Jiang, B, Han, D, Cai, J, Lancaster, HL, Davies, MPA 
ORCID: 0000-0002-7609-4977, Walstra, ANH, Gratama, JWC, Silva, M, Yi, J, van der Aalst, CM et al (show 3 more authors)
(2026)
Negligible impact of perifissural nodules in an AI-first reader workflow from UK lung screening trial
European Radiology, 36 (8).
pp. 6209-6217.
ISSN 0938-7994, 1432-1084
|
Text
Jiang Eur Radiol 2026 Negligible impact of perifissural nodules in an AI-first reader workflow from UK lung screening trial.pdf - Author Accepted Manuscript Available under License Creative Commons Attribution. Download (438kB) | Preview |
Abstract
Objective: To evaluate the effect of perifissural nodules (PFNs) on radiologist workload within an AI-first reader workflow for lung cancer screening, given that AI cannot morphologically classify benign PFNs measuring ≥ 100 mm3. Materials and methods: One thousand two hundred fifty baseline low-dose CT scans from the UK Lung Screening (UKLS) Trial were analyzed. A commercially available AI software automatically identified all nodules with solid components ≥ 100 mm³ per the NELSON 2.0-European Position Statement (EUPS) guideline. Three readers independently performed PFN classification, with a senior radiologist with over 20 years of experience performing an arbitration read for the final reference classification (typical PFN, atypical PFN, or non-PFN). Histological outcomes for all fissure-attached nodules were reviewed to confirm benignity. The proportion of participants where a benign typical PFN was the sole finding of nodule presence ≥ 100 mm³ was calculated, representing the extra workload for radiologists to review. Results: A total of 1252 participants (mean age, 68.5 ± 4.0 years; 928 men [74%]) were analyzed. AI detected 838 nodules with solid components ≥ 100 mm³ in 431 (34%) participants. 57 nodules in 49 (3.9%) participants were classified as typical PFNs by the reference standard. Only 24 of 1252 participants (1.9%) had a typical PFN ≥ 100 mm³ as the sole finding that added extra workload. No typical PFNs (0/57) were malignant. Conclusion: The impact of typical PFNs on the maximum achievable radiologist workload reduction in an AI-first reader workflow is negligible, with only 1.9% of participants requiring additional radiologist review triggered solely by these benign nodules. Key Points: Question In an AI-first lung cancer screening workflow, do typical PFNs ≥ 100 mm3 create a significant bottleneck for radiologist workload? Findings In the UKLS trial, typical PFNs ≥ 100 mm³ were rare, creating negligible extra workload (1.9% of participants), and none were malignant (0/57). Clinical relevance The concern that PFN morphology creates a bottleneck in AI-first screening workflows is unfounded. Our findings support the feasibility of volume-based AI triage, allowing radiologists to focus on other false positives without being overwhelmed by PFNs.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Humans, Lung Neoplasms, Radiographic Image Interpretation, Computer-Assisted, Tomography, X-Ray Computed, Artificial Intelligence, Aged, Workload, Female, Male, Solitary Pulmonary Nodule, Early Detection of Cancer, Workflow, United Kingdom, Intelligent Systems |
| Divisions: | Faculty of Health & Life Sciences Faculty of Health & Life Sciences > Inst. Systems, Molec & Integrative Biology Faculty of Health & Life Sciences > Inst. Systems, Molec & Integrative Biology > Molecular & Clinical Cancer Medicine |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 02 Apr 2026 15:34 |
| Last Modified: | 01 Aug 2026 01:21 |
| DOI: | 10.1007/s00330-026-12444-4 |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3197739 |
| Disclaimer: | The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate. |
Altmetric
Altmetric