Sci-Thurs PM: Planning-06: A Simple, Robust IMRT Optimization Method for Lung Cancer, Accounting for Tissue Heterogeneity and Intra-Fraction Lung Tumour Motion
Bibliographic record
Abstract
Background For lung cancer radiotherapy, respiratory motion broadens dose penumbra, increasing the amount of normal tissues irradiated and reducing the target dose near the edge. Traditionally, large PTV margins are used to ensure coverage of the tumour in the presence of motion. Unfortunately, organs at risk intersecting with the PTV also receive high doses. The objective of this work was to evaluate a robust lung strategy to account for the effects of respiratory motion on tumour coverage and normal tissue sparing. Hypothesis Accumulating dose from 4DCT phases using a deformable registration tool combined with penumbral and motion compensation IMRT techniques can be used to develop robust lung plans that reduce the dose to normal tissues and maintain therapeutic coverage of the PTV. Methods A deformable image registration tool was used to plan and accumulate dose over 10 phases of the breathing cycle for clinical IMRT plans and robust IMRT plans of 5 NSCLC patients. Robust plans have reduced beam apertures, but added segments which prefentially boost the portion of the target that falls outside of the reference phase (e.g. the exhale phase). The dose to this boost volume was set to 110% of the prescription dose inside the peripheral edge of the PTV. Clinical and robust plans were normalized and compared for CTV coverage and lung dose. Results for the ipsilateral lung showed that on average, V20, V10 and V5 decreased by approximately 3.0% with the robust approach. For all cases, the accumulated dose to CTV was increased. Conclusions Robust lung IMRT allows for reduction of geometric margins sparing ipsilateral lung and enhancing tumour coverage.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".