Potential to improve early detection of lung cancer through expansion of focused and opportunistic screening approaches: a scoping review focusing on potential expansion of screening risk factors and adoption of artificial intelligence models.
8 affiliations
- 1Department of Clinical Oncology, The Chinese University of Hong Kong, Hong Kong, China.
- 2Department of Surgery (Division of Thoracic Surgery), McMaster University, Hamilton, Canada.
- 3Department of Chest Diseases, Faculty of Medicine, Hacettepe Universitesi, Ankara, Turkey.
- 4Department of Medicine, NYU Langone Health Perlmutter Cancer Center, New York City, NY, USA.
- 5Department of Surgery (Division of Thoracic Surgery), Kaiser Permanente Northern California, Oakland, CA, USA.
- 6Department of Surgery, University of California San Francisco Medical School of Medicine, San Francisco, USA.
- 7Department of Clinical Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, USA.
- 8Department of Internal Medicine, National Taiwan University, Taipei, Taiwan.
Abstract
Background
Low-dose computerized tomography (LDCT) is the gold standard for lung cancer (LC) screening, but access is hindered by strict eligibility criteria and limited infrastructure in low/middle-income countries. A combination of approaches, including expanded screening eligibility criteria and artificial intelligence (AI), could improve detection of early LC in patients excluded by current screening criteria.Areas covered
A scoping review was conducted (PubMed, Cochrane library, congresses) to explore two potentially complementary approaches to enhance LC screening. The first identified LC risk factors and their potential inclusion in expanded screening criteria, allowing more people at risk to become eligible for screening; the second examined studies involving AI-augmented imaging (CXR or LDCT) to improve workflow, triage, and opportunistic nodule detection.Expert opinion/commentary
Several robustly evidenced risk factors for LC were identified that are not included in current screening programs; widening of eligibility criteria could be considered based on local risk profile but requires further research to demonstrate benefit. Opportunistic use of AI-tools to assess incidental chest images may identify patients with pulmonary nodules requiring further investigation but their role in population-level LC screening requires prospective validation in diverse healthcare systems.
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BibTeX
@article{mok2026potential,
title = {{Potential to improve early detection of lung cancer through expansion of focused and opportunistic screening approaches: a scoping review focusing on potential expansion of screening risk factors and adoption of artificial intelligence models.}},
author = {Tony S K Mok and Christian Finley and Deniz Köksal and Molly S C Li and Elaine Shum and Jeffrey B Velotta and Pan-Chyr Yang},
journal = {Lung cancer management},
year = {2026},
month = oct,
doi = {10.1080/17581966.2026.2743569},
url = {https://europepmc.org/article/MED/42864862},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Potential to improve early detection of lung cancer through expansion of focused and opportunistic screening approaches: a scoping review focusing on potential expansion of screening risk factors and adoption of artificial intelligence models.
AU - Tony S K Mok
AU - Christian Finley
AU - Deniz Köksal
AU - Molly S C Li
AU - Elaine Shum
AU - Jeffrey B Velotta
AU - Pan-Chyr Yang
PY - 2026
DA - 2026/10/12
JO - Lung cancer management
DO - 10.1080/17581966.2026.2743569
UR - https://europepmc.org/article/MED/42864862
AB - Background Low-dose computerized tomography (LDCT) is the gold standard for lung cancer (LC) screening, but access is hindered by strict eligibility criteria and limited infrastructure in low/middle-income countries. A combination of approaches, including expanded screening eligibility criteria and artificial intelligence (AI), could improve detection of early LC in patients excluded by current screening criteria.Areas covered A scoping review was conducted (PubMed, Cochrane library, congresses) to explore two potentially complementary approaches to enhance LC screening. The first identified LC risk factors and their potential inclusion in expanded screening criteria, allowing more people at risk to become eligible for screening; the second examined studies involving AI-augmented imaging (CXR or LDCT) to improve workflow, triage, and opportunistic nodule detection.Expert opinion/commentary Several robustly evidenced risk factors for LC were identified that are not included in current screening programs; widening of eligibility criteria could be considered based on local risk profile but requires further research to demonstrate benefit. Opportunistic use of AI-tools to assess incidental chest images may identify patients with pulmonary nodules requiring further investigation but their role in population-level LC screening requires prospective validation in diverse healthcare systems.
SN - 1758-1966
SN - 1758-1974
ER -