Volumetric‐modulated arc therapy versus intensity‐modulated radiotherapy for large volume retroperitoneal sarcomas: A comparative analysis of dosimetric and treatment delivery parameters
Bibliographic record
Abstract
PURPOSE: To compare dosimetric and treatment delivery parameter differences between volumetric-modulated arc radiotherapy (VMAT) and intensity-modulated radiotherapy (IMRT) for large volume retroperitoneal sarcomas (RPS). MATERIALS AND METHODS: Both VMAT and IMRT planning were performed on CT datasets of 10 patients with RPS who had been previously treated with preoperative radiotherapy. Plans were optimized to deliver ≥95% dose to the PTV and were evaluated for conformity and homogeneity. Dose to the organs at risk (OARs) (kidney, liver, spinal cord, and bowel space), unspecified tissue, and dose evaluation volumes (DEVs) at 1, 2, and 5 cm from PTV were calculated and compared. Monitor units (MUs) and treatment delivery times were recorded and compared between the two techniques. The deliverability of the large volume RPS VMAT plans was verified by portal dosimetry on a Truebeam™ linac. RESULTS: VMAT and IMRT plans were equivalent for PTV coverage and homogeneity (P > 0.05); however, VMAT plans had slightly better conformity index, CI (P < 0.001). Doses to the OARs were not significantly different between VMAT and IMRT plans (P > 0.05). Mean doses to the unspecified tissue as well as at 1, 2, and 5 cm DEVs were lower with VMAT compared with IMRT, P = 0.04 and P < 0.01, respectively. MUs and average beam-on times were both significantly lower in the VMAT vs IMRT plans, P < 0.001 and P = 0.001, respectively. All VMAT plans passed portal dosimetry delivery verification with an average gamma passing rate of 99.6 ± 0.4%. CONCLUSIONS: VMAT planning for large volume RPS improved CI, and achieved comparable OAR sparing, as compared with IMRT. As treatment delivery time was lower, the use of VMAT for RPS may translate into improved treatment delivery efficiency.
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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 | 0.001 | 0.002 |
| 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".