<sup>68</sup>Ga‐prostate‐specific membrane antigen (PSMA) PET/CT as a clinical decision‐making tool in biochemically recurrent prostate cancer
Bibliographic record
Abstract
Abstract Objective PSMA PET/CT has demonstrated superior sensitivity over conventional imaging in the detection of local and distant recurrence in biochemically relapsed (BCR) prostate cancer. We prospectively investigated the management impact of68Ga‐PSMA PET/CT imaging in men with BCR, with the aim of identifying baseline clinicopathological predictors for management change. Patients and methods Men with BCR who met eligibility criteria underwent68Ga‐PSMA‐11 PET/CT at Monash Health (Melbourne, Australia). Intended management plans were prospectively documented before and after68Ga‐PSMA PET/CT imaging. Binary logistic regression analysis was performed to identify potential clinicopathological predictors of management change. Descriptive statistics were used to characterize the nature of these changes. Results Seventy men underwent68Ga‐PSMA‐11 PET/CT imaging. Median age was 67 years (IQR 63–72) and median PSA was 0.48 ng/ml (IQR 0.21–1.9). PSMA‐avid disease was observed in 56% (39/70) of patients. Pre‐scan management plan was altered following scanning in 43% (30/70) of patients. Management changes were significantly more common in patients with higher baseline PSA levels (PSA≥2 ng/ml,p = 0.01). 18/36 (50%) of the patients initially planned for watchful waiting had their management changed, including the use of salvage pelvic radiotherapy (n = 7) and stereotactic ablative body radiotherapy to oligometastatic disease (n = 6). Conclusion Management change after68Ga‐PSMA PET/CT for BCR is common and typically resulted in treatment intensification strategies in those planned for a watchful waiting approach. This study adds to the growing pool of evidence supporting the clinical utility of PSMA PET/CT imaging in the care of patients with BCR after definitive therapy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".