Impact of Field-of-view Zooming and Segmentation Batches on Radiomics Features Reproducibility and Machine Learning Performance in Thyroid Scintigraphy
Utilizing robust radiomic features significantly improved the performance of ML models in thyroid disease classification, enabling more accurate and generalizable diagnostic outcomes across diverse data sets.
Authors
- Habib Zaidi (ORCID: https://orcid.org/0000-0001-7559-5297)
- Arman Rahmim (ORCID: https://orcid.org/0000-0002-9980-2403)
- Habiballah Moradi (ORCID: https://orcid.org/0000-0002-8759-0562)
- Ghasem Hajianfar (ORCID: https://orcid.org/0000-0001-5359-2407)
- Omid Gharibi (ORCID: https://orcid.org/0000-0003-3403-9168)
- Akram Yazdani (ORCID: https://orcid.org/0000-0002-8353-1653)
- Atena Aghaee (ORCID: https://orcid.org/0000-0002-1351-3142)
- Soroush Bagheri (ORCID: https://orcid.org/0009-0007-6227-2166)
- Maziar Sabouri (ORCID: https://orcid.org/0000-0001-7525-8952)
- Ali Mohammad Nickfarjam (ORCID: https://orcid.org/0000-0003-3782-3038)
- Babak Yazdani
- Akbar Aliasgharzadeh
Institutions
- University Medical Center Groningen (NL)
- University of British Columbia (CA)
- Mashhad University of Medical Sciences (IR)
- University of Groningen (NL)
- University of Southern Denmark (DK)
- Shiraz University of Medical Sciences (IR)
- Kashan University of Medical Sciences (IR)
- Namazi Hospital (IR)
- University Hospital of Geneva (CH)
Publication Details
- Journal
- Clinical Nuclear Medicine
- Published
- 2025-06-17
- DOI
- https://doi.org/10.1097/rlu.0000000000005995
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung