Literature DB >> 30050763

Selecting lung cancer screenees using risk prediction models-where do we go from here.

Martin C Tammemägi1.   

Abstract

The National Lung Screening Trial (NLST) demonstrated that low dose computed tomography (LDCT) screening could reduce lung cancer mortality by 20% in high-risk individuals. The United States Preventive Services Task Force (USPSTF) and Centers for Medicare and Medicaid Services (CMS) approved lung cancer screening. The NLST, USPSTF and CMS define high risk as smoking ≥30 pack-years, smoking within the past 15 years, and being ages 55-74, 55-80 or 55-77. Retrospective studies demonstrated selection using model-estimated risk is superior to NLST-like criteria: higher sensitivity and positive predictive value (PPV), more deaths averted and higher cost-effectiveness. Projects are underway that may additionally support use of risk to determine eligibility. Firstly, the International Lung Screen Trial (ILST) is prospectively enrolling 4,000 individuals for screening if individuals have PLCOm2012 model risk ≥1.5% or are USPSTF+ve. Six-year follow-up will allow comparisons. Interim results support the risk approach. Secondly, Cancer Care Ontario started the Lung Cancer Screening Pilot for People at High Risk in order to find optimal design for province-wide programmatic screening. They are enrolling 3,000 individuals to screening based on PLCOm2012 risk ≥2%. Some hesitation to recommend screening selection based on model risk comes from the observation that selected individuals are older, have more comorbidities, are expected to have fewer life years and quality-adjusted life years (QALY) and are more likely to die from competing causes. We show that 25.6% of NLST eligible smokers are at low risk (6-year lung cancer incidence proportion =0.008). This group will not benefit from screening but has lower age, fewer comorbidities and fewer competing causes of death. When they are excluded from the NLST+ve group, age, comorbidity count and competing causes of death are similar to those in the PLCOm2012+ve group. In some jurisdictions, model-based lung cancer screening selection needs to take into consideration the elevated risk in blacks and indigenous peoples.

Entities:  

Keywords:  Lung cancer screening; risk models; risk prediction

Year:  2018        PMID: 30050763      PMCID: PMC6037970          DOI: 10.21037/tlcr.2018.06.03

Source DB:  PubMed          Journal:  Transl Lung Cancer Res        ISSN: 2218-6751


  29 in total

1.  Cancer incidence in indigenous people in Australia, New Zealand, Canada, and the USA: a comparative population-based study.

Authors:  Suzanne P Moore; Sébastien Antoni; Amy Colquhoun; Bonnie Healy; Lis Ellison-Loschmann; John D Potter; Gail Garvey; Freddie Bray
Journal:  Lancet Oncol       Date:  2015-10-22       Impact factor: 41.316

2.  Measuring cancer in indigenous populations.

Authors:  Diana Sarfati; Gail Garvey; Bridget Robson; Suzanne Moore; Ruth Cunningham; Diana Withrow; Kalinda Griffiths; Nadine R Caron; Freddie Bray
Journal:  Ann Epidemiol       Date:  2018-02-15       Impact factor: 3.797

3.  A simple model for predicting lung cancer occurrence in a lung cancer screening program: The Pittsburgh Predictor.

Authors:  David O Wilson; Joel Weissfeld
Journal:  Lung Cancer       Date:  2015-03-28       Impact factor: 5.705

4.  Selecting High-Risk Individuals for Lung Cancer Screening: A Prospective Evaluation of Existing Risk Models and Eligibility Criteria in the German EPIC Cohort.

Authors:  Kuanrong Li; Anika Hüsing; Disorn Sookthai; Manuela Bergmann; Heiner Boeing; Nikolaus Becker; Rudolf Kaaks
Journal:  Cancer Prev Res (Phila)       Date:  2015-06-15

Review 5.  Application of risk prediction models to lung cancer screening: a review.

Authors:  Martin C Tammemägi
Journal:  J Thorac Imaging       Date:  2015-03       Impact factor: 3.000

6.  Comparative analysis of 5 lung cancer natural history and screening models that reproduce outcomes of the NLST and PLCO trials.

Authors:  Rafael Meza; Kevin ten Haaf; Chung Yin Kong; Ayca Erdogan; William C Black; Martin C Tammemagi; Sung Eun Choi; Jihyoun Jeon; Summer S Han; Vidit Munshi; Joost van Rosmalen; Paul Pinsky; Pamela M McMahon; Harry J de Koning; Eric J Feuer; William D Hazelton; Sylvia K Plevritis
Journal:  Cancer       Date:  2014-02-27       Impact factor: 6.860

7.  Targeting of low-dose CT screening according to the risk of lung-cancer death.

Authors:  Anil K Chaturvedi; Hormuzd A Katki; Stephanie A Kovalchik; Martin Tammemagi; Christine D Berg; Neil E Caporaso; Tom L Riley; Mary Korch; Gerard A Silvestri
Journal:  N Engl J Med       Date:  2013-07-18       Impact factor: 91.245

8.  Low-dose lung computed tomography screening before age 55: estimates of the mortality reduction required to outweigh the radiation-induced cancer risk.

Authors:  Amy Berrington de González; Kwang Pyo Kim; Christine D Berg
Journal:  J Med Screen       Date:  2008       Impact factor: 2.136

9.  Participant selection for lung cancer screening by risk modelling (the Pan-Canadian Early Detection of Lung Cancer [PanCan] study): a single-arm, prospective study.

