Establishing action threshold for change in patient anatomy using <scp>EPID</scp> gamma analysis and <scp>PTV</scp> coverage for head and neck radiotherapy treatment
Bibliographic record
Abstract
PURPOSE: To present a new adaptive radiotherapy (ART) method based on relative gamma analysis and patient classification for the identification of anatomical changes that induce a sufficient dosimetric impact to affect the treatment delivery and require complete replanning. METHODS: This retrospective study includes 55 patients treated for a head and neck cancer with IMRT, VMAT, or 3D conformal RT. Electronic Portal Imaging Device images for all treatment fields were acquired daily at every fraction. CBCTs were collected at least once a week. Gamma analysis was performed using the first fraction of the treatment as a reference once validated that it was delivered without error. Gamma analysis parameters (<γ>, standard deviation and the Top 1% γ) were used to define categories using statistic from a k-means clustering analysis. From these categories an action threshold was defined and correlated with dosimetric changes. For 23 of 55 patients, the V100% for PTV was computed for both, the planning CT and original contours deformed onto CBCT acquired at the last fraction. These values were then compared with 2D image relative γ-analysis of EPID images. Sensitivity and specificity of the method for the detection of dosimetric changes were computed. RESULTS: Three categories indicating an increasing level of change with the planned treatment were identified. A threshold was established for which patients were at risk of deviation at <γ> = 0.42. From 23 recomputing plans, it has been confirmed that patients with a strong dosimetric impact were above this threshold, with a specificity of 0.80 and a sensitivity of 0.84. CONCLUSIONS: The specificity and the sensitivity value confirmed the performance of the method to detect anatomical changes. The γ-analysis threshold correlated well with morphological changes that have a relevant dosimetric impact. Analysis of daily EPID images provides a method to identify patients at risk of deviation from their planned treatment and can support an early replanning decision.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".