The Performance and Role of PSMA PET Scans in Localised Prostate Cancer
Bibliographic record
Abstract
Background/Objectives: Prostate cancer (PCa) is one of the most prevalent cancers in men. While PSA testing aids in early detection, it often identifies clinically insignificant PCa (ciPCa), which may not necessitate treatment. Prostate-specific membrane antigen (PSMA) PET scans have emerged as a promising tool to evaluate of localised PCa. This review aims to assess the current evidence of using PSMA PET scans for localised PCa. Methods: Peer-reviewed publications on PSMA PET scans in localised PCa, from inception to May 2024, were retrieved from PubMed. The outcomes evaluated included diagnostic performance in identifying intraprostatic lesions, detecting csPCa (ISUP GG ≥ 2), and role peri-treatment. Results: The addition of PSMA PET/CT to MRI improved the sensitivity (from 83% to 97%) and NPV (72% to 91%) of detecting csPCa. PSMA PET helped improve risk stratification in active surveillance by identifying MRI-occult lesions in up to 29% of patients, of which up to 10% may harbour underlying unfavourable pathology. In local staging, PSMA PET/MRI outperforms MRI in identifying extra-prostatic extension (77% vs. 73%) and seminal vesicle invasion (90% vs. 87%). PSMA PET scans are also superior to MRI in nodal staging and bone scans in identifying bony metastasis. PSMA PET scans appear useful in guiding treatment of localised PCa and aiding follow-up. Conclusions: PSMA PET scans are valuable for evaluating localised PCa by improving the detection of csPCa and enhancing local staging. However, most available studies are retrospective, and long-term oncological outcomes remain underreported due to the relative novelty of PSMA PET scans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".