SU‐E‐T‐589: A Robust 4D Treatment Planning Approach for Lung Radiotherapy
Bibliographic record
Abstract
Purpose: 4D treatment planning optimizes the cumulative dose delivered over the whole respiratory cycle to generate a plan that compensates for a patients individual respiratory motion. However, changes in the time spent in the each respiratory state may render a 4D plan invalid. We introduced and evaluate two robust treatment planning approaches to compensate for respiratory motion. Methods: A 4D optimization method was developed which optimizes the phase‐weighted dose distribution. Two robust 4D treatment planning approaches were tested: (1) planning on the average motion pdf (AVE_PDF); and (2) combining 4D plans designed on the “worst case” pdfs (WC_PDF). The sensitivity of nominal and robust 4D treatment plans to respiratory motion variations was tested for two scenarios where respiratory phase weights were modified to model changes in amplitude as well as the relative proportion of the respiratory cycle spent inhaling vs. exhaling. Results: The DVHs of robust plans show less sensitivity to variation in breathing pattern. Compared to the nominal 4D plan, robust plans improve the V95 by 2 to 6 Gy and CTV min dose by 1 to 5 Gy. Conclusion: Robust 4D plan can be designed either using average pdf approach or worst case pdf approach. We find that nominal 4D plans are very sensitive to the variation in respiration pattern while robust 4D plans are less sensitive under the similar changes. As compared to static 4D plan, healthy tissue sparing is also better in robust plan. This research is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC).
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".