Poster — Thur Eve — 25: In vivo dosimetric verification of intensity‐modulated radiation therapy
Bibliographic record
Abstract
Dosimetric verification of patient treatment plans has become increasingly important due to the widespread use of complicated delivery techniques. IMRT and VMAT treatments are typically verified prior to start of the patient's course of treatment, using a point dose and/or a film measurement. Pre-treatment verification will not detect patient or machine-related errors; therefore, in vivo dosimetric verification is the only way to determine if the patient's treatment was delivered correctly. Portal images were acquired throughout the course of five prostate and six head-and-neck patient IMRT treatments. The corresponding predicted images were calculated using a previously developed portal dose image prediction algorithm, which combines a versatile fluence model with a patient scatter and EPID dose prediction model. The prostate patient image agreement was found to vary day-to-day due to rectal gas pockets and the effect of adjustable support rails on the patient couch. The head-and-neck patient images were observed to be more consistent daily, but an increased measured dose was evident at the periphery of the patient, likely due to patient weight loss. The majority of the fields agreed within 3% and 3 mm for greater than 90% of the pixels, as established by the χ-comparison. This work demonstrates the changes in patient anatomy that are detectable with the portal dose image prediction model. Prior to clinical implementation, the effect of the couch must be incorporated into the model, the image acquisition must be automatically scheduled and routine EPID QA must be undertaken to ensure the collection of high-quality EPID images.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".