Alert system for monitoring changes in patient anatomy during radiation therapy of head and neck cancer
Bibliographic record
Abstract
The purpose of this study is to validate a previously developed algorithm for alerting clinicians when to consider re-CT simulation due to changes in the patient's anatomy during radiation therapy of head and neck cancer. Cone beam computed tomography (CBCT) data were collected prospectively for 77 patients. Each CBCT was mathematically compared to a reference CBCT using the gamma index. We defined the match quality parameter (MQP) as an indicator of CBCT image similarity, where a negative MQP value indicates a poorer CBCT match than the match between the first two CBCT acquired during treatment. If three consecutive MQP values were below a chosen threshold, an "alert" is triggered to indicate action required, for example, possible re-CT simulation. The timing of image review requests made by the radiation therapists and any re-CT/re-plan decisions were documented for each patient's treatment course. The MQP for each patient (including any re-plans) was calculated in a manner that was blinded from the clinical process. The MQP as a function of fraction number was compared to actual clinical decisions in the treatment progress to evaluate alert system performance. There was a total of 93 plans (including re-plans) with 34 positives (action required) and 59 negatives (no action required). The sensitivity of the alert system was 0.76 and the false positive rate was 0.37. Only 1 case out of the 34 positive cases would have been missed by both the alert system and our clinical process. Despite the false negatives and false positives, analysis of the timing of alert triggers showed that the alert system could have resulted in seven fewer clinical misses. The alert system has the potential to be a valuable tool to complement human judgment and to provide a quality assurance safeguard to help improve the delivery of radiation treatment of head and neck cancer.
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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) | 0.000 | 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".