A comparative dosimetric evaluation of dynamic conformal arc therapy and volumetric modulated arc therapy for lung stereotactic body radiotherapy
Bibliographic record
Abstract
This study assesses the viability of utilizing dynamic conformal arc therapy (DCAT) as an alternative to volumetric-modulated arc therapy (VMAT) in stereotactic body radiation therapy (SBRT) for lung cancer, with a focus on four-dimensional computed tomography (4DCT) in free-breathing conditions. We selected four non-small cell lung cancer (NSCLC) patients who had previously undergone VMAT SBRT and re-planned their treatment using DCAT. We compared the DCAT and VMAT plans based on dose distribution, conformity index (CI), homogeneity index (HI), gradient index (GI), and total monitor units (MUs). Quality assurance (QA) assessments for both plans were conducted using the Octavius 4D system (PTW, Freiburg, Germany). The results showed that CI Paddick was 0.80 ± 0.06 and 0.79 ± 0.04 (p-value >0.05), HI was 1.16 ± 0.03 (p-value <0.05) and 1.07 ± 0.02, GI was 3.91 ± 0.05 and 3.40 ± 0.05 (p-value <0.05), and MU was 1880.42 ± 135.19 and 5020.82 ± 188.03 for DCAT and VMAT, respectively (p-value <0.05). The average gamma passing rate exceeded 95 % with a 2 %/2 mm criteria. The dose distribution displayed remarkable similarity between DCAT and VMAT. The DCAT technique exhibits the capacity to deliver PTV dose distributions comparable to those achieved with VMAT, while significantly reducing treatment duration.
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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 | 0.001 | 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".