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 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 | 0.002 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".