Coplanar versus noncoplanar intensity‐modulated radiation therapy (IMRT) and volumetric‐modulated arc therapy (VMAT) treatment planning for fronto‐temporal high‐grade glioma
Bibliographic record
Abstract
The purpose of this study was to compare dosimetric and radiobiological parameters of treatment plans using coplanar and noncoplanar beam arrangements in patients with fronto-temporal high-grade glioma (HGG) generated for intensity-modulated radiotherapy (IMRT) or volumetric-modulated arc therapy (VMAT). Ten cases of HGG overlapping the optic apparatus were selected. Four separate plans were created for each case: coplanar IMRT, noncoplanar IMRT (ncIMRT), VMAT, and noncoplanar VMAT (ncVMAT). The prescription dose was 60 Gy in 30 fractions. Dose-volume histograms and equivalent uniform doses (EUD) for planning target volumes (PTVs) and organs at risk (OARs) were generated. The four techniques resulted in comparable mean, minimum, maximum PTV doses, and PTV EUDs (p ≥ 0.33). The mean PTV dose and EUD averaged for all techniques were 59.98 Gy (Standard Deviation (SD) ± 0.15) and 59.86 Gy (SD ± 0.27). Non-coplanar IMRT significantly reduced contralateral anterior globe EUDs (6.7 Gy versus 8.2 Gy, p = 0.05), while both ncIMRT and ncVMAT reduced contralateral retina EUDs (16 Gy versus 18.8 Gy, p = 0.03). Noncoplanar techniques resulted in lower contralateral temporal lobe dose (22.2 Gy versus 24.7 Gy). Compared to IMRT, VMAT techniques required fewer monitor units (755 vs. 478, p ≤ 0.001) but longer optimization times. Treatment delivery times were 6.1 and 10.5 minutes for coplanar and ncIMRT versus 2.9 and 5.0 minutes for coplanar and ncVMAT. In this study, all techniques achieved comparable target coverage. Superior sparing of contralateral optic structures was seen with ncIMRT. The VMAT techniques reduced treatment delivery duration but prolonged plan optimization times, compared to IMRT techniques. Technique selection should be individualized, based on patient-specific clinical and dosimetric parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".