ArticleClinical and translational radiation oncology2024
Selection for proton radiotherapy of grade 1-3 glioma patients.
Article in Clinical and translational radiation oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Prediction model for the selection of patients with glioma to proton therapy.Acta oncologica (Stockholm, Sweden) · 2025Article
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: For adult patients with grade 1-3 gliomas, identifying patients with an indication for proton therapy (PT) can be challenging due to sparse evidence supporting its benefits. In this study, we aimed to ensure national consensus and develop a decision support tool to aid clinicians in identifying patients with grade 1-3 gliomas eligible for PT. Methods: Sixty-one historic patients referred for postoperative radiotherapy for glioma grade 1-3 were included in this study and had new photon therapy and PT plans calculated. These plans along with clinical parameters were presented to neurooncologists with experience in treating brain tumours. The patients were presented at three workshops (WSs), where each neurooncologist individually had to choose between photon and proton therapy. Important parameters were selected using cross validation. Multivariable logistic regression was used to predict the neurooncologists' treatment modality choice. Results: At the three WSs 23, 24 and 19 randomly selected patients were presented. Seventy-five percent of the neurooncologists agreed for 14 patients (61%), 16 patients (67%) and 15 patients (79%) at WS1, WS2 and WS3. Age at radiotherapy and difference in mean dose (ΔDmean) to the residual brain were significant predictors of the choice of treatment modality, p < 0.001. Model coefficients were: β Conclusion: Higher degree of agreement was reached. Age and ΔDmean to the residual brain significantly predicted the choice of radiation modality. We have developed a decision support model which may aid in the selection of patients with glioma grade 1-3 to PT.
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