Robustness assessment of a novel 4D optimization approach for lung cancer radiotherapy
Bibliographic record
Abstract
One of the main challenges to treatment of lung cancer with radiation therapy is the tumor motion due to respiration. Previously, a novel approach was developed to generate treatment plans which compensate for respiratory motion and its variations. The worst case method is based on combining two intensity maps from two 4D plans optimized on the two worst cases of motion variations. The worst case planning method was previously tested on simulated motion variations. The goal of this project was to further test the worst case approach on realistic patient motion variations and treatment planning data. Two approaches to combining worst case plans were investigated: the first method takes the average of the two intensity maps, and the second method takes the maximum intensity of the two intensity maps. The robustness of worst case plans was compared with ITV plans and nominal 4D plans on three different motion variation scenarios. Study 1 and 2 investigated the robustness of the worst case methods on amplitude variations and patient motion variations on simulated image data. Study 3 investigated the robustness of the worst case methods on patient motion variations using real patient image data. The average intensity worst case method was only robust to Study 3 motion variations. The maximum intensity worst case method, the margin based, and the nominal approaches were not robust to any of the motion variations. Further evaluation over a wide range of tumour sizes, motion amplitudes and variability is required to determine the clinical applicability of the worst case planning method.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".