Fiducial marker motion relative to the tumor bed has a significant impact on PTV margins in partial breast irradiation
Bibliographic record
Abstract
INTRODUCTION: With the introduction of accelerated partial breast irradiation (APBI) and the trend of reducing the number of fractions, the geometric accuracy of treatment delivery becomes critical. APBI patient setup is often based on fiducials, as the seroma is frequently not visible on pretreatment imaging. We assessed the motion of fiducials relative to the tumor bed between planning CT and treatment, and calculated margins to compensate for this motion. METHODS: A cohort of seventy patients treated with APBI on a Cyberknife was included. Planning and in-room pretreatment CT scans were registered on the tumor bed. Residual motion of the centers of mass of surgical clips and interstitial gold markers was calculated. We calculated the margins required per desired percentage of patients with 100% CTV coverage, and the systematic and random errors for fiducial motion. RESULTS: For a single fraction treatment, a margin of 1.8 mm would ensure 100% CTV coverage in 90% of patients when using surgical clips for patient set-up. When using interstitial markers, the margin should be 2.2 mm. The systematic and random errors were 0.46 mm for surgical clip motion and 0.60 mm for interstitial marker motion. No clinical factors were found predictive for fiducial motion. CONCLUSIONS: Fiducial motion relative to the tumor bed between planning CT and APBI treatment is non-negligible and should be included in the PTV margin calculation to prevent geographical miss. Systematic and random errors of fiducial motion were combined with other geometric uncertainties to calculate comprehensive PTV margins for different treatment techniques.
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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 | 0.013 |
| 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 | 0.000 | 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".