Authors:  Martin C Tammemagi; Heidi Schmidt; Simon Martel; Annette McWilliams; John R Goffin; Michael R Johnston; Garth Nicholas; Alain Tremblay; Rick Bhatia; Geoffrey Liu; Kam Soghrati; Kazuhiro Yasufuku; David M Hwang; Francis Laberge; Michel Gingras; Sergio Pasian; Christian Couture; John R Mayo; Paola V Nasute Fauerbach; Sukhinder Atkar-Khattra; Stuart J Peacock; Sonya Cressman; Diana Ionescu; John C English; Richard J Finley; John Yee; Serge Puksa; Lori Stewart; Scott Tsai; Ehsan Haider; Colm Boylan; Jean-Claude Cutz; Daria Manos; Zhaolin Xu; Glenwood D Goss; Jean M Seely; Kayvan Amjadi; Harmanjatinder S Sekhon; Paul Burrowes; Paul MacEachern; Stefan Urbanski; Don D Sin; Wan C Tan; Natasha B Leighl; Frances A Shepherd; William K Evans; Ming-Sound Tsao; Stephen Lam
Journal:  Lancet Oncol       Date:  2017-10-18       Impact factor: 41.316

10.  Performance and Cost-Effectiveness of Computed Tomography Lung Cancer Screening Scenarios in a Population-Based Setting: A Microsimulation Modeling Analysis in Ontario, Canada.

Authors:  Kevin Ten Haaf; Martin C Tammemägi; Susan J Bondy; Carlijn M van der Aalst; Sumei Gu; S Elizabeth McGregor; Garth Nicholas; Harry J de Koning; Lawrence F Paszat
Journal:  PLoS Med       Date:  2017-02-07       Impact factor: 11.069

View more
  9 in total

1.  External Validation of Risk Prediction Models Incorporating Common Genetic Variants for Incident Colorectal Cancer Using UK Biobank.

Authors:  Catherine L Saunders; Britt Kilian; Deborah J Thompson; Luke J McGeoch; Simon J Griffin; Antonis C Antoniou; Jon D Emery; Fiona M Walter; Joe Dennis; Xin Yang; Juliet A Usher-Smith
Journal:  Cancer Prev Res (Phila)       Date:  2020-02-18

2.  Effects of Personalized Risk Information on Patients Referred for Lung Cancer Screening with Low-Dose CT.

Authors:  Paul K J Han; Christine Lary; Adam Black; Caitlin Gutheil; Hayley Mandeville; Jason Yahwak; Mayuko Fukunaga
Journal:  Med Decis Making       Date:  2019-10-20       Impact factor: 2.583

Review 3.  Implementation planning for lung cancer screening in China.

Authors:  Yue I Cheng; Michael P A Davies; Dan Liu; Weimin Li; John K Field
Journal:  Precis Clin Med       Date:  2019-03-14

Review 4.  An update on CT screening for lung cancer: the first major targeted cancer screening programme.

Authors:  David R Baldwin; Matthew E J Callister
Journal:  Br J Radiol       Date:  2020-09-07       Impact factor: 3.039

5.  Evaluation of the Benefits and Harms of Lung Cancer Screening With Low-Dose Computed Tomography: Modeling Study for the US Preventive Services Task Force.

Authors:  Rafael Meza; Jihyoun Jeon; Iakovos Toumazis; Kevin Ten Haaf; Pianpian Cao; Mehrad Bastani; Summer S Han; Erik F Blom; Daniel E Jonas; Eric J Feuer; Sylvia K Plevritis; Harry J de Koning; Chung Yin Kong
Journal:  JAMA       Date:  2021-03-09       Impact factor: 157.335

Review 6.  Recommendations for Implementing Lung Cancer Screening with Low-Dose Computed Tomography in Europe.

Authors:  Giulia Veronesi; David R Baldwin; Claudia I Henschke; Simone Ghislandi; Sergio Iavicoli; Matthijs Oudkerk; Harry J De Koning; Joseph Shemesh; John K Field; Javier J Zulueta; Denis Horgan; Lucia Fiestas Navarrete; Maurizio Valentino Infante; Pierluigi Novellis; Rachael L Murray; Nir Peled; Cristiano Rampinelli; Gaetano Rocco; Witold Rzyman; Giorgio Vittorio Scagliotti; Martin C Tammemagi; Luca Bertolaccini; Natthaya Triphuridet; Rowena Yip; Alexia Rossi; Suresh Senan; Giuseppe Ferrante; Kate Brain; Carlijn van der Aalst; Lorenzo Bonomo; Dario Consonni; Jan P Van Meerbeeck; Patrick Maisonneuve; Silvia Novello; Anand Devaraj; Zaigham Saghir; Giuseppe Pelosi
Journal:  Cancers (Basel)       Date:  2020-06-24       Impact factor: 6.639

7.  Targeting lung cancer screening to individuals at greatest risk: the role of genetic factors.

Authors:  Mikey B Lebrett; Emma J Crosbie; Miriam J Smith; Emma R Woodward; D Gareth Evans; Philip A J Crosbie
Journal:  J Med Genet       Date:  2021-01-29       Impact factor: 6.318

8.  Deep Learning Using Chest Radiographs to Identify High-Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model.

Authors:  Michael T Lu; Vineet K Raghu; Thomas Mayrhofer; Hugo J W L Aerts; Udo Hoffmann
Journal:  Ann Intern Med       Date:  2020-09-01       Impact factor: 51.598

9.  A model based on the quantification of complement C4c, CYFRA 21-1 and CRP exhibits high specificity for the early diagnosis of lung cancer.

Authors:  Daniel Ajona; Ana Remirez; Cristina Sainz; Cristina Bertolo; Alvaro Gonzalez; Nerea Varo; María D Lozano; Javier J Zulueta; Miguel Mesa-Guzman; Ana C Martin; Rosa Perez-Palacios; Jose Luis Perez-Gracia; Pierre P Massion; Luis M Montuenga; Ruben Pio
Journal:  Transl Res       Date:  2021-02-19       Impact factor: 7.012

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.