Robustness of three external beam treatment techniques against inter‐fractional positional variations of the metal port in breast tissue expanders
Bibliographic record
Abstract
Abstract Introduction Temporary breast tissue expanders contain a metal port that varies in position throughout the course of radiation treatments. The purpose of this study was to quantify the robustness of the three most common external beam treatment techniques (tangential three‐dimensional conformal radiation therapy [3DCRT], volumetric modulated arc therapy [VMAT], and helical tomotherapy) against our measured inter‐fractional positional variations of the port. Methods For eight breast cases, a clinical plan was created for each of the three techniques. The dosimetric effect of our previously measured inter‐fractional port errors was evaluated for two classes of error: internal port errors (IPEs) and patient registration errors (PREs). For both classes of error, daily variable and systematic errors were modeled, and their cumulative effects were compared against the originally planned doses. Results For systematic IPE, the 1%–99% range in point dose differences inside a 5‐mm target abutting the implant was the highest for tangential 3DCRT, and it was within 6% and 9% when calculated with Monte Carlo and collapsed cone calculation engines, respectively. Daily variable PRE resulted in mean changes of −3.0% and −3.5% to V100%Rx of the target for VMAT and tomotherapy, respectively. For nearby organs, daily variable PRE resulted in changes to V20Gy of the ipsilateral lung of less than 2% in all three techniques, while V5Gy of the heart increased by as much as 6% in VMAT and 10% in tomotherapy. Conclusions When IPEs were modeled, dose variability was the largest in tangential 3DCRT, leading to areas of underdosage in the shadow of the port. When PREs were modeled, the target coverage and nearby organs were affected the most in VMAT and helical tomotherapy. In reality, port positional errors result from a combination of IPE and PRE, suggesting that VMAT and tomotherapy are more robust when patient registration errors are minimized, despite the presence of IPE.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".