Evidence
Evaluation of AI Detection of Lung Nodules in Routine Chest CTs Compared to Lung Cancer Screening CTs
ECR: Poster Presentation
Wakelin et al. 2026
Despite lung cancer being a leading cause of mortality in Europe, eligibility requirements for screening are complex and region specific, limiting adoption rates. Artificial intelligence (AI) that identifies lung nodules in routine CT scans could help detect lung nodules that would otherwise be missed, but it is unclear if the same AI can be used for both screening and routine CTs given the different patient populations scanned. Here we characterize the performance of an AI algorithm to detect lung nodules within routine chest CT scans and compare to screening low dose CT (LDCT).
AI-assisted lung nodule detection on computed tomography: effects on diagnostic accuracy, consistency, and efficiency
ECR: Poster Presentation
Kim et al. 2026
Lung nodule detection and tracking on low-dose chest CT is time intensive and subject to substantial interobserver variability, impacting early-stage lung cancer detection. This study aimed to determine whether AI-assisted lung nodule detection improves radiologists’ diagnostic performance, consistency, and efficiency across nodule sizes.
206 Screening Benefits and Harms; A Review of False Positives and Negatives from the Somerset, Wiltshire, Avon and Gloucestershire (SWAG) Targeted Lung Health Check (TLHC) Programme
BTOG: Poster Presentation
Palmer et al. 2025
Clinical outcomes from the SWAG TLHC programme were reviewed to better understand potential harms, including over-investigation, missed diagnoses and cancers developing between screening rounds. Of the scans reviewed, 4098/4198 (97.6%) were negative (confirmed true negatives) and 80/4198 (1.9%) were abnormal (confirmed true positives). One person was diagnosed with lung cancer 18 months after a reportedly normal TLC scan, giving a false negative rate of 0.02%.