MO‐F‐CAMPUS‐J‐05: Using 2D Relative Gamma Analysis From EPID Image as a Predictor of Plan Deterioration Due to Anatomical Changes
Bibliographic record
Abstract
Purpose: One of the side effects of radiotherapy for head and neck (H&N) cancer is the patient's anatomical changes. The changes can strongly affect the planned dose distribution. In this work, our goal is to demonstrate that relative analysis of EPID images is a fast and simple method to detect anatomical changes that can have a strong dosimetric impact on the treatment plan for H&N patients. Methods: EPID images were recorded at every beam and all fractions for 50 H&N patients. Of these, five patients that showed important anatomical changes were selected to evaluate dosimetric impacts of these changes and to correlate them with a 2D relative gamma analysis of EPID images. The planning CT and original contours were deformed onto CBCTs (one mid treatment and one at the end of treatment). By using deformable image registration, it was possible to map accurate CT numbers from the planning CT to the anatomy of the day obtained with CBCTs. Clinical treatment plan were then copied on the deformed dataset and dose was re‐computed. In parallel, EPID images were analysed using the gamma index (3%3mm) relative to the first image. Results: It was possible to divide patients in two distinct, statistically different (p<0.001) categories using an average gamma index of 0.5 as a threshold. Below this threshold no significant dosimetric degradation of the plan are observed. Above this threshold two types of plan deterioration were seen: (1) target dose increases, but coverage remains adequate while dose to at least one OAR increases beyond tolerances; (2) the OAR doses remain low, but the target dose is reduced and coverage becomes inadequate. Conclusion: Relative analysis gamma of EPID images could indeed be a fast and simple method to detect anatomical changes that can potentially deteriorates treatment plan for H&N patients. This work was supported in part by Varian Medical System
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".