Prostate-Specific Membrane Antigen Ligand Positron Emission Tomography in Men with Nonmetastatic Castration-Resistant Prostate Cancer
Bibliographic record
Abstract
Abstract Purpose: Systemic androgen-signaling inhibition added to ongoing androgen-deprivation therapy (ADT) improved clinical outcomes in patients with nonmetastatic castration-resistant prostate cancer without detectable metastases by conventional imaging (nmCRPC). Prostate-specific membrane antigen ligand positron emission tomography (PSMA-PET) detects prostate cancer with superior sensitivity to conventional imaging, but its performance in nmCRPC remains largely unknown. We characterized cancer burden in high-risk patients with nmCRPC using PSMA-PET. Experimental Design: We retrospectively included 200 patients with nmCRPC, prostate-specific antigen (PSA) >2 ng/mL, and high risk for metastatic disease [PSA doubling time (PSADT) of ≤10 months and/or Gleason score of ≥8] from six high-volume PET centers. We centrally reviewed PSMA-PET detection rate for pelvic disease and distant metastases (M1). We further evaluated SPARTAN patients stratified by risk factors for PSMA-PET-detected M1 disease. Results: PSMA-PET was positive in 196 of 200 patients. Overall, 44% had pelvic diseases, including 24% with local prostate bed recurrence, and 55% had M1 disease despite negative conventional imaging. Interobserver agreement was very high (κ: 0.81–0.91). PSA ≥ 5.5 ng/mL, locoregional nodal involvement determined by pathology (pN1), prior primary radiation, and prior salvage radiotherapy independently predicted M1 disease (all P < 0.05). Conclusions: PSMA-PET detected any disease in nearly all patients and M1 disease in 55% of patients previously diagnosed with nmCRPC, including subgroups with PSADT of ≤10 months and Gleason score of ≥8. The value of PSMA-PET imaging for treatment guidance should be tested in future studies.
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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 | 0.001 |
| 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) | 0.001 | 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".