Evaluation of compartmentalized automatic segmentation for definition of the GTV in glioblastoma radiotherapy
The Neosoma Glioma model generates clinically useful postoperative GTV segmentations, with geometric performance comparable to expert variability and dosimetric equivalence to consensus contours. It reduces contouring time by over 50%, enabling faster RT workflows. Its consistency across diverse GBM presentations supports its practical value. AI-based segmentation can help standardize GBM target definition when integrated into RT planning with proper quality assurance.
Authors
- Robert Poel (ORCID: https://orcid.org/0000-0002-9817-4935)
- Lucas Mose (ORCID: https://orcid.org/0009-0006-0693-2716)
- Philipp Reinhardt (ORCID: https://orcid.org/0000-0003-3951-9275)
- Peter Manser (ORCID: https://orcid.org/0000-0003-3225-8025)
- Ekin Ermiş (ORCID: https://orcid.org/0000-0002-6974-6762)
- Daniel M. Aebersold (ORCID: https://orcid.org/0000-0002-9493-3834)
- Michael Müller (ORCID: https://orcid.org/0000-0002-5930-9905)
- Mauricio Reyes
- Sarah Brueningk
- Silvan Meuller
Institutions
- University of Bern (CH)
- Heidelberg University (DE)
- University Hospital of Bern (CH)
- University Hospital Heidelberg (DE)
Publication Details
- Journal
- Radiotherapy and Oncology
- Published
- 2025-11-26
- DOI
- https://doi.org/10.1016/j.radonc.2025.111308
- Primary Topic
- Glioma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00