SU‐E‐J‐51: Interfractional Trend Analysis of Dose Discrepancies Based on 2D Portal Dosimetry
Bibliographic record
Abstract
PURPOSE: During a radiotherapy treatment course the dose delivery can be influenced by a number of factors, e.g. anatomical changes over time. Thiscan Result in discrepancies between planned and delivered dose. The electronic portal imaging device has been demonstrated to be valuable fortransit dosimetry verification. The aim of this study is to investigate theinformation that can be derived from 2D transit portal dosimetry by examining interfractional dose changes over a treatment course. METHODS AND MATERIALS: To create a trend overview of the interfractional changes intransit dose, the predicted portal dose for the different beams is compared to a measured portal dose using a ? EVALUATION: For each beam of the delivered fraction information is extracted from the ? images to differentiatesystematic from random dose delivery errors. From the systematic dose errors of a fraction for different projected contours, derived from the treatment planning contours several metrics are extracted like percentage pixels with ? exceeding unity. Finally the extracted metrics from each contour and beam are weighted with beam weight and the average andstandard deviation are calculated, resulting in a fraction Result. For this study, we analyzed 6 lung cancer patients and 20 prostate cancer patients. RESULTS: In some prostate cases the rectal filling was causing the dose delivery problems. For the lung cancer patients, anatomy changes from the diminishing atelectasis caused a transit dose difference and adaptations to the plan were applied. CONCLUSION: We have shown that from interfractional trend overview valuable information can be derived. However, to use this for adaptive radiotherapy, 2D transit dose differences with this methodshould be correlated with the 3D delivered dose, to define decision criteria.By optimizing these decision criteria it should be possible to prevent eitherover or under dosage of the tumor or OARs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".