Poster ‐ 36: Effect of Planning Target Volume Coverage on the Dose Delivered in Lung Radiotherapy
Bibliographic record
Abstract
Purpose: In lung radiotherapy, breathing motion may be encompassed by contouring the internal target volume (ITV). Remaining uncertainties are included in a geometrical expansion to the planning target volume (PTV). In IMRT, the treatment is then optimized until a desired PTV fraction is covered by the appropriate dose. The resulting beams often carry high fluence in the PTV margin to overcome low lung density and to generate steep dose gradients. During treatment, the high density tumour can enter the PTV margin, potentially increasing target dose. Thus, planning lung IMRT with a reduced PTV dose may still achieve the desired ITV dose during treatment. Methods: A retrospective analysis was carried out with 25 IMRT plans prescribed to 63 Gy in 30 fractions. The plans were re‐normalized to cover various fractions of the PTV by different isodose lines. For each case, the isocentre was moved using 125 shifts derived from all 3D combinations of 0 mm, (PTV margin ‐ 1 mm), and PTV margin. After each shift, the dose was recomputed to approximate the delivered dose. Results and Conclusion: Our plans typically cover 95% of the PTV by 95% of the dose. Reducing the PTV covered to 94% did not significantly reduce the delivered ITV doses for (PTV margin ‐ 1 mm) shifts. Target doses were reduced significantly for all other shifts and planning goals studied. Thus, a reduced planning goal will likely deliver the desired target dose as long as the ITV rarely enters the last mm of the PTV margin.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".