The impact of prostate-specific membrane antigen-positron emission tomography imaging on oncologic outcomes in prostate cancer patients with biochemical recurrence after radical local therapy: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Prostate-specific membrane antigen (PSMA)-positron emission tomography (PET) is increasingly utilized in prostate cancer patients upon biochemical recurrence (BCR) to facilitate the planning of salvage therapy. However, the impact of PSMA-PET imaging on oncologic outcomes is still poorly understood. To evaluate the impact of PSMA-PET on oncological outcomes of salvage therapy in patients with BCR following radical treatment with curative intent for clinically non-metastatic prostate cancer.EVIDENCE ACQUISITION: MEDLINE (via PubMed), Web of Science Core Collection, and SCOPUS databases were screened through May 2024, for prospective and retrospective studies investigating the use of PSMA-PET to guide salvage therapy upon BCR following primary radical treatment. The review protocol was registered in PROSPERO (CRD42024509298). The primary outcome was the biochemical progression-free survival (BCP-FS).EVIDENCE SYNTHESIS: Overall, we pooled data from 16 studies comprising 3081 patients; 92% underwent primary radical prostatectomy. The estimated 3-year BCP-FS following salvage therapy was 76% (95% CI: 67-85%) in patients with negative PSMA-PET, 79% (95% CI: 70-89%) in patients with local recurrences, 63% (95% CI: 52-74%) in cases of regional lymph node metastases, and 32% (95% CI: 15-49%) in patients presenting distant metastases on PSMA-PET. The estimated 1- and 2-year BCP-FS showed a similar trend as 3-year BCP-FS.CONCLUSIONS: Patients with local recurrence on PSMA-PET imaging have a similar prognosis to those with PSMA-PET-negative disease but with worse BCP-FS in patients found to harbor nodal or distant metastases. PSMA-PET seems to help stratify BCP-FS estimates based on recurrence sites.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.027 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".