Single Arc Volumetric Modulated Arc Therapy for Complex Brain Gliomas: Is There an Advantage as Compared to Intensity Modulated Radiotherapy or by Adding a Partial Arc?
Bibliographic record
Abstract
The objective of this study was to determine if volumetric modulated arc therapy (VMAT) offers advantages over intensity modulated radiotherapy (IMRT) for complex brain gliomas and evaluate the role of an additional partial arc. Twelve patients with glioma involving critical organs at risk (OAR) were selected [six low grade brainstem glioma (BG) and six glioblastoma (GB) cases]. BGs were prescribed 54 Gy/30 fractions (frx), and GB treated to 50 Gy/30 frx to a lower dose PTV (PTV50) with a simultaneous integrated boost delivering a total dose of 60 Gy/30 frx to a higher dose PTV (PTV60). VMAT was planned with a single arc (VMAT1) and with an additional coplanar partial arc spanning 90° (VMAT2). We observed VMATI improving the PTV equivalent uniform dose (EUD) for BG cases (p=0.027), improving the V95 for the PTV50 in GB cases (p=0.026) and resulting in more conformal GB plans (p=0.008) as compare to IMRT. However, for the GB PTV60, IMRT achieved favorable V95 over VMAT1 and VMAT2 (0.0046 and 0.008, respectively). The GB total integral dose (ID) was significantly lower with VMAT1 and VMAT2 (p=0.049 and p=0.006, respectively). Both VMAT1 and VMAT2 reduced the ID, however, only at the 5 Gy threshold for BG cases (p=0.011 and 0.005, respectively). VMAT achieved a lower spinal cord maximum dose and EUD for BG cases and higher optic nerve doses, otherwise no significant differences were observed. VMAT1 yielded the fastest treatment times and least MU. We conclude that VMAT offers faster treatment delivery for complex brain tumors while maintaining similar dosimetric qualities to IMRT. Selective dosimetric advantages in terms of spinal cord sparing and lowering the ID are observed favoring the use of an additional coplanar partial arc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".