A radiotherapy planning study of RapidArc, intensity modulated radiotherapy, three‐dimensional conformal radiotherapy, and parallel opposed beams in the treatment of pediatric retroperitoneal tumors
Bibliographic record
Abstract
BACKGROUND: This planning study compared RapidArc, fixed-field IMRT (cIMRT), 3D conformal radiotherapy (3D-CRT), and a parallel-opposed pair (POP) for children with retroperitoneal tumors. PROCEDURE: Plans were generated in eight patients to treat the PTV (dose range 19.8-45 Gy) while limiting kidney and liver doses. In selected patients, vertebral body (VB) dose heterogeneity was minimized. Cumulative DVH parameters, monitor units (MU), and treatment times were compared for the four techniques using the Wilcoxon matched pairs test. RESULTS: RapidArc and cIMRT covered target volumes more conformally than 3D-CRT and POP (P = 0.012). There was no difference in the ability to meet kidney dose constraints. A significantly lower volume of the liver received 12 Gy with cIMRT or RapidArc compared with 3D-CRT (P = 0.028). Where VB was included in PTV, VB dose homogeneity was generally within 94-104% of the prescription dose. Time to deliver a single fraction with RapidArc, POP, 3D-CRT, and cIMRT was 1.25 ± 0.01, 1.38 ± 0.10, 2.6 ± 0.45, and 4.02 ± 1.12 min, respectively (P = 0.012). Monitor units for a single fraction with POP, 3D-CRT, RapidArc, and cIMRT were 203 ± 26, 235 ± 32, 325 ± 71, and 665 ± 215, respectively (P < 0.05). CONCLUSIONS: POP resulted in favorable MU, treatment time and dosimetry but had poor conformality. 3D-CRT was more conformal but had higher MU and treatment time. RapidArc and cIMRT were generally no better dosimetrically than conformal techniques. RapidArc was dosimetrically very similar to cIMRT, but resulted in a major reduction in time and MU used to deliver the radiation.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".