Verification of Balloon Integrity by Ultrasound Imaging in Accelerated Partial Breast Irradiation
Bibliographic record
Abstract
Purpose: An overview of the ultrasound imaging artifacts encountered in Mammosite balloon APBI is presented. Analysis of ultrasound measurement errors and the dosimetric impact of balloon leakage were used to determine whether clinically relevant changes in balloon size can be reliably detected. Methods: Ultrasound imaging of a Mammosite balloon phantom was performed to better understand measurement errors and accuracy. The dose to the prescription point as a function of balloon diameter was computed for different sized balloons. The results were compared to phantom measurements of balloon diameter versus filling volume to assess the dose change that would result from tissue moving inward with a shrinking balloon boundary. In APBI patients undergoing a course of 10 treatment fractions, the accuracy and variability of balloon size measured with ultrasound imaging was compared to CT. Results: Ultrasound artifacts combine to form a false image of the distal balloon boundary. Proper US probe orientation and choice of measurement point locations improved distance measurement accuracy. A 1 mm change in balloon diameter is measurable with ±0.1 mm error and corresponds to <4% change in dose 1 cm from the balloon. Measurement errors relative to CT averaged less than 1.4 mm and variability (standard deviation) over the course of treatment averaged 1.9 mm. Conclusions: Properly performed ultrasound image acquisition and analysis can detect dosimetrically relevant changes in the size of a leaking balloon. This study confirms that US imaging is a valid method of verifying APBI balloon integrity over the course of treatment.
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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 | 0.004 |
| 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) | 0.001 | 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".