Treatment planning study of Volumetric Modulated Arc Therapy and three dimensional field-in-field techniques for left chest-wall cancers with regional lymph nodes
Bibliographic record
Abstract
AIM: This study aims to investigate whether there are dosimetric advantages to using VMAT (Volumetric Modulated Arc Therapy) for left-sided chest-wall patients over the three-dimensional conformal field-in-field (FinF) technique. BACKGROUND: There is a lack of dosimetric studies dedicated for chest-wall patients. Potential dosimetric advantage could be obtained using VMAT due to complex geometry of PTVs (Planning Target Volumes) and OARs (Organs at Risk) in chest-wall and lymph nodes. MATERIALS AND METHODS: VMAT and FinF plans were generated and evaluated based on DVHs (Dose Volume Histograms) for both PTVs and OARs for 22 left-sided chest-wall patients with involved regional nodes. PTV HIs (Homogeneity Indices) and CIs (Conformity Indices), and EUDs (Equivalent Uniform Doses) for PTVs and OARs were also evaluated for comparisons between VMAT and FinF. RESULTS: in chest-wall patients compared to FinF plans. The volumes of the right lung and right breast receiving 5 Gy were much higher in VMAT than those in FinF for all patients. CONCLUSIONS: Compared to the FinF technique, there is a generally limited benefit using VMAT for left-sided chest-wall patients due to large low-dose-bath to OARs with insignificant improvement in PTV coverage. In case where FinF planning cannot meet dose constrains, VMAT provides a viable option. The use of VMAT planning over the FinF technique in chest-wall cancers should be carefully analyzed on an individual basis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".