Patterns of practice of adaptive re-planning for anatomic variances during cone-beam CT guided radiotherapy
Bibliographic record
Abstract
PURPOSE: Substantial, unanticipated anatomic variances during cone-beam CT (CBCT)-guided radiotherapy can potentially impact treatment accuracy and clinical outcomes. This study assessed patterns of practice of CBCT variances reported by RTTs and subsequent interventions for multiple-disease sites. METHODS: A chart review was conducted at a large cancer centre for patients treated with daily online CBCT-guided radiotherapy. Patients selected for review were identified via RTT-reported variances that then triggered offline multi-disciplinary assessment. Cases were categorized by the type of anatomic variance observed on CBCT and any further interventions recorded such as un-scheduled adaptive re-planning. RESULTS: Over a 1-year period, 287 variances from 261 patients were identified (6.2% of the 4207 patients treated with daily CBCT-guided radiotherapy), most often occurring within the first 5 fractions of the treatment course. Of these variances, 21% (59/287) were re-planned and 3.5% (10/287) discontinued treatment altogether. Lung was the most frequent disease-site (27% of 287 variances) reported with IGRT-related variances although head and neck and sarcoma were most frequently re-planned (19% of 59 re-plans for each site). Technical or clinical rationales for re-planning were not routinely documented in patient medical records. All disease-sites had numerous categories of variances. Three of the four most frequent categories were for tumor-related changes on CBCT, and the re-planning rate was highest for tumor progression at 25%. Normal tissue variances were the second most frequency category, and re-planned in 14% of those cases. CONCLUSION: adaptive re-planning or treatment discontinuation. Improved understanding of the clinical decisions in these cases would aid in developing more routine, systematic adaptive strategies.
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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 | 0.001 |
| 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".