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.
TL;DR
A scoping review was conducted to explore two potentially complementary approaches to enhance LC screening, including studies involving AI-augmented imaging (CXR or LDCT) to improve workflow, triage, and opportunistic nodule detection.
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.
