Setup variations in locoregional radiotherapy for breast cancer: an electronic portal imaging study
Bibliographic record
Abstract
Recent trials demonstrating a survival benefit with locoregional radiotherapy (LRRT) to the chest wall and regional nodes in women with node-positive breast cancer have led to increased use of complex techniques to match three or more radiation fields, but information on setup reproducibility with LRRT for breast cancer is scarce. This study reports the magnitude and directions of random and systematic deviations in LRRT for breast cancer using an offline electronic portal imaging verification protocol. Electronic portal images (EPIs) of 46 consecutive women treated with LRRT for breast cancer from March 2001 to February 2002 with LRRT were analysed. Comparisons of EPIs to the corresponding digitally reconstructed radiographs were performed offline with anatomy matching. Displacements in mm were recorded in the superior-inferior (SI), medial-lateral (ML), and anterior-posterior (AP) directions. Random errors ranged from 2.0 mm to 2.5 mm for the breast/chest wall tangential treatments and 2.3 mm to 3.9 mm for the supraclavicular nodal treatments. Systematic errors occurred to a greater degree in the AP direction for the tangential fields and in the ML direction for the supraclavicular field. Displacements of > or =10 mm were found in 1.2% of breast/chest wall tangential treatments and in 6.2% of supraclavicular nodal treatments. These data demonstrate that EPI is a useful tool to verify setup reproducibility in LRRT for breast cancer.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".