On the impact of absorbed dose specification, tissue heterogeneities, and applicator heterogeneities on Monte Carlo‐based dosimetry of Ir‐192, Se‐75, and Yb‐169 in conventional and intensity‐modulated brachytherapy for the treatment of cervical cancer
Bibliographic record
Abstract
Purpose The purpose of this study was to evaluate the impact of dose reporting schemes and tissue/applicator heterogeneities for 192Ir‐, 75Se‐, and 169Yb‐based MRI‐guided conventional and intensity‐modulated brachytherapy. Methods and Materials Treatment plans using a variety of dose reporting and tissue/applicator segmentation schemes were generated for a cohort (n = 10) of cervical cancer patients treated with 192Ir‐based Venezia brachytherapy. Dose calculations were performed using RapidBrachyMCTPS, a Geant4‐based research Monte Carlo treatment planning system. Ultimately, five dose calculation scenarios were evaluated: (a) dose to water in water (Dw,w); (b) Dw,w taking the applicator material into consideration (Dw,wApp); (c) dose to water in medium (Dw,m); (d and e) dose to medium in medium with mass densities assigned either nominally per structure (Dm,m (Nom)) or voxel‐by‐voxel (Dm,m). Results Ignoring the plastic Venezia applicator (Dw,wApp) overestimates Dm,m by up to 1% (average) with high energy source (192Ir and 75Se) and up to 2% with 169Yb. Scoring dose to water (Dw,wApp or Dw,m) generally overestimates dose and this effect increases with decreasing photon energy. Reporting dose other than Dm,m (or Dm,m Nom) for 169Yb‐based conventional and intensity‐modulated brachytherapy leads to a simultaneous overestimation (up to 4%) of CTVHR D90 and underestimation (up to 2%) of bladder D2cc due to a significant dip in the mass‐energy absorption ratios at the depths of nearby targets and OARs. Using a nominal mass‐density assignment per structure, rather than a CT‐derived voxel‐by‐voxel assignment for MRI‐guided brachytherapy, amounts to a dose error up to 1% for all radionuclides considered. Conclusions The effects of the considered dose reporting schemes trend correspondingly between conventional and intensity‐modulated brachytherapy. In the absence of CT‐derived mass densities, MRI‐only‐based dosimetry can adequately approximate Dm,m by assigning nominal mass densities to structures. Tissue and applicator heterogeneities do not significantly impact dosimetry for 192Ir and 75Se, but do for 169Yb; dose reporting must be explicitly defined since Dw,m and Dw,w may overstate the dosimetric benefits.
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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 | 0.003 | 0.009 |
| 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) | 0.000 | 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".