Poster - Thur Eve - 62: A Retrospective Assessment of the Prevalence and Dosimetric Effect of Lateral Electron Disequilibrium in a Population of Lung Cancer Patients Treated by Stereotactic Body Radiation Therapy
Bibliographic record
Abstract
Stereotactic Body Radiation Therapy (SBRT) is a treatment option for early stage non-small cell lung cancer (NSCLC). SBRT uses tightly conformed megavoltage (MV) x-ray beams to ablate the tumour. However, small MV x-ray fields may produce lateral electron disequilibrium (LED) within lung tissue, which can reduce the dose to tumour. The goal of this work is to estimate the prevalence of LED in NSCLC patients treated with SBRT, and determine dose effects for patients prone or averse to LED. Thirty NSCLC patients were randomly selected for analysis. 4-dimensional CT lung images were segmented into the right and left upper and lower lobes (RUL, RLL, LUL, LLL), and the right middle lobe. Dose calculations were performed using volume-modulated arc therapy in the Pinnacle3 TPS. Most tumours were located in the upper lobes (RUL 53%, LUL 27%) where density was significantly lower (RUL −808±46 HU vs. RLL −743±71 HU; LUL −808 ±56 HU vs. LLL −746±70 HU; p<0.001). In general, the prevalence of LED increased with higher beam energy. Using 6MV photons, patients with a RUL tumour experienced moderate (81 %), and mild (19%) levels of LED. At 18MV, LED became more prominent with severe (50%) and moderate (50%) LED exhibited. Dosimetrically, for patients prone to LED, poorer target coverage (i.e. increased R100 by 20%) and improved lung sparing (i.e. reduced V20 by −46%) was observed. The common location of lung cancers in the upper lobes, coupled with lower lung density, results in the potential occurrence of LED, which may underdose the tumour.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".