Prostate volume changes during permanent seed brachytherapy: an analysis of intra-operative variations, predictive factors and clinical implication
Bibliographic record
Abstract
BACKGROUND: To determine prostate volume (Pvol) changes at 3 different time points during the course of I¹²⁵ permanent seed brachytherapy (PB). To assess the impact of these changes on acute urinary retention (AUR) and dosimetric outcome. METHODS: We analyzed 149 hormone-naïve patients. Measurements of the prostate volume were done using three-dimensional transrectal ultrasound (3D-TRUS) in the operating room before insertion of any needle (V1), after the insertion of 2 fixation needles with a harpoon (V2) and upon completion of the implant (V3). The quality of the implant was analyzed with the D90 (minimum dose in Grays received by 90% of the prostate volume) at day 30. RESULTS: Mean baseline prostate volume (V1) was 37.4 ± 9.6 cc. A volume increase of >5% was seen in 51% between V1-V2 (mean = 2.5 cc, p < 0.01), in 42% between V2-V3 (mean = 1.9 cc, p < 0.01) and in 71% between V1-V3 (mean = 4.5 cc, p < 0.01). Pvol changes caused by insertion of the fixation needles were not statistically different than those caused by the implant itself (p = 0.23).In multivariate linear regression analysis, baseline Pvol is predictive of Pvol changes between V2 and V1 and V3 and V1 but not between V3 and V2. The extent of prostate swelling had an influence on D90. An increase of 10% in prostate volume between V1 and V2 results in an increase of D90 at Day 30 by 11.7%. Baseline Pvol (V1) was the only predictor of the duration of urinary retention in both univariate and multivariate (p = 0.04) regression analysis. CONCLUSIONS: A large part of intraoperative swelling occurs already after the insertion of the fixation needles. This early prostate swelling predicts for D90 but not for AUR.
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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 | 0.000 | 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) | 0.001 | 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".