictors in multiple regression: a bad idea.

Authors:  Patrick Royston; Douglas G Altman; Willi Sauerbrei
Journal:  Stat Med       Date:  2006-01-15       Impact factor: 2.373

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  257 in total

1.  Effects of personalized colorectal cancer risk information on laypersons' interest in colorectal cancer screening: The importance of individual differences.

Authors:  Paul K J Han; Christine W Duarte; Susannah Daggett; Andrea Siewers; Bill Killam; Kahsi A Smith; Andrew N Freedman
Journal:  Patient Educ Couns       Date:  2015-07-19

2.  Clinical Utility of Chromosomal Aneusomy in Individuals at High Risk of Lung Cancer.

Authors:  Anna E Barón; Severine Kako; William J Feser; Heather Malinowski; Daniel Merrick; Kavita Garg; Stephen Malkoski; Shannon Pretzel; Jill M Siegfried; Wilbur A Franklin; York Miller; Holly J Wolf; Marileila Varella-Garcia
Journal:  J Thorac Oncol       Date:  2017-06-19       Impact factor: 15.609

3.  Improving selection criteria for lung cancer screening. The potential role of emphysema.

Authors:  Pablo Sanchez-Salcedo; David O Wilson; Juan P de-Torres; Joel L Weissfeld; Juan Berto; Arantzazu Campo; Ana B Alcaide; Jesús Pueyo; Gorka Bastarrika; Luis M Seijo; Maria J Pajares; Ruben Pio; Luis M Montuenga; Javier J Zulueta
Journal:  Am J Respir Crit Care Med       Date:  2015-04-15       Impact factor: 21.405

Review 4.  Screening for lung cancer with low-dose computed tomography: a review of current status.

Authors:  Henry M Marshall; Rayleen V Bowman; Ian A Yang; Kwun M Fong; Christine D Berg
Journal:  J Thorac Dis       Date:  2013-10       Impact factor: 2.895

Review 5.  Lung cancer early detection and health disparities: the intersection of epigenetics and ethnicity.

Authors:  Lane Lerner; Robert Winn; Alicia Hulbert
Journal:  J Thorac Dis       Date:  2018-04       Impact factor: 2.895

Review 6.  Implementing lung cancer screening in the real world: opportunity, challenges and solutions.

Authors:  Robert J Optican; Caroline Chiles
Journal:  Transl Lung Cancer Res       Date:  2015-08

7.  Deep Multi-task Prediction of Lung Cancer and Cancer-free Progression from Censored Heterogenous Clinical Imaging.

Authors:  Riqiang Gao; Lingfeng Li; Yucheng Tang; Sanja L Antic; Alexis B Paulson; Yuankai Huo; Kim L Sandler; Pierre P Massion; Bennett A Landman
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2020-03-10

8.  Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method.

Authors:  Peng Huang; Cheng T Lin; Yuliang Li; Martin C Tammemagi; Malcolm V Brock; Sukhinder Atkar-Khattra; Yanxun Xu; Ping Hu; John R Mayo; Heidi Schmidt; Michel Gingras; Sergio Pasian; Lori Stewart; Scott Tsai; Jean M Seely; Daria Manos; Paul Burrowes; Rick Bhatia; Ming-Sound Tsao; Stephen Lam
Journal:  Lancet Digit Health       Date:  2019-10-17

Review 9.  Risk factors assessment and risk prediction models in lung cancer screening candidates.

Authors:  Mariusz Adamek; Ewa Wachuła; Sylwia Szabłowska-Siwik; Agnieszka Boratyn-Nowicka; Damian Czyżewski
Journal:  Ann Transl Med       Date:  2016-04

10.  Should Never-Smokers at Increased Risk for Lung Cancer Be Screened?

Authors:  Kevin Ten Haaf; Harry J de Koning
Journal:  J Thorac Oncol       Date:  2015-09       Impact factor: 15.609

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