Sci-Thur PM: YIS - 04: Forcing lateral electron disequilibrium to spare lung tissue: A novel technique for SBRT of small lung tumours
Bibliographic record
Abstract
Stereotactic body radiation therapy(SBRT), a technique that uses tightly conformed Megavoltage(MV) x-ray fields, improves local control of lung cancer. However, small MV x-ray fields can cause lateral electron disequilibrium(LED), which reduces the dose within lung. These effects are difficult to predict and are presently a cause of alarm for the radiotherapy community. Previously, we developed The Relative Depth Dose Factor(RDDF), which is an indicator of the extent of LED (RDDF < 1). We propose a positive application of LED for lung sparing in SBRT: LED can be exploited to irradiate a small tumor while greatly reducing the dose in surrounding lung tissue. The Monte Carlo code, DOSXYZnrc, was employed to calculate dose within a cylindrical lung phantom. The phantom's diameter and height were set to 25 cm, and consisted of water and lung (density = 0.25g/cm3) shells surrounding a small water tumor (volume = 0.8 cm3). Two 180° 6MV arcs were focused onto the tumor with field sizes of 1×1cm2(RDDF∼0.5) and 3×3cm2(RDDF∼1). Analyzing dose results, the 1×1cm2 arc reduced dose within lung and water tissues by 70% and 80% compared to the 3×3cm2 arc. Although, central tumor dose was also reduced by 15% using the 1×1cm2 arc, these reductions can be offset by escalating the prescription dose appropriately. Using the RDDF as a guideline, it's possible to design a SBRT treatment plan that reduces lung dose while maintaining relatively high tumor dose levels. Clinical application requires an accurate dose algorithm and may lower SBRT dose-induced toxicity levels in patients.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".