Multicenter Evaluation of Biochemical Relapse–Free Survival Outcomes for Intraoperatively Planned Prostate Brachytherapy Using an Automated Delivery System
Bibliographic record
Abstract
Purpose To report biochemical recurrence in prostate cancer treated with intraoperatively planned low-dose-rate prostate brachytherapy using an automated delivery system (IO-LDRB). Methods and Materials Between 2003 and 2013, 2608 patients from 3 centers were treated with IO-LDRB as single-modality treatment for low or low-tier intermediate-risk prostate cancer. Databases from the 3 centers have been analyzed. These independent databases were collected prospectively. Patient, tumor, and treatment characteristics were then compared, Kaplan-Meier survival estimates of biochemical relapse–free survival (bRFS) were generated, and the Cox proportional hazards model was used to determine factors predicting for relapse. Results A total of 2608 patients with a median follow-up of 4.7 (interquartile range, 3.1-6.9) years were analyzed. Median age was 64 (range, 42-84) years. In these patients, median initial prostate-specific antigen was 5.5 ng/mL, 74% were T1, and 26% were T2; 73% were Gleason 6, and 25% Gleason 7. Median percentage of biopsy cores positive was 33%, and median gland volume was 34.2 cm 3 . Eleven percent of patients received hormones for a median of 3.0 months before implantation. Median seed activity was 0.437 mCi, D90 (dose covering 90% of the prostate volume) was 186.7 Gy, and V100 was 99.37%. Biochemical relapse was observed in 124 patients (4.8%), and median time to failure was 4.0 years. Predicted bRFS was 93% at 7 years. On Cox regression bRFS was dependent only on D90 at the time of implantation and prostate-specific antigen density. Conclusions This study demonstrates that IO-LDRB is an effective treatment option for patients with low and low-tier intermediate-risk prostate cancer. Rates of biochemical relapse remain low several years after treatment. These results compared favorably to published manual preplan technique results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".