TH‐CD‐207A‐10: Using the Gamma Index to Flag Changes in Anatomy During Radiation Therapy of Head and Neck Cancer
Bibliographic record
Abstract
Purpose: This article presents a fast algorithm for comparing 3‐D anatomy from Cone‐Beam CT (CBCT) imaging using the gamma comparison index and to demonstrate how this can be used to flag patients for possible re‐planning of treatment. Methods: CBCT scans acquired on a Varian linear accelerator during treatment were used as input to the gamma comparator using thresholds of 5 mm distance to agreement and 30 Hounsfield Unit CT number difference. The fraction 1 CBCT study was initially used as the reference. Should there be a re‐plan during treatment, the reference resets to the CBCT study acquired on the day 1 of the re‐plan. Histograms of failing pixels (γ > 1) were generated from each 3‐D gamma map. An indicator of anatomy congruence, the match quality parameter (MQP), was derived from failed pixel histograms using the 90th percentile gamma value. The MQP was plotted versus fraction number and related to actual repeat computed tomography (re‐CT) order dates as decided by a radiation oncologist. From this, decision criteria were derived for the algorithm to “trigger” re‐CT consideration and predictive power was scored using receiver‐operator characteristic (ROC) analysis. Results: The MQP plot generally showed that the on‐line match from CBCT image guidance deteriorated as the treatment progressed due to weight loss and tumor regression. The optimized MQP criteria for triggering re‐CT consideration demonstrated high sensitivity and specificity, consistent with actual re‐CT order dates within ± 3 fractions. Out of 20 patients that were actually re‐planned, the algorithm failed to trigger a re‐CT recommendation only twice and this was caused by CBCT ring artifacts. Conclusion: We have demonstrated that gamma comparisons can be used to evaluate CBCT‐acquired anatomy pairs and, from this, an algorithm can be “trained” to flag patients for possible re‐planning in a manner consistent with local radiation oncology practice.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".