Image‐guided high dose rate endorectal brachytherapy
Bibliographic record
Abstract
Fractionated high dose rate endorectal brachytherapy (HDR-EBT) using CT-based treatment planning is an alternative method for preoperative down-sizing and down-staging of advanced rectal adeno-carcinomas. The authors present an image guidance procedure that was developed to ensure daily dose reproducibility for the four brachytherapy treatment fractions. Since the applicator might not be placed before each treatment fraction inside the rectal lumen in the same manner as it was placed during the 3D CT volume acquisition used for treatment planning, there is a shift along the catheter axis that may have to be performed. The required shift is determined by comparison of a daily radiograph with the treatment planning digitally-reconstructed radiograph (DRR). A procedure is developed for DRR reconstruction from the 3D data set used for the treatment planning, and two possible daily longitudinal shifts are illustrated: above and below the planning dose distribution. The authors also describe the procedure for rotational alignment illustrated on a clinical case. Reproduction of the treatment planned dose distribution on a daily basis is crucial for the success of fractionated 3D based brachytherapy treatments. Due to the cylindrical symmetry of the applicator used for preoperative HDR-EBT, two types of adjustments are necessary: applicator rotation and dwell position shift along the applicator's longitudinal axis. The impact of the longitudinal applicator shift prior to treatment delivery for 62 patients treated in our institution is also assessed.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".