Literature DB >> 28929865

False-positive screens and lung cancer risk in the National Lung Screening Trial: Implications for shared decision-making.

Paul F Pinsky1, Christina R Bellinger2, David P Miller2.   

Abstract

Objectives Low-dose computed tomography lung cancer screening has been shown to reduce lung cancer mortality but has a high false-positive rate. The precision medicine approach to low-dose computed tomography screening assesses subjects' benefits versus harms based on their personal lung cancer risk, where harms include false-positive screens and resultant invasive procedures. We assess the relationship between lung cancer risk and the rate of false-positive LDCT screens. Methods The National Lung Screening Trial randomized high-risk subjects to three annual screens with low-dose computed tomography or chest radiographs. Following the completion of National Lung Screening Trial, the Lung CT Screening Reporting and Data System (Lung-RADS) classification system was developed and retrospectively applied to National Lung Screening Trial low-dose computed tomography findings. The rate of false-positive screens (by Lung-RADS) and the resultant invasive procedures were examined as a function of lung cancer risk estimated by a model. Results Of 26,722 subjects randomized to the low-dose computed tomography arm, 26,309 received a baseline screen and were included in the analysis. The proportion with any false positive over three screening rounds increased from 12.9% to 25.9% from lowest to highest risk decile, and the proportion with an invasive procedure following a false positive also significantly increased from 0.7% to 2.0% from lowest to highest risk decile. Conclusion These findings indicate a need for personalized low-dose computed tomography lung cancer screening decision aids to accurately convey the benefits to harm trade-off.

Entities:  

Keywords:  Lung cancer; false-positive tests; low-dose computed tomography; screening; shared decision-making

Mesh:

Year:  2017        PMID: 28929865     DOI: 10.1177/0969141317727771

Source DB:  PubMed          Journal:  J Med Screen        ISSN: 0969-1413            Impact factor:   2.136


  20 in total

1.  Lung Cancer Screening Benefits and Harms Stratified by Patient Risk: Information to Improve Patient Decision Aids.

Authors:  Christina Bellinger; Paul Pinsky; Kristie Foley; Douglas Case; Ajay Dharod; David Miller
Journal:  Ann Am Thorac Soc       Date:  2019-04

2.  "Commentary on: Lung cancer screening with MRI: results of the first screening round"-Michael Meier-Schroers et al.

Authors:  Pei Ing Ngam; Joanna Zhi Jie Ling
Journal:  J Cancer Res Clin Oncol       Date:  2018-05-10       Impact factor: 4.553

Review 3.  Multilevel Opportunities to Address Lung Cancer Stigma across the Cancer Control Continuum.

Authors:  Heidi A Hamann; Elizabeth S Ver Hoeve; Lisa Carter-Harris; Jamie L Studts; Jamie S Ostroff
Journal:  J Thorac Oncol       Date:  2018-05-23       Impact factor: 15.609

Review 4.  Lung Cancer Screening with Low-Dose CT: a Meta-Analysis.

Authors:  Richard M Hoffman; Rami P Atallah; Roger D Struble; Robert G Badgett
Journal:  J Gen Intern Med       Date:  2020-06-24       Impact factor: 5.128

5.  Outcomes of Shared Decision-Making for Low-Dose Screening for Lung Cancer in an Academic Medical Center.

Authors:  Jan M Eberth; Anja Zgodic; Scott C Pelland; Stephanie Y Wang; David P Miller
Journal:  J Cancer Educ       Date:  2022-04-30       Impact factor: 2.037

Review 6.  Impact of low-dose computed tomography (LDCT) screening on lung cancer-related mortality.

Authors:  Asha Bonney; Reem Malouf; Corynne Marchal; David Manners; Kwun M Fong; Henry M Marshall; Louis B Irving; Renée Manser
Journal:  Cochrane Database Syst Rev       Date:  2022-08-03

7.  Attitudes of Clinicians about Screening Head and Neck Cancer Survivors for Lung Cancer Using Low-Dose Computed Tomography.

Authors:  Kimberly Dukes; Aaron T Seaman; Richard M Hoffman; Alan J Christensen; Nicholas Kendell; Andrew L Sussman; Miriam Vélez-Bermúdez; Robert J Volk; Nitin A Pagedar
Journal:  Ann Otol Rhinol Laryngol       Date:  2019-08-13       Impact factor: 1.547

8.  Evaluation of a novel deep learning-based classifier for perifissural nodules.

Authors:  Daiwei Han; Marjolein Heuvelmans; Mieneke Rook; Monique Dorrius; Luutsen van Houten; Noah Waterfield Price; Lyndsey C Pickup; Petr Novotny; Matthijs Oudkerk; Jerome Declerck; Fergus Gleeson; Peter van Ooijen; Rozemarijn Vliegenthart
Journal:  Eur Radiol       Date:  2020-12-02       Impact factor: 5.315

9.  Classification of lung nodules in CT scans using three-dimensional deep convolutional neural networks with a checkpoint ensemble method.

Authors:  Hwejin Jung; Bumsoo Kim; Inyeop Lee; Junhyun Lee; Jaewoo Kang
Journal:  BMC Med Imaging       Date:  2018-12-03       Impact factor: 1.930

10.  Circulating ensembles of tumor-associated cells: A redoubtable new systemic hallmark of cancer.

Authors:  Dadasaheb Akolkar; Darshana Patil; Timothy Crook; Sewanti Limaye; Raymond Page; Vineet Datta; Revati Patil; Cynthe Sims; Anantbhushan Ranade; Pradeep Fulmali; Pooja Fulmali; Navin Srivastava; Pradip Devhare; Sachin Apurwa; Shoeb Patel; Sanket Patil; Archana Adhav; Sushant Pawar; Akshay Ainwale; Rohit Chougule; Madhavi Apastamb; Ajay Srinivasan; Rajan Datar
Journal:  Int J Cancer       Date:  2019-12-16       Impact factor: 7.396

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