Evaluation of compartmentalized automatic segmentation for definition of the GTV in glioblastoma radiotherapy
Background and purpose - Manual delineation of target volumes in glioblastoma (GBM) radiotherapy (RT) is time-consuming and variable. This study evaluates the clinical applicability of a preliminary deep learning model (Neosoma Glioma) for automating gross tumor volume (GTV) segmentation in postoper...
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
January 2026
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| In: |
Radiotherapy and oncology
Year: 2026, Jahrgang: 214, Pages: 1-8 |
| ISSN: | 1879-0887 |
| DOI: | 10.1016/j.radonc.2025.111308 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.radonc.2025.111308 Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S0167814025053125 
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| Verfasserangaben: | Robert Poel, Lucas Mose, Philipp Reinhardt, Michael Müller, Silvan Meuller, Mauricio Reyes, Sarah Brueningk, Peter Manser, Daniel M. Aebersold, Ekin Ermiş |
MARC
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| 245 | 1 | 0 | |a Evaluation of compartmentalized automatic segmentation for definition of the GTV in glioblastoma radiotherapy |c Robert Poel, Lucas Mose, Philipp Reinhardt, Michael Müller, Silvan Meuller, Mauricio Reyes, Sarah Brueningk, Peter Manser, Daniel M. Aebersold, Ekin Ermiş |
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| 520 | |a Background and purpose - Manual delineation of target volumes in glioblastoma (GBM) radiotherapy (RT) is time-consuming and variable. This study evaluates the clinical applicability of a preliminary deep learning model (Neosoma Glioma) for automating gross tumor volume (GTV) segmentation in postoperative GBM per ESTRO-EANO guidelines. - Materials and methods - We retrospectively analyzed 100 GBM cases treated at Inselspital University Hospital, Bern (2016-2020) with standardized multi-modal MRI. Auto-segmented GTVs were compared to expert-defined contours using geometric metrics. Radiation oncologists reviewed and adjusted the best-performing configuration. Time savings, geometric similarity, and dosimetric impact were assessed. - Results - Optimal auto-segmentation (resection cavity plus enhancing tumor with 1 mm margin) achieved a mean Dice similarity coefficient of 0.79 (SD = 0.14) vs. ground truth. Manual adjustment took 5.9 (SD = 4.6) minutes vs. 12.3 (SD = 6.8) minutes for manual contouring (>50 % time reduction). The mean Dice between auto-segmented and adjusted GTVs was 0.84 (SD = 0.18). Dosimetric evaluation showed plans from adjusted auto-segmentations were equivalent to those based on consensus contours, with no clinically relevant differences in target coverage or organ-at-risk sparing. - Conclusion - 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. | ||
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| 700 | 1 | |a Reinhardt, Philipp |e VerfasserIn |4 aut | |
| 700 | 1 | |a Müller, Michael |e VerfasserIn |4 aut | |
| 700 | 1 | |a Meuller, Silvan |e VerfasserIn |4 aut | |
| 700 | 1 | |a Reyes, Mauricio |e VerfasserIn |4 aut | |
| 700 | 1 | |a Brueningk, Sarah |e VerfasserIn |4 aut | |
| 700 | 1 | |a Manser, Peter |e VerfasserIn |4 aut | |
| 700 | 1 | |a Aebersold, Daniel M. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Ermiş, Ekin |e VerfasserIn |4 aut | |
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