Biochemical recurrence after radical prostatectomy and postoperative radiotherapy: current evidence and controversial issues
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review explores challenges in managing biochemical recurrence (BCR) after radical prostatectomy and postoperative radiotherapy for prostate cancer (PCa) highlighting gaps in risk stratification, imaging, and emerging therapies, as well as advances in molecular imaging and personalized treatment. RECENT FINDINGS: Approximately half of PCa patients experience a second BCR after postoperative radiotherapy. Time to recurrence, PSA kinetics, adverse pathological features (ISUP 4-5, pT3-4, and positive surgical margins), alongside genetic profile, are key factors for risk stratification. Combination of androgen deprivation therapy (ADT) and novel androgen receptor pathway inhibitors (ARPIs) represents an established treatment choice. However, recent findings emphasize the growing role of prostate-specific membrane antigen (PSMA) PET in detecting recurrent disease and guide tailored strategies. Based on early phase II trials and retrospective studies, metastasis-directed therapy (MDT) has demonstrated promising efficacy in oligorecurrent PCa, although further validation is warranted. SUMMARY: BCR after radical prostatectomy and postoperative radiotherapy represents a challenge in PCa management. Risk stratification is key for guiding the addition of ARPIs to standard ADT. PSMA PET may further refine tailored strategies such as MDT, whose promising efficacy needs further exploration. Ongoing trials will clarify treatment sequencing and patient selection in the evolving paradigm of BCR management.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".