Sci-Thurs AM: YIS-07: Dosimetric Consequences of Surgical Cavity Contour Variability in Accelerated Partial Breast Irradiation
Bibliographic record
Abstract
Introduction: Contouring variability of the surgical cavity (SC) can have important implications in the planning and delivery of accelerated partial breast irradiation (APBI). This study aims to quantify the dosimetric consequences of these variations. Methods: Twelve patients with breast lesions suitable for APBI underwent four CT scans: one planning CT and three CTs during treatment. Three radiation oncologists contoured the SC on each CT. In addition, for three patients, oncologists repeated SC contouring twice to assess intraobserver variations. SC contour variability was quantified by constructing a representative SC (RSC) and calculating the standard deviation (SD) at each RSC contour point. Treatment fields from the original plan were applied to repeat CTs. The dosimetric impact of contour variations was assessed using the equivalent uniform dose (EUD) formalism. Dose-volume constraints for normal tissues were also examined during treatment. Results: The maximum interobserver RSC SD was larger than the maximum intraobserver SD (1.50 versus 0.90 cm; ). Despite these differences, there was adequate dose coverage of the SC. The SC EUD was less than 38.0 Gy in only 9.3% of CT studies. Dose-volume constraints for the thyroid and ipsilateral lung were satisfied for all CT studies. While heart constraints were met for right-sided lesions, they were not met for two CT studies for a left-breast patient. Conclusions: Planning margins used for APBI provide adequate dose coverage despite contour variability. The planning dose constraint for the heart is not always met during treatment for left-breast patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".