Shape matters: unsupervised exploration of IDH-wildtype glioma imaging survival predictors
Shape radiomics and tumor volume clustering in IDH-wildtype gliomas are investigated for enhanced prognostic accuracy. Two distinct phenotypic clusters were identified with different median OSs. Integrating shape radiomics and volume-based clustering enhances OS prediction in IDH-wildtype glioma patients.
Authors
- Gianluca Brugnara (ORCID: https://orcid.org/0000-0003-2461-1407)
- Martin Bendszus (ORCID: https://orcid.org/0000-0002-9094-6769)
- Philipp Kickingereder (ORCID: https://orcid.org/0000-0002-6224-0064)
- Martha Foltyn‐Dumitru (ORCID: https://orcid.org/0009-0005-2925-9176)
- Marianne Schell (ORCID: https://orcid.org/0000-0002-9754-1350)
- Felix Sahm (ORCID: https://orcid.org/0000-0001-5441-1962)
- Mustafa Ahmed Mahmutoglu (ORCID: https://orcid.org/0000-0002-4891-5547)
- Sabine Heiland (ORCID: https://orcid.org/0000-0002-2796-3451)
- Wolfgang Wick (ORCID: https://orcid.org/0000-0002-6171-634X)
- Tobias Keßler (ORCID: https://orcid.org/0000-0001-8350-7074)
Institutions
- German Cancer Research Center (DE)
- Heidelberg University (DE)
- University Hospital Bonn (DE)
- University Hospital Heidelberg (DE)
Publication Details
- Journal
- European Radiology
- Published
- 2024-09-09
- DOI
- https://doi.org/10.1007/s00330-024-11042-6
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.21
Funders
- Deutsche Forschungsgemeinschaft