Malignancy classification of thyroid incidentalomas using 18F-fluorodeoxy-d-glucose PET/computed tomography-derived radiomics
This study highlights the potential of 18 F-FDG PET/CT-derived radiomics to distinguish benign from malignant thyroid lesions. Further studies with larger cohorts and deep learning-based methods could obtain more robust results.
Authors
- Marina Herrero-Huertas
- Koen J. Hartemink (ORCID: https://orcid.org/0000-0003-4282-1096)
- Annemarieke Bartels‐Rutten (ORCID: https://orcid.org/0000-0001-6826-6065)
- Wouter V. Vogel (ORCID: https://orcid.org/0000-0002-4992-8024)
- Marceline W. Piek (ORCID: https://orcid.org/0000-0003-2216-1369)
- Mohamed A. Abdelatty (ORCID: https://orcid.org/0000-0003-2740-4657)
- Jan Paul de Boer (ORCID: https://orcid.org/0000-0001-6218-6128)
- Iris M. C. van der Ploeg (ORCID: https://orcid.org/0000-0003-2805-2104)
- Zuhir Bodalal (ORCID: https://orcid.org/0000-0002-2617-8128)
- Melda Yeghaian
- Geerard L. Beets (ORCID: https://orcid.org/0000-0002-1671-9912)
- Stefano Trebeschi (ORCID: https://orcid.org/0000-0002-5714-289X)
Institutions
- Cairo University (EG)
- University of Southern Denmark (DK)
- Maastricht University (NL)
- Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital (NL)
- Hospital Universitario Ramón y Cajal (ES)
Publication Details
- Journal
- Nuclear Medicine Communications
- Published
- 2025-07-24
- DOI
- https://doi.org/10.1097/mnm.0000000000002031
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00