Framework for the quantitative assessment of adaptive radiation therapy protocols
Bibliographic record
Abstract
BACKGROUND: Adaptive radiation therapy (ART) "flags," such as change in external body contour or relative weight loss, are widely used to identify which head and neck cancer (HNC) patients may benefit from replanned treatment. Despite the popularity of ART, few published quantitative approaches verify the accuracy of replan candidate identification, especially with regards to the simple flagging approaches that are considered current standard of practice. We propose a quantitative evaluation framework, demonstrated through the assessment of a single institution's clinical ART flag: change in body contour exceeding 1.5 cm. METHODS: Ground truth replan criteria were established by surveying HNC radiation oncologists. Patient-specific dose deviations were approximated by using weekly acquired CBCT images to deform copies of the CT simulation, yielding during treatment "synthetic CTs." The original plan reapplied to the synthetic CTs estimated interfractional dose deposition and truth table analysis compared ground truth flagging with the clinical ART metric. This process was demonstrated by assessing flagged fractions for 15 HNC patients whose body contour changed by >1.5 cm at some point in their treatment. RESULTS: Survey results indicated that geometric shifts of high-dose volumes relative to image-guided radiation therapy alignment of bony anatomy were of most interest to HNC physicians. This evaluation framework successfully identified a fundamental discrepancy between the "truth" criteria and the body contour flagging protocol selected to identify changes in central axis dose. The body contour flag had poor sensitivity to survey-derived major violation criteria (0%-28%). The sensitivity of a random sample for comparable violation/flagging frequencies was 27%. CONCLUSIONS: These results indicate that centers should establish ground truth replan criteria to assess current standard of practice ART protocols. In addition, more effective replan flags may be tested and identified according to the proposed framework. Such improvements in ART flagging may contribute to better clinical resource allocation and patient outcome.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".