Planning target volume margins for prostate radiotherapy using daily electronic portal imaging and implanted fiducial markers
Bibliographic record
Abstract
BACKGROUND: Fiducial markers and daily electronic portal imaging (EPI) can reduce the risk of geographic miss in prostate cancer radiotherapy. The purpose of this study was to estimate CTV to PTV margin requirements, without and with the use of this image guidance strategy. METHODS: 46 patients underwent placement of 3 radio-opaque fiducial markers prior to prostate RT. Daily pre-treatment EPIs were taken, and isocenter placement errors were corrected if they were > or = 3 mm along the left-right or superior-inferior axes, and/or > or = 2 mm along the anterior-posterior axis. During-treatment EPIs were then obtained to estimate intra-fraction motion. RESULTS: Without image guidance, margins of 0.57 cm, 0.79 cm and 0.77 cm, along the left-right, superior-inferior and anterior-posterior axes respectively, are required to give 95% probability of complete CTV coverage each day. With the above image guidance strategy, these margins can be reduced to 0.36 cm, 0.37 cm and 0.37 cm respectively. Correction of all isocenter placement errors, regardless of size, would permit minimal additional reduction in margins. CONCLUSIONS: Image guidance, using implanted fiducial markers and daily EPI, permits the use of narrower PTV margins without compromising coverage of the target, in the radiotherapy of prostate 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 | 0.001 | 0.008 |
| 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".