PT  - JOURNAL ARTICLE
AU  - Geppert, Julia
AU  - Asgharzadeh, Asra
AU  - Brown, Anna
AU  - Stinton, Chris
AU  - Helm, Emma J
AU  - Jayakody, Surangi
AU  - Todkill, Daniel
AU  - Gallacher, Daniel
AU  - Ghiasvand, Hesam
AU  - Patel, Mubarak
AU  - Auguste, Peter
AU  - Tsertsvadze, Alexander
AU  - Chen, Yen-Fu
AU  - Grove, Amy
AU  - Shinkins, Bethany
AU  - Clarke, Aileen
AU  - Taylor-Phillips, Sian
TI  - Software using artificial intelligence for nodule and cancer detection in CT lung cancer screening: systematic review of test accuracy studies
AID  - 10.1136/thorax-2024-221662
DP  - 2024 Nov 01
TA  - Thorax
PG  - 1040--1049
VI  - 79
IP  - 11
4099  - http://thorax.bmj.com/content/79/11/1040.short
4100  - http://thorax.bmj.com/content/79/11/1040.full
SO  - Thorax2024 Nov 01; 79
AB  - Objectives To examine the accuracy and impact of artificial intelligence (AI) software assistance in lung cancer screening using CT.Methods A systematic review of CE-marked, AI-based software for automated detection and analysis of nodules in CT lung cancer screening was conducted. Multiple databases including Medline, Embase and Cochrane CENTRAL were searched from 2012 to March 2023. Primary research reporting test accuracy or impact on reading time or clinical management was included. QUADAS-2 and QUADAS-C were used to assess risk of bias. We undertook narrative synthesis.Results Eleven studies evaluating six different AI-based software and reporting on 19 770 patients were eligible. All were at high risk of bias with multiple applicability concerns. Compared with unaided reading, AI-assisted reading was faster and generally improved sensitivity (+5% to +20% for detecting/categorising actionable nodules; +3% to +15% for detecting/categorising malignant nodules), with lower specificity (−7% to −3% for correctly detecting/categorising people without actionable nodules; −8% to −6% for correctly detecting/categorising people without malignant nodules). AI assistance tended to increase the proportion of nodules allocated to higher risk categories. Assuming 0.5% cancer prevalence, these results would translate into additional 150–750 cancers detected per million people attending screening but lead to an additional 59 700 to 79 600 people attending screening without cancer receiving unnecessary CT surveillance.Conclusions AI assistance in lung cancer screening may improve sensitivity but increases the number of false-positive results and unnecessary surveillance. Future research needs to increase the specificity of AI-assisted reading and minimise risk of bias and applicability concerns through improved study design.PROSPERO registration number CRD42021298449.All data relevant to the study are included in the article or uploaded as supplementary information.