Individualized Dose-Escalation of HDR Prostate Brachytherapy Implant to Decrease Required External Beam Radiation Dose: A Retrospective Feasibility Study
Bibliographic record
Abstract
PURPOSE: High-dose-rate brachytherapy (HDR-BT) is commonly combined with external beam radiation therapy (EBRT) for the treatment of localized prostate cancer. Escalating the HDR-BT dose as far as organ-at-risk (OAR) constraints allow, on a personalized basis, would allow for a reduction in EBRT dose while achieving similar total biologic equivalence. The primary objective of this study was to determine the dosimetric feasibility of escalating the HDR-BT dose from 15 Gy to 16 or 17 Gy while continuing to meet OAR constraints from the original 15 Gy plan on an individualized basis. METHODS AND MATERIALS: A total of 53 consecutive HDR-BT plans were retrospectively assessed to determine what percentage of plans could be reoptimized to deliver a dose of 16 Gy or 17 Gy, while meeting defined 15-Gy OAR constraints. Factors independently associated with dose escalation were examined. RESULTS: were independently predictive of the ability to dose escalate to 16 Gy. CONCLUSIONS: Individualized HDR-BT dose escalation beyond 15 Gy without compromising OAR constraints is dosimetrically feasible. This approach could allow for a corresponding reduction of EBRT fractions (ie, from 15 to 12 fractions) and would be beneficial in terms of resource savings for departments, convenience for patients, and potentially better tolerance of treatment with the expected reduction in biologically equivalent doses to OARs. A clinical trial is being developed to investigate the efficacy and tolerance of personalized HDR-BT/EBRT dose fractionation for localized intracapsular 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".