Prostate-Specific Membrane Antigen Ligand Positron Emission Tomography in Men with Nonmetastatic Castration-Resistant Prostate Cancer
Why this work is in the frame
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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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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it