Dosimetric Comparison of VMAT Alone and VMAT with HDR Brachytherapy Boost Using Clinical and Biological Dose Models in Localized Prostate Cancer
Bibliographic record
Abstract
BACKGROUND: Combining external beam radiotherapy (EBRT) with high-dose-rate (HDR) brachytherapy (BT) enables biologically effective dose escalation in prostate cancer. However, comparative evaluation of such regimens using radiobiological modeling remains limited. METHODS: Dose regimens based on clinical practice were analyzed using α/β values of 1.5 and 3 Gy for the prostate. Ten patients with available planning CT, pelvic MRI, and ultrasound-guided BT plans were retrospectively evaluated. Physical and biological dose distributions were recalculated for various EBRT and HDR-BT combinations. Biological effective dose (BED) values were determined for the prostate and organs at risk (OARs: anterior rectal wall, bladder base, urethra). Regimens yielding the highest ΔBED between prostate and OARs were considered most favorable. RESULTS: All regimens met clinical dose constraints. The most favorable ΔBED profiles for bladder and rectum were observed with HDR-BT regimens (2 × 15 Gy) combined with either 23 × 2 Gy or 15 × 2.5 Gy EBRT, independent of the assumed α/β value. EBRT-only regimens achieved superior urethral sparing, while higher HDR doses led to increased urethral exposure. CONCLUSIONS: This study underscores the value of radiobiological modeling in differentiating and optimizing prostate cancer radiotherapy strategies. While the trade-offs between dose escalation and OAR sparing are clinically known, our biologically driven analysis provides a more quantitative foundation for selecting and tailoring combined EBRT/HDR-BT regimens in practice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".