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Comparative Study
. 2026 Jan:232:116137.
doi: 10.1016/j.ejca.2025.116137. Epub 2025 Nov 22.

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

Beibei Jiang  1 , Harriet L Lancaster  2 , Michael P A Davies  3 , Jan-Willem C Gratama  4 , Mario Silva  5 , Daiwei Han  6 , Jaeyoun Yi  7 , Carlijn M van der Aalst  8 , Anand Devaraj  9 , Marjolein A Heuvelmans  10 , John K Field  11 , Matthijs Oudkerk  12
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Free article
Comparative Study

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

Beibei Jiang et al. Eur J Cancer. 2026 Jan.
Free article

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 mm3 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.

Keywords: Artificial Intelligence; Comparative study; Computed tomography; Growth-rate; Low-Dose CT; Lung cancer screening; Pulmonary nodule; Screening follow-up; VDT.

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Conflict of interest statement

Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: M.P.A.D reports receiving reports from Roy Castle Lung Cancer Foundation, and NIH. J.F.K. reports receiving grants/contracts from EU Horizon, MRC-Saver, CRUK, IAA, Elypta, and Therapeutic Antibody Identification for Lung cancer and COPD, consulting fees from Elypta, Qure.ai, iDNA, and Astra Zeneca, and support for meeting attendance from IASLC. A.D. reports receiving consulting fees from Brainomix, Boehringer Ingelheim, and Astra Zeneca, and stock/stock options with Brainomix. M.S reports receiving support for a lecture at ESTI congress from Coreline LTD. M.O. and C.M.vdA. reports receiving support from EU Horizon grant as part of the ongoing 4ITLR screening implementation trial. J.Y is an employee of Coreline Soft, the developer of the AI software (AVIEW LCS) evaluated in this study. All other authors declare that they have no competing interests.

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