Dosimetric evaluation of VMAT automated breast treatment plans: Towards the establishment of an institutional plan acceptability criteria
Bibliographic record
Abstract
Abstract Introduction: To evaluate the clinical suitability of the current facility-based treatment plan protocol in establishing acceptability criteria. Material and methods: Automated Volumetric Arc Therapy (VMAT) treatment plans were retrospectively evaluated for intact breast and chest-wall cancer patients from January 2021 to January 2023. Results: A total of 94 patients were planned and treated using automated contouring and VMAT planning technique. The number of patients planned and treated for intact breast and chest-wall were 41 (43.6%) and 53 (56.4%), respectively. The mean intact breast volumes for optimization (Brst_opt) receiving 95% and 105% of the prescribed doses were 92.80% ± 1.11 and 1.54% ± 1.02, respectively. Their corresponding mean chest-wall volumes for optimization (Chst_opt) were 90.65% ± 3.19 and 2.28% ± 2.99, respectively. For left-sided cases, the mean heart dose received was 4.61 Gy ± 1.76 and 5.18 Gy ± 1.55 for intact breast plans and that for chest-wall plans, respectively. The mean ipsilateral lung volume receiving 20 Gy of the prescribed dose was 12.22% ± 3.86 and 13.19% ± 3.74 for intact breast plans and chest-wall plans, respectively. For the Brst_opt and Chst_opt dose metrics were calculated; the mean homogeneity index (HI) was 0.14 ± 0.03 and 0.15 ± 0.04, mean uniformity index (UI) was 1.09 ± 0.03 and 1.11 ± 0.03, and mean conformity index (CI) were 0.92 ± 0.04 and 0.91 ± 0.04, respectively. Conclusions: The dosimetric evaluation shows a good dose distribution in the target volumes with minimal doses to the organs at risk (OAR). Assessment of the current data affirms the clinical usefulness of the facility-adopted protocol in achieving quality treatment plans for intact breast and chest-wall irradiations. The establishment of plan acceptability criteria will help achieve improved overall treatment outcomes.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".