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
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.
Authors
- Beibei Jiang (ORCID: https://orcid.org/0000-0003-3182-5032)
- Harriet L Lancaster (ORCID: https://orcid.org/0000-0003-3025-9745)
- John K. Field (ORCID: https://orcid.org/0000-0003-3951-6365)
- Jaeyoun Yi (ORCID: https://orcid.org/0000-0002-7664-9493)
- Daiwei Han (ORCID: https://orcid.org/0000-0003-0225-5245)
- Marjolein A. Heuvelmans (ORCID: https://orcid.org/0000-0002-5712-4085)
- Michael P.A. Davies (ORCID: https://orcid.org/0000-0002-7609-4977)
- Matthijs Oudkerk (ORCID: https://orcid.org/0000-0003-2800-4110)
- Mario Silva (ORCID: https://orcid.org/0000-0002-2538-7032)
- Anand Devaraj
- Carlijn M. van der Aalst
- Jan‐Willem Gratama
Institutions
- University of Parma (IT)
- University Medical Center Groningen (NL)
- University of Liverpool (GB)
- University of Groningen (NL)
- Gelre Hospitals (NL)
- Erasmus MC (NL)
- Royal Brompton Hospital (GB)
Publication Details
- Journal
- European Journal of Cancer
- Published
- 2025-11-22
- DOI
- https://doi.org/10.1016/j.ejca.2025.116137
- Citations
- 2
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 3.04
Funders
- Roy Castle Lung Cancer Foundation
- National Institute for Health and Care Research
- European Commission
- Rijksuniversiteit Groningen
- Erasmus Medisch Centrum
- China Scholarship Council
- Health Technology Assessment Programme