Prediction model for the selection of patients with glioma to proton therapy
Background and purpose: The selection of patients with low-grade gliomas for proton therapy (PT) is often based on the comparison of photon and PT plans and demonstrating meaningful dose reduction to the healthy brain or critical structures. The aim of this retrospective study was to identify clinic...
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| Language: | English |
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Medical Journals Sweden
2025-07-01
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| Series: | Acta Oncologica |
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| Online Access: | https://medicaljournalssweden.se/actaoncologica/article/view/43883 |
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| author | Jesper Folsted Kallehauge Siri Grondahl Camilla Skinnerup Byskov Morten Høyer Slavka Lukacova |
| author_facet | Jesper Folsted Kallehauge Siri Grondahl Camilla Skinnerup Byskov Morten Høyer Slavka Lukacova |
| author_sort | Jesper Folsted Kallehauge |
| collection | DOAJ |
| description | Background and purpose: The selection of patients with low-grade gliomas for proton therapy (PT) is often based on the comparison of photon and PT plans and demonstrating meaningful dose reduction to the healthy brain or critical structures. The aim of this retrospective study was to identify clinical parameters associated with referral to PT and build a prediction model.
Patients and methods: The dataset consisted of patients with isocitrate dehydrogenase (IDH)-mutant grades 2 and 3 glioma and candidates for PT at the Aarhus University Hospital. Clinical (age, diagnosis, clinical target volume [CTV], and treatment) and dosimetric (prescribed dose and mean dose (Dmean) to healthy brain) parameters were collected. Univariate and multivariate logistic regression were used to assess the association with selection for PT. The dataset was split into training (n = 37, period 2019–2022) and test (n = 12, period 2023) cohorts. Prediction models were built using logistic regression algorithms and support vector machines (SVMs) and evaluated using the area under the precision-recall curve (AUC-PR).
Results: Age (p = 0.03) and CTV (p = 0.01) were significantly associated with the selection for PT and were used for model prediction. The logistic regression demonstrated AUC-PR at 0.999 (CI 0.999–1.000) and 0.998 (0.996–1.000) for training and test cohorts, respectively. SVM showed similar results with AUC-PR at 0.993 (0.993–0.994) for training and 0.999 (0.998–1.000) for test cohorts.
Interpretation: Logistic regression and SVM using age and CTV performed equally well and achieved a very high positive predictive value. With the pending external validation in a larger dataset, the prospects of this work suggest more consistent and efficient patient referral for PT.
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| format | Article |
| id | doaj-art-cafb05f3e5df4cdfa4b9f4763ca04863 |
| institution | DOAJ |
| issn | 1651-226X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Medical Journals Sweden |
| record_format | Article |
| series | Acta Oncologica |
| spelling | doaj-art-cafb05f3e5df4cdfa4b9f4763ca048632025-08-20T03:13:22ZengMedical Journals SwedenActa Oncologica1651-226X2025-07-016410.2340/1651-226X.2025.43883Prediction model for the selection of patients with glioma to proton therapyJesper Folsted Kallehauge0Siri Grondahl1Camilla Skinnerup Byskov2Morten Høyer3Slavka Lukacova4https://orcid.org/0000-0002-2875-095XDanish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark; Institute for Clinical Medicine, Aarhus University, Aarhus, DenmarkDepartment of Oncology, Aarhus University Hospital, Aarhus, DenmarkDepartment of Oncology, Aarhus University Hospital, Aarhus, DenmarkDanish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark; Institute for Clinical Medicine, Aarhus University, Aarhus, DenmarkInstitute for Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Oncology, Aarhus University Hospital, Aarhus, DenmarkBackground and purpose: The selection of patients with low-grade gliomas for proton therapy (PT) is often based on the comparison of photon and PT plans and demonstrating meaningful dose reduction to the healthy brain or critical structures. The aim of this retrospective study was to identify clinical parameters associated with referral to PT and build a prediction model. Patients and methods: The dataset consisted of patients with isocitrate dehydrogenase (IDH)-mutant grades 2 and 3 glioma and candidates for PT at the Aarhus University Hospital. Clinical (age, diagnosis, clinical target volume [CTV], and treatment) and dosimetric (prescribed dose and mean dose (Dmean) to healthy brain) parameters were collected. Univariate and multivariate logistic regression were used to assess the association with selection for PT. The dataset was split into training (n = 37, period 2019–2022) and test (n = 12, period 2023) cohorts. Prediction models were built using logistic regression algorithms and support vector machines (SVMs) and evaluated using the area under the precision-recall curve (AUC-PR). Results: Age (p = 0.03) and CTV (p = 0.01) were significantly associated with the selection for PT and were used for model prediction. The logistic regression demonstrated AUC-PR at 0.999 (CI 0.999–1.000) and 0.998 (0.996–1.000) for training and test cohorts, respectively. SVM showed similar results with AUC-PR at 0.993 (0.993–0.994) for training and 0.999 (0.998–1.000) for test cohorts. Interpretation: Logistic regression and SVM using age and CTV performed equally well and achieved a very high positive predictive value. With the pending external validation in a larger dataset, the prospects of this work suggest more consistent and efficient patient referral for PT. https://medicaljournalssweden.se/actaoncologica/article/view/43883GliomaProtonPrediction models |
| spellingShingle | Jesper Folsted Kallehauge Siri Grondahl Camilla Skinnerup Byskov Morten Høyer Slavka Lukacova Prediction model for the selection of patients with glioma to proton therapy Acta Oncologica Glioma Proton Prediction models |
| title | Prediction model for the selection of patients with glioma to proton therapy |
| title_full | Prediction model for the selection of patients with glioma to proton therapy |
| title_fullStr | Prediction model for the selection of patients with glioma to proton therapy |
| title_full_unstemmed | Prediction model for the selection of patients with glioma to proton therapy |
| title_short | Prediction model for the selection of patients with glioma to proton therapy |
| title_sort | prediction model for the selection of patients with glioma to proton therapy |
| topic | Glioma Proton Prediction models |
| url | https://medicaljournalssweden.se/actaoncologica/article/view/43883 |
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