Evidence map›Paper›PMID 42432262›Full record

ArticleEuropean radiology2026

Deep learning-based malignancy probability estimation of pulmonary nodules in PET/CT imaging.

Lars Leijten, Erik H J G Aarntzen, Roel L J Verhoeven, Adrienne H Brouwers, Bram Geurts, Johannes A van der Heide, Klaas Pieter Koopmans, Walter Noordzij, Gilles N Stormezand, Erik H F M van der Heijden and 1 more

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Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

11 authors.

Lars LeijtenDepartment of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands. lars.leijten@radboudumc.nl.ORCID http://orcid.org/0009-0006-9396-0204
Erik H J G AarntzenDepartment of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
Roel L J VerhoevenDepartment of Pulmonary Diseases, Radboud University Medical Center, Nijmegen, The Netherlands.
Adrienne H BrouwersDepartment of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Bram GeurtsDepartment of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
Johannes A van der HeideDepartment of Radiology and Nuclear Medicine, Ziekenhuis Gelderse Vallei, Ede, The Netherlands.
Klaas Pieter KoopmansDepartment of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Walter NoordzijDepartment of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Gilles N StormezandDepartment of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Erik H F M van der HeijdenDepartment of Pulmonary Diseases, Radboud University Medical Center, Nijmegen, The Netherlands.
Colin JacobsDepartment of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.

Funding

Innovative Health Initiative 101112053
6 · The paper itself

Abstract

objectiveThe current BTS guidelines recommend evaluation of suspicious pulmonary nodules using [ MATERIALS AND

methodsIn a single-center retrospective study, we collected 533 indeterminate pulmonary nodules (268 malignant) with a mean diameter of 18.4 mm (SD ± 12.1) in 436 patients. Histopathological malignancy confirmation or a minimum 2-year benign national cancer registry follow-up served as the reference standard. Model diagnostic performance was compared against the Herder model and seven clinicians in a reader study on a test set of 161 nodules (80 malignant).

resultsAITO-PETCT-MP achieved an AUC of 0.78 [95% CI: 0.70-0.85] compared to the Herder model: AUC = 0.73 [0.65-0.80] (non-inferiority: p = 0.005). On average, experienced clinicians achieved an AUC of 0.80 [0.75-0.85]. Stratifying into BTS follow-up categories, the Herder model referred more benign nodules for potential direct treatment (26/81) than AITO-PETCT-MP and clinicians (both 3/81), while AITO-PETCT-MP and clinicians assigned more malignant cases to CT surveillance instead of direct treatment.

conclusionAITO-PETCT-MP demonstrated non-inferior performance to the guideline-recommended Herder model, while only using imaging data. Diagnostic performance fell in the performance range of seven clinicians. Differences in BTS follow-up recommendations between the Herder model and clinicians suggest a difference in patient management compared to current clinical practice. KEY POINTS: Question How well can an imaging-only deep learning model estimate pulmonary nodule malignancy probability on [

Indexed as

Deep learningLung neoplasmsMultiple pulmonary nodulesPositron emission tomography computed tomographySolitary pulmonary nodule

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PMID42432262

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