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Radiotherapy quality assurance in the PRO-GLIO trial: results from a dummy run comparing experts across twelve institutions in two Scandinavian countries
Oslo Univ Hosp, Norway; Univ Oslo, Norway.
Oslo Univ Hosp, Norway; Univ South Eastern Norway, Norway.
Sahlgrens Univ Hosp, Sweden; Univ Gothenburg, Sweden.
Oslo Univ Hosp, Norway.
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2026 (English)In: Clinical and Translational Radiation Oncology, E-ISSN 2405-6308, Vol. 60, article id 101220Article in journal (Refereed) Published
Abstract [en]

Background: Target volume and organ of interest (OOI) contouring and treatment planning for IDH-mutated gliomas CNS WHO grades 2 and 3 are subject to interindividual variation. We aimed to assess this variation and heighten delineation awareness as a quality assurance measure in the PRO-GLIO trial. Methods: Five experts established consensus delineations for target volumes and defined OOI in two IDH-mutated gliomas cases. Next, target volumes and OOI were delineated by experts from 12 treatment centers participating in the PRO-GLIO trial, and by deep learning segmentation (DLS). These structures were compared to consensus contours based on predefined quantitative and qualitative parameters. Proton and photon treatment planning were performed on the consensus delineations by 11 centers. Results: Median dice similarity coefficient (DSC) for clinical target volume (CTV) was 0.92 for both cases, whereas the median 95th percentile Hausdorff distance for CTV was 0.54 cm and 0.65 cm. Larger variability in DSC and volume size was seen for OOI, e.g., a median DSC of 0.47 and 0.64 for the optic chiasm and between 0.61 and 0.77 for the hippocampi. DSC-derived DLS-values were within the range of manual delineations for all OOI. Conclusions: Delineation variation between study centers was minor for target volumes, especially for DSC of CTV. Variability was considerably higher for several OOI, representing a potential hazard not least with new and more precise radiotherapy techniques. Interestingly, DLS performed OOI-segmentation within the range of manual experts and represents a reasonable OOI-segmentation alternative in the future.

Place, publisher, year, edition, pages
ELSEVIER IRELAND LTD , 2026. Vol. 60, article id 101220
Keywords [en]
Quality assurance; IDH-mutated glioma; Interobserver variability; Deep learning segmentation
National Category
Medical Imaging
Identifiers
URN: urn:nbn:se:liu:diva-226902DOI: 10.1016/j.ctro.2026.101220ISI: 001808771500001PubMedID: 42383068Scopus ID: 2-s2.0-105042615574OAI: oai:DiVA.org:liu-226902DiVA, id: diva2:2094304
Note

Funding Agencies|South-Eastern Norway Regional Health Authority [2021081]; Norwegian Cancer Society [216158]; Network in Radiation Oncology (NIRO); Swedish Society of Medicine [SLS-890541]; Gothenburg Society of Medicine [GLS-887961]; Jubileumsklinikens Cancerfond; Lions Cancer Research Fund of Western Sweden

Available from: 2026-08-21 Created: 2026-08-21 Last updated: 2026-08-21

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Division of Surgery, Orthopedics and OncologyFaculty of Medicine and Health SciencesDepartment of Oncology
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