SU‐E‐J‐208: Dosimetric Assessment of Treatment Using CBCT Images
Bibliographic record
Abstract
PURPOSE: To evaluate target coverage for five breast patients receiving boost treatment to the tumor bed by calculating the daily dose on cone beam computed tomography (CBCT) images and utilizing deformable image registration techniques. METHODS: The daily dose is calculated on pretreatment CBCT images using the same beam configuration as the original volumetric modulated arc therapy (VMAT) plan. Calculations were done with two different isocenter positions according to: (1) the initial patient setup and (2) the shifts applied for treatment based on CBCT verification. The daily doses are deformed and accumulated onto the planning CT using commercially available deformable image registration software. The dose distribution is compared to the original distribution and tumor and PTV coverage is evaluated for both situations (initial and shifted positions). The deformation accuracy is evaluated by calculating the change in centroid location and the Dice coefficient of deformed contours. RESULTS: The tumor bed is adequately covered regardless of the treatment position. The average dose received by 98% (D98) of the tumor bed volume differs from the original plan by +1.6% and -0.2% for the shifted and initial positions respectively. However, when dose is accumulated in the initial setup position PTV coverage is lost; the average D98 for the PTV changes by -15.8% and -26.9% for the shifted and initial positions respectively. The average change in centroid location is 0.43 mm and 1.53 mm for the left and right lung contour respectively. The Dice coefficient for the left and right lung is 0.94 and 0.95 respectively. CONCLUSIONS: The margins used to define the PTV are sufficient to ensure tumor bed coverage for the given positioning variability. We are also confident in the deformation used to deform and accumulate dose based on deformed contour comparison. Hardware provided by MIM Software Inc.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".