Radical radiotherapy for locally advanced non-small cell lung cancer—what’s up with arm positioning?
Bibliographic record
Abstract
Abstract: Radical thoracic radiotherapy is ideally delivered in the arms-up (AU) position; however, patient comfort may only allow for arms-down (AD) positioning to be feasible. Objectives of this study were (I) to evaluate the dosimetric impact of changing arm position during treatment and (II) to compare plan quality for optimization in AU vs. AD positions. In this retrospective planning study, stage III lung cancer patients (n=10) who received 60 Gy in 30 fractions using volumetric modulated arc therapy (VMAT) were identified. To simulate AD treatment, a PET/CT (acquired AD) was registered to the planning CT (acquired AU) for arm delineation. The clinically delivered plan (AU) was recalculated with a density override to 1 g/cm3 for one or both arm contours (AD). Plans were also re-optimized for the AD position. Dose-volume parameters were compared for each scenario. Moving from AU to AD without re-optimization resulted in a mean 3.7% reduction in PTV D95; in all cases, this caused 95% of the PTV to receive ≤57 Gy. The mean arms D2cc were 23.1 and 4.0 Gy for the ipsilateral and contralateral, respectively. Dosimetric consequences of ipsilateral arm only were similar to both AD, whereas contralateral arm only had less than 1% effect on PTV D95. Re-optimizing to account for both AD recovered PTV D95 coverage with acceptable doses to all organs at risk. Arm D2cc were also decreased to 5.5 and 2.3 Gy for ipsilateral and contralateral, respectively. There was a significant difference in heart V25 and mean heart dose (P<0.001), but the magnitude was small at 4.1% for V25 and 1.7 Gy for mean heart dose and the plans still met institutional dose constraints. This planning study suggests that it is feasible to plan radiotherapy for locally advanced lung cancer patients in the AD position using VMAT, when necessary, with only a modest dosimetric impact.
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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".