Feasibility of AI as first reader in the 4-IN-THE-LUNG-RUN lung cancer screening trial: impact on negative-misclassifications and clinical referral rate
This study showed AI outperforms radiologists with significantly less NMs and therefore shows promise as first reader in a LCS program at baseline, by independently ruling-out negative cases without substantially increasing the risk of missed clinical referrals.
Authors
- Harriet L Lancaster (ORCID: https://orcid.org/0000-0003-3025-9745)
- Sytse F. Oudkerk
- Daiwei Han (ORCID: https://orcid.org/0000-0003-0225-5245)
- Marjolein A. Heuvelmans (ORCID: https://orcid.org/0000-0002-5712-4085)
- Dana Moldovanu (ORCID: https://orcid.org/0000-0003-3211-0856)
- Jan W. Gratama (ORCID: https://orcid.org/0000-0001-7483-4997)
- Harry J. de Koning (ORCID: https://orcid.org/0000-0003-4682-3646)
- Matthijs Oudkerk (ORCID: https://orcid.org/0000-0003-2800-4110)
- J Hubert
- Mario Silva (ORCID: https://orcid.org/0000-0002-2538-7032)
- A. Walstra
- Carlijn M. van der Aalst
Institutions
- University of Parma (IT)
- University Medical Center Groningen (NL)
- Gelre Hospitals (NL)
- Erasmus MC (NL)
- HumanTotalCare (Netherlands) (NL)
Publication Details
- Journal
- European Journal of Cancer
- Published
- 2024-12-28
- DOI
- https://doi.org/10.1016/j.ejca.2024.115214
- Citations
- 11
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 4.61
Funders
- European Commission
- Horizon 2020 Framework Programme
- H2020 Societal Challenges