Lung sparing and dose escalation in a robust‐inspired IMRT planning method for lung radiotherapy that accounts for intrafraction motion
Bibliographic record
Abstract
PURPOSE: To test the efficacy of a simple, robust-inspired intensity modulated radiotherapy (IMRT) planning strategy for lung radiotherapy designed to reduce lung dose and escalate tumor dose using realistic dose accumulation tools. METHODS: A deformable image registration tool was used to plan and accumulate dose over all phases of the breathing cycle for conventional and robust-inspired IMRT strategies of eight nonsmall cell lung cancer patients exhibiting peak-to-peak respiratory motion with amplitudes ranging from 1 to 2 cm in the craniocaudal direction. The authors' robust-inspired plans were designed to have smaller beam apertures based on target location during exhale, combined with edge-enhanced intensity maps to ensure target coverage during inspiration. For these, a new planning target volume defined as the rPTV was generated from a 5-mm isotropic expansion of the clinical target volume (CTV) on end-exhale combined with a boost volume, set to 110% of the prescription dose. Plans were evaluated in terms of (i) lung sparing and (ii) dose escalation for mean lung dose (MLD) isotoxicity. CTV and planning target volumes (PTV) coverage and lung dose were compared to the conventional IMRT approach. RESULTS: Robust-inspired plans showed potential lung dose reductions in seven out of eight patients. For non-GTV lung, percent reductions of 3%-14% in MLD and 6%-15% in V20 were observed. For seven of eight cases, the robust-like approach yielded increased accumulated doses to CTV. Isotoxicity studies for MLD showed increased dose to the CTV and the rPTV, in the range of 104%-118% and 95%-114% of prescription dose, respectively. CONCLUSIONS: A 4D dose calculation based on deformable image registration was used to evaluate a robust-inspired planning strategy for lung radiotherapy. This method offers notable reductions to lung dose while improving tumor coverage through the use of reduced geometric margins combined with edge enhancements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 0.000 |
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".