Dose-painted volumetric modulated arc therapy of high-grade glioma using 3,4-dihydroxy-6-[18F]fluoro-L-phenylalanine positron emission tomography
Bibliographic record
Abstract
Objective: To determine whether dose painting with volumetric modulated arc therapy for high-grade gliomas using 3,4-dihydroxy-6-[18F]fluoro-l-phenylalanine (18F-FDOPA) positron emission tomography (PET) could achieve dose-escalated coverage of biological target volumes (BTVs) without increasing the dose to cranial organs at risk (OARs). Methods: 10 patients with high-grade gliomas underwent CT, MRI, and 18F-FDOPA PET/CT images for post-operative radiation therapy planning. Two volumetric modulated arc therapy plans were retrospectively generated for each patient: a conventional plan with 60 Gy in 30 fractions to the planning target volume delineated on MRI and a dose-escalated plan with a maximum dose of 80 Gy in 30 fractions to BTVs. BTVs were created by thresholding 18F-FDOPA PET/CT uptake using a linear quadratic model that assumed tracer uptake was linearly related to tumour cell density. The maximum doses and equivalent uniform doses of OARs were compared. Results: The median volume of the planning target volume receiving at least 95% of the prescribed dose (V 95%) was 99.6% with and 99.5% without dose painting. The median V 95% was >99.2% for BTVs. The maximum doses and equivalent uniform doses to the OARs did not differ significantly between the conventional and dose-painted plans. Conclusion: Using commercially available treatment planning software, dose painting for high-grade gliomas was feasible with good BTV coverage and no significant change in the dose to OARs. Advances in knowledge: A novel treatment planning strategy was used to achieve dose painting for gliomas with BTVs obtained from 18F-FDOPA PET/CT using a radiobiological model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".