Is intraoperative real-time dosimetry in prostate seed brachytherapy predictive of biochemical outcome?
Bibliographic record
Abstract
Purpose: To analyze intraoperative (IO) dosimetry using transrectal ultrasound (TRUS), performed before and after prostate low-dose-rate brachytherapy (LDR-BT), and compare it to dosimetry performed 30 days following the LDR-BT implant (Day 30). Material and methods:A total of 236 patients underwent prostate LDR-BT using 125 I that was performed with a three-dimensional TRUS-guided interactive inverse preplanning system (preimplant dosimetry).After the implant procedure, the TRUS was repeated in the operating room, and the dosimetry was recalculated (postimplant dosimetry) and compared to dosimetry on Day 30 computed tomography (CT) scans.Area under curve (AUC) statistics was used for models predictive of dosimetric parameters at Day 30.Results: The median follow-up for patients without BF was 96 months, the 5-year and 8-year biochemical recurrence (BR)-free rate was 96% and 90%, respectively.The postimplant median D 90 was 3.8 Gy lower (interquartile range [IQR], 12.4-0.9),and the V 100 only 1% less (IQR, 2.9-0.2%)than the preimplant dosimetry.When comparing the postimplant and the Day 30 dosimetries, the postimplant median D 90 was 9.6 Gy higher (IQR [-] 9.5-30.3Gy), and the V 100 was 3.2% greater (0.2-8.9%) than Day 30 postimplant dosimetry.The variables that best predicted the D 90 of Day 30 was the postimplant D 90 (AUC = 0.62, p = 0.038).None of the analyzed values for IO or Day 30 dosimetry showed any predictive value for BR. Conclusions:Although improving the IO preimplant and postimplant dosimetry improved dosimetry on Day 30, the BR-free rate was not dependent on any dosimetric parameter.Unpredictable factors such as intraprostatic seed migration and IO factors, prevented the accurate prediction of Day 30 dosimetry.
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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 | 0.006 |
| 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".