Evidence
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%.
Targeted Lung Health Check Programme Final Evaluation Report
Ipsos
Mouland et al. Nov 2024
The TLHC programme (2019–2024) tested lung health checks in real-world settings to diagnose lung cancers earlier, following promising pilot results. Across more than 25 sites, 1.22 million invitations were issued, with 44% uptake. Of 163,000 CT scans, 2,748 cancers were diagnosed (1.7% conversion), around three-quarters at stage 1–2. Impact analysis showed an additional 781 early-stage cancers diagnosed in pilot areas, replicating small-scale outcomes and demonstrating real-world effectiveness while identifying demographic and operational challenges.
Performance of AI for Preoperative CT Assessment of Lung Metastases: Retrospective Analysis of 167 Patients
European Journal of Radiology
Masci et al. Oct 2024
The performance of artificial intelligence (AI) in the preoperative detection of lung metastases on CT was evaluated. Patients who underwent lung metastasectomy were enrolled. Their preoperative CT scans were retrospectively processed by AI. The results indicated that AI significantly increases the sensitivity of preoperative detection of lung metastases and enables earlier detection, with a significant potential benefit for patient management.
Early experiences of integrating an artificial intelligence-based diagnostic decision support system into radiology settings: a qualitative study
Nuša Farič, PhD, Sue Hinder, PhD, Robin Williams, PhD, Rishi Ramaesh, MD, Miguel O Bernabeu, PhD, Edwin van Beek, PhD, Kathrin Cresswell, PhD
“It just works”: The smooth deployment of the lung solution at Portsmouth NHS
Mark Gardner
When it comes to AI adoption in radiology, we often focus on the experience of the clinicians. Yet seamless use of AI wouldn’t be possible without workflow integration, made possible in collaboration with the hospitals’ IT teams.
Lung cancer screening rolled out nationwide in the UK
"I’m doing twice as much CT reporting": The impact of the lung solution at Salisbury NHS
Higher Agreement Between Readers with Deep Learning CAD Software for Reporting Pulmonary Nodules on CT
European Journal of Radiology Open
Hempel et al. Aug 2022
This retrospective study evaluated the impact of CAD software on the pulmonary nodule management recommendations of radiologists in a cohort of patients with incidentally detected nodules on CT. Two radiologists independently assessed 50 chest CT cases twice, unaided and aided by CAD. Readers showed a better agreement with the aid of CAD with and a significant reduction of reading time, suggesting that a dedicated CAD system for aiding in pulmonary nodule reporting may help improve the uniformity of management recommendations.
Validation of a Deep Learning Computer Aided System for CT Based Lung Nodule Detection, Classification, and Growth Rate Estimation in a Routine Clinical Population
PLoS One
Murchison et al. May 2022
The deep learning algorithm of a commercially available computer assisted diagnosis system (CAD) was trained with a lung cancer screening cohort and developed for detection, classification, quantification, and growth of actionable pulmonary nodules on chest CT scans. In a retrospective cohort of a routine clinical population, the applied CAD significantly increased radiologist’s detection of actionable nodules while minimally increasing the false positive rate, suggesting it can assist in pulmonary nodule detection and management.
Validation, Comparison, and Combination of Algorithms for Automatic Detection of Pulmonary Nodules in Computed Tomography Images: The LUNA16 Challenge
Medical Image Analysis
Setio et al. Dec 2017
The LUNA16 challenge is an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans. Participants developed algorithms focused on 1) the complete nodule detection track or 2) the false positive reduction track. The impact of combined systems was also investigated. The combination of these solutions achieved a sensitivity of over 95% at fewer than 1.0 false positives per scan, highlighting the potential to improve detection performance.