Quality requirements to establish a successful lung cancer screening program

Lung cancer is the leading cause of cancer-related deaths globally. There is a strong body of evidence from the last two decades to support the effectiveness of lung cancer screening (LCS) with low radiation dose computed tomography (LDCT) in reducing lung cancer mortality and all-cause mortality. N...

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Bibliographic Details
Main Authors: Amna Burzic, David R. Baldwin
Format: Article
Language:English
Published: Publicaciones Permanyer 2024-01-01
Series:Barcelona Respiratory Network Reviews
Subjects:
Online Access:https://www.brnreviews.com/frame_eng.php?id=243
Description
Summary:Lung cancer is the leading cause of cancer-related deaths globally. There is a strong body of evidence from the last two decades to support the effectiveness of lung cancer screening (LCS) with low radiation dose computed tomography (LDCT) in reducing lung cancer mortality and all-cause mortality. National programmes are approved and either ongoing or in planning in Poland, Croatia, the UK, Canada, Australia and the US. Other countries are proceeding with pilot programmes, some of which are framed as research studies. The European Commission’s Group of Chief Scientific Advisors recommended that lung cancer screening (LCS) be added to the other established cancer screening programs in Europe and the European Council recommended that LCS be implemented in a stepwise approach depending on national priorities. However, it is essential that clinical and cost-effectiveness shown in studies and pilot programmes are replicated in national programmes. This is achieved through the use of evidence-based strategies across each element of screening from participant selection to treatment. This review addresses key quality requirements identified in the literature which must form part of a successful LCS program. We describe the challenges and, where possible, suggest solutions for the implementation of screening. We will highlight the areas for further research including risk-prediction models for screening eligibility, optimising recruitment methods, personalisation of screening intervals, biomarkers, smoking cessation integration, and artificial intelligence.
ISSN:2462-3172