Lam, Stephen, Bai, Chunxue, Baldwin, David, Chen, Yan, Connolly, Casey, de Koning, Harry, Heuvelmans, Marjolein A, Hu, Ping, Kazerooni, Ella A, Lancaster, Harriet L et al (show 10 more authors)
(2023)
Current and Future Perspectives on CT Screening for Lung Cancer: A Road Map for 2023-2027 from the IASLC.
Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer, 19 (1).
S1556-0864(23)00687-1-S1556-0864(23)00687-1.
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Abstract
Low-dose computed tomography (LDCT) screening for lung cancer significantly reduces mortality from lung cancer, as demonstrated 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 (IASLC) which focuses on the major themes pertinent to the successful global implementation of LDCT screening and developed a strategy to further the implementation of lung cancer screening globally. These recommendations provide a 5-year road map to advance the implementation of LDCT screening globally including: (i) establishing universal screening program quality indicators; (ii) establishing evidence-based criteria to identify individuals who have never smoked but are at high risk of developing lung cancer; (iii) develop recommendations for incidentally detected lung nodule tracking and management protocols to complement programmatic lung cancer screening; (iv) Integrate artificial intelligence (AI) and biomarkers to increase the prediction of malignancy in suspicious CT screen-detected lesions; and (v) standardize high-quality performance AI protocols that lead to substantial reductions in costs, resource utilization and radiologist reporting time; (vi) personalize CT screening intervals based on an individual's lung cancer risk; (vii) develop evidence to support clinical management and cost-effectiveness of other identified abnormalities on a lung cancer screening CT; (viii) develop publicly accessible, easy-to-use, geospatial tools to plan and monitor equitable access to screening services; and (ix) establish a global shared education resource for lung cancer screening CT to ensure high quality reading and reporting.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Lung, Humans, Lung Neoplasms, Tomography, X-Ray Computed, Mass Screening, Artificial Intelligence, Early Detection of Cancer |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 19 Sep 2023 09:42 |
| Last Modified: | 31 Jan 2024 19:50 |
| DOI: | 10.1016/j.jtho.2023.07.019 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3172792 |
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