Predicting aggressive disease and poor outcome in endometrial cancer using preoperative [18F]FDG PET primary tumor radiomics
R<sub>dss</sub> predicts short-term survival with high accuracy, outperforming tumor SUV<sub>max/mean</sub>, but not MTV, TLG and LN<sub>PET</sub>. The combined C<sub>dss</sub> model yields high accuracy for predicting both short- and long-term survival, outperforming all conventional PET imaging markers.
Authors
- Camilla Krakstad (ORCID: https://orcid.org/0000-0002-0174-8139)
- Ingfrid S. Haldorsen (ORCID: https://orcid.org/0000-0001-9313-7564)
- Ankush Gulati
- Kristine E. Fasmer (ORCID: https://orcid.org/0000-0001-8773-7870)
- Sunniva Lindås
Institutions
- Haukeland University Hospital (NO)
- University of Bergen (NO)
Publication Details
- Journal
- European Journal of Nuclear Medicine and Molecular Imaging
- Published
- 2025-06-11
- DOI
- https://doi.org/10.1007/s00259-025-07335-7
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Trond Mohn stiftelse
- Helse Vest Regionalt Helseføretak