Poster — Thur Eve — 62: Assessing the clinical application of the van Herk margin formula for lung radiotherapy
Bibliographic record
Abstract
According to a margin recipe developed by van Herk et al. the Planning Target Volume (PTV) margin to ensure the Clinical Target Volume is covered by at least 95% of the prescribed dose can be calculated by applying the following formula: . In the van Herk Margin formula (VHMF), Σ is the standard deviation (SD) of all systematic errors; σ is the SD of random errors and σp is the width of the penumbra. This formula is based on an idealized dose profile model that may not account for factors that vary significantly in lung radiotherapy such as tumour size and tissue density. The purpose of this study was to use accurate dose calculation algorithms and respiratory motion modeling to investigate the validity of the VHMF for lung radiotherapy. Random and systematic errors were simulated in treatment planning software using dose accumulation techniques for clinically relevant 3DCRT and IMRT treatment plans constructed on virtual phantoms. Phantom parameters such as target size, peak‐to‐peak motion amplitude and tissue density were varied to investigate their impact on the systematic and random error components of the margin formula. The VHMF was found to provide adequate dose coverage for all plans generated on different target sizes and motion amplitudes. Although discrepancies existed between idealized and realistic dose profiles in water and lung, the dose coverage defined by the V95 was not affected. The margin formula was found to be robust; however, further investigation of the influence of plan conformity is needed.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".