Assessment of dose to vaginal mucosa for gynecologic template interstitial high-dose-rate brachytherapy using Monte Carlo simulation
Bibliographic record
Abstract
Purpose: This study investigated reliable vaginal mucosa dose-volume histogram (DVH) metrics in gynecologic template interstitial high-dose-rate brachytherapy (HDR-BT) for the purpose of standardized dose reporting.Material and methods: Gynecologic template (Syed/Neblett) interstitial HDR-BT patients treated from September 2016 to November 2022 at the study institute were included in the cohort.Each patient implant included a vaginal mucosa contour defined by a 5 mm expansion from vaginal cylinder, then another volume with clinical target volume subtracted.DVH metrics were investigated between D 0.1cc to D 4cc .Clinical plans were re-calculated using Monte Carlo (MC) simulations both in heterogeneous material and in water.Results: The patient cohort included 61 patients with clinical plans using conventional homogeneous dose calculation (TG43).Heterogeneous vs. water MC dose differences were between -1.1% and -1.4% for all metrics investigated.DVH metrics D 1cc and smaller resulted in > 5% discrepancies between TG43 and MC dose (to water) calculation due to the proximity of source positions in/nearby the vaginal mucosa.Reliability improved when DVH metric volume was larger (D 2cc and D 4cc ).Both D 2cc and D 4cc presented very high linear correlation between TG43 and MC reported doses for the vagina, and average ± standard deviation dose difference was 4.6 ±2.9% and -3.0 ±1.9%, respectively.Dose differences decreased when the clinical target volume was removed: -1.5 ±3.5% and -0.8 ±2.1% for D 2cc and D 4cc , respectively.Conclusions: For perineal template gynecologic HDR-BT procedures, the 2 cc volume is the smallest representative volume that reliably reports vaginal dose and at minimum should be reported to establish dose and outcome evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 | 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".