Sci—Sat AM: Brachy — 08: MRI‐guided planning and maximum achievable HR‐CTV doses in cervix brachytherapy
Bibliographic record
Abstract
PURPOSE: To present an institutional experience with MRI-based intracavitary brachytherapy planning for cervix cancer treatments using the EMBRACE protocol and to evaluate maximum HR-CTV doses that can be achieved when OAR (bladder, rectum, and sigmoid) doses are allowed to equal GECESTRO recommended thresholds. METHOD: Dose metrics from treatment plans for 20 patients created using MR images (for contouring HR-CTV and OARs) fused with CT images (for applicator reconstruction) are presented. Starting with a standard Manchester loading, plans were manually optimized (MO) by adjusting dwell positions and times to obtain the desired HR-CTV D90 target coverage of 35 Gy while limiting OAR doses to below recommended tolerances. In addition, retrospective planning was done using: (i) volume optimization (VO) to compare differences with MO in obtaining the desired target coverage; and (ii) MO and VO techniques to get the highest possible HR-CTV coverage by allowing OAR doses to equal tolerance values. The latter plans are referred to as MAX plans. RESULTS AND CONCLUSIONS: 3D MRI-guided treatment planning for cervix brachytherapy was shown to improve dose-volume coverage of the target and OARs. MO could conform HR-CTV D90 to the prescribed dose similar to the VO technique. Sigmoid was often the dose limiting structure. With respect to the prescribed HR-CTV D90 dose of 35 Gy, MAX plans could increase the prescribed dose by about 22% and 30% for MO and VO plans, respectively, without exceeding OAR thresholds. Consequently, dose escalation for MRI-guided cervix brachytherapy appears feasible should clinical circumstances warrant.
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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 | 0.002 |
| 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) | 0.009 | 0.002 |
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".