Prostate-specific antigen density does not predict metastatic disease on PSMA-PET in high-risk prostate cancer patients with negative conventional imaging
Bibliographic record
Abstract
INTRODUCTION: The ability of prostate-specific antigen density (PSAD) to predict metastatic disease on prostate-specific membrane antigen-positron emission tomography (PSMA-PET) at initial staging in high-risk prostate cancer (PCa) for men with negative conventional imaging is unclear. We hypothesized that there might be a PSAD cutoff below which PSMA-PET would be unnecessary, as it would so rarely identify metastatic disease. METHODS: F-DCFPyl PSMA-PET for primary staging between January 2018 and December 2022 at the University Health Network was performed. Student's t-tests or Mann-Whitney U tests were used to compare continuous variables by PSMA-PET positivity status. Receiver operating characteristic curve analysis to compare PSA and PSAD performance and Chi-squared automatic interaction detector methodologies were used to identify predictors of metastatic disease. RESULTS: (IQR 0.19-0.83), respectively. PSMA-PET was positive in 40% of cases for metastatic disease. The area under the curve (AUC) to predict metastatic disease on PSMA-PET was 0.55 for PSAD (p=0.57). Patients with metastatic disease on PSMA-PET had higher Gleason grade group (GG) scores on biopsy (53 vs. 20% GG5, p<0.001) and more extraprostatic extension (19 vs. 6%, p=0.03) and perineural invasion (65 vs. 45%, p=0.03). CONCLUSIONS: In this retrospective cohort, PSAD does not reliably predict which patients with high-risk PCa and negative conventional imaging will have metastatic disease unveiled by PSMA-PET.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".