Jiang, B 
ORCID: 0000-0003-3182-5032, Lancaster, HL, Davies, MPA ORCID: 0000-0002-7609-4977, Gratama, JWC, Silva, M ORCID: 0000-0002-2538-7032, Han, D, Yi, J ORCID: 0000-0002-7664-9493, van der Aalst, CM, Devaraj, A, Heuvelmans, MA et al (show 2 more authors)
(2026)
AI performance for nodule volume doubling time in the follow-up of the UKLS lung cancer screening study compared to expert consensus and histological validation
European Journal of Cancer, 232.
116137-.
ISSN 0959-8049, 1879-0852
Abstract
Aim To validate an artificial intelligence (AI) software for automated assessment of nodule growth by volume doubling time measurement (VDT) on protocol-mandated follow-up low-dose CT (LDCT) scans from the UK lung cancer screening (UKLS) trial. Methods This validation study included 710 UKLS participants with 939 LDCT follow-up scans (361 3-month and 578 12-month). Follow-up scans were assessed independently by both AI and human readers. A positive finding warranting referral was defined as the largest nodule with a solid component ≥ 100 mm<sup>3</sup> showing VDT ≤ 400 days at follow-up. Performance was benchmarked against the European expert panel (reference standard) and then the histological outcomes (gold standard). Results Against the expert panel, AI achieved the lowest 3-month negative misclassification (NM) rate (1/11, 9.1 %), versus human readers (range: 18.2–63.6 %). AI’s positive misclassification (PM) rate was initially 7.8 % (28/361) at 3 months but decreased to 0.9 % (5/578) at 12 months. Against histological outcomes of 9 screen-detected lung cancers, AI identified VDT ≤ 400 days in all 4 cancers also deemed positive by the expert panel at the earliest 3-month follow-up, while human readers missed or delayed referrals in 1–3 of these. AI also identified VDT ≤ 400 days in 3 of 5 cancers that the panel classified as negative, primarily due to their sub-threshold volume (<100mm³). Conclusions The automated AI system showed strong VDT assessment performance in follow-up screening, outperforming human readers in the early identification of rapid growth in histologically-confirmed cancers, thus supporting its potential to enhance risk stratification and facilitate earlier lung cancer detection.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Artificial Intelligence, Lung cancer screening, Pulmonary nodule, VDT, Growth-rate, Screening follow-up, Low-Dose CT, Computed tomography, Comparative study |
| 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 > Inst. Systems, Molec & Integrative Biology (T&R Staff) Faculty of Health & Life Sciences > Inst. Systems, Molec & Integrative Biology > Molecular & Clinical Cancer Medicine |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 15 Dec 2025 16:21 |
| Last Modified: | 28 Feb 2026 15:59 |
| DOI: | 10.1016/j.ejca.2025.116137 |
| Open Access URL: | https://doi.org/10.1016/j.ejca.2025.116137 |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3196139 |
| 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. |
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