Current and Future Perspectives on Computed Tomography Screening for Lung Cancer: A Roadmap From 2023 to 2027 From the International Association for the Study of Lung Cancer.

Incidentally detected lung nodules Lung cancer LDCT screening Never-smokers Quality indicators

Journal

Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer
ISSN: 1556-1380
Titre abrégé: J Thorac Oncol
Pays: United States
ID NLM: 101274235

Informations de publication

Date de publication:
23 Jul 2023
Historique:
received: 05 04 2023
revised: 13 06 2023
accepted: 18 07 2023
pubmed: 25 7 2023
medline: 25 7 2023
entrez: 24 7 2023
Statut: aheadofprint

Résumé

Low-dose computed tomography (LDCT) screening for lung cancer substantially reduces mortality from lung cancer, as revealed in randomized controlled trials and meta-analyses. This review is based on the ninth CT screening symposium of the International Association for the Study of Lung Cancer, which focuses on the major themes pertinent to the successful global implementation of LDCT screening and develops a strategy to further the implementation of lung cancer screening globally. These recommendations provide a 5-year roadmap to advance the implementation of LDCT screening globally, including the following: (1) establish universal screening program quality indicators; (2) establish evidence-based criteria to identify individuals who have never smoked but are at high-risk of developing lung cancer; (3) develop recommendations for incidentally detected lung nodule tracking and management protocols to complement programmatic lung cancer screening; (4) Integrate artificial intelligence and biomarkers to increase the prediction of malignancy in suspicious CT screen-detected lesions; and (5) standardize high-quality performance artificial intelligence protocols that lead to substantial reductions in costs, resource utilization and radiologist reporting time; (6) personalize CT screening intervals on the basis of an individual's lung cancer risk; (7) develop evidence to support clinical management and cost-effectiveness of other identified abnormalities on a lung cancer screening CT; (8) develop publicly accessible, easy-to-use geospatial tools to plan and monitor equitable access to screening services; and (9) establish a global shared education resource for lung cancer screening CT to ensure high-quality reading and reporting.

Identifiants

pubmed: 37487906
pii: S1556-0864(23)00687-1
doi: 10.1016/j.jtho.2023.07.019
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : NCI NIH HHS
ID : R01 CA262164
Pays : United States
Organisme : NCI NIH HHS
ID : R03 CA245979
Pays : United States
Organisme : NCI NIH HHS
ID : U19 CA203654
Pays : United States

Informations de copyright

Copyright © 2023 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

Auteurs

Stephen Lam (S)

Department of Integrative Oncology, British Columbia Cancer Research Institute, Vancouver, British Columbia, Canada; Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada. Electronic address: slam2@bccancer.bc.ca.

Chunxue Bai (C)

Shanghai Respiratory Research Institute and Chinese Alliance Against Cancer, Shanghai, People's Republic of China.

David R Baldwin (DR)

Nottingham University Hospitals National Health Services (NHS) Trust, Nottingham, United Kingdom.

Yan Chen (Y)

Digital Screening, Faculty of Medicine & Health Sciences, University of Nottingham Medical School, Nottingham, United Kingdom.

Casey Connolly (C)

International Association for the Study of Lung Cancer, Denver, Colorado.

Harry de Koning (H)

Department of Public Health, Erasmus MC University Medical Centre Rotterdam, The Netherlands.

Marjolein A Heuvelmans (MA)

University of Groningen, Groningen, The Netherlands; Department of Epidemiology, University Medical Center Groningen, Groningen, The Netherlands; The Institute for Diagnostic Accuracy, Groningen, The Netherlands.

Ping Hu (P)

Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Bethesda, Maryland.

Ella A Kazerooni (EA)

Division of Cardiothoracic Radiology, Department of Radiology, University of Michigan Medical School, Ann Arbor, Michigan; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan.

Harriet L Lancaster (HL)

University of Groningen, Groningen, The Netherlands; Department of Epidemiology, University Medical Center Groningen, Groningen, The Netherlands; The Institute for Diagnostic Accuracy, Groningen, The Netherlands.

Georg Langs (G)

Computational Imaging Research Laboratory, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.

Annette McWilliams (A)

Department of Respiratory Medicine, Fiona Stanley Hospital, Murdoch, Western Australia, Australia; Australia University of Western Australia, Nedlands, Western Australia.

Raymond U Osarogiagbon (RU)

Thoracic Oncology Research Group, Baptist Cancer Center, Memphis, Tennessee.

Matthijs Oudkerk (M)

Center for Medical Imaging and The Institute for Diagnostic Accuracy, Faculty of Medical Sciences, University of Groningen, Groningen, The Netherlands.

Matthew Peters (M)

Woolcock Institute of Respiratory Medicine, Macquarie University, Sydney, New South Wales, Australia.

Hilary A Robbins (HA)

Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France.

Liora Sahar (L)

Data Science, American Cancer Society, Atlanta, Georgia.

Robert A Smith (RA)

Early Cancer Detection Science, American Cancer Society, Atlanta, Georgia.

Natthaya Triphuridet (N)

Department of Medicine, Chulabhorn Hospital, Bangkok, Thailand.

John Field (J)

Department of Molecular and Clinical Cancer Medicine, The University of Liverpool, Liverpool, United Kingdom.

Classifications MeSH