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Article History

Received: 19 December 2024

Revised: 2 May 2025

Accepted: 17 June 2025

First Online: 15 July 2025

Compliance with ethical standards

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: The scientific guarantor of this publication is Colin Jacobs.

: The authors of this manuscript declare relationships with the following companies: Cornelia Schaefer-Prokop received speaker fees from Canon and Boehringer and royalties from Thieme and Elsevier unrelated to nodule research; Colin Jacobs receives research grants and royalties to host institution from MeVis Medical Solutions, Bremen, Germany; and payment for lectures from Canon Medical Systems and Johnson & Johnson. Colin Jacobs is a collaborator in a public-private research project where Radboudumc collaborates with Philips Medical Systems (Best, the Netherlands). He is a member of the Scientific Editorial Board of European Radiology (section: Imaging Informatics and Artificial Intelligence); as such, they have not participated in the selection or review processes for this article. He reports no other relationships that are related to the subject matter of the article. The other authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

: No complex statistical methods were necessary for this paper.

: Written informed consent was waived by the Institutional Review Board due to the retrospective design and data pseudonymisation.

: Institutional Review Board approval was obtained.

: Some study subjects have been previously reported. A preliminary analysis using a subset of benign nodules from this study was used in a conference abstract at ECR 2024. The current study included more annotations and a more comprehensive analysis. Dinnessen et al (2024). External validation of an AI algorithm for pulmonary nodule malignancy risk estimation on a dataset of incidentally detected pulmonary nodules. RPS 2104-5, p247; ECR 2024 Book of Abstracts. Insights Imaging 15:223. .

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