Prostate-specific Membrane Antigen Positron Emission Tomography–detected Disease Extent and Overall Survival of Patients with High-risk Nonmetastatic Castration-resistant Prostate Cancer: An International Multicenter Retrospective Study
Bibliographic record
Abstract
Previously, we demonstrated that prostate-specific membrane antigen positron emission tomography (PSMA-PET) revealed distant metastases in 109/200 patients (39% distant nodes, 24% bone, and 6% visceral organ) with nonmetastatic castration-resistant prostate cancer (nmCRPC) and high-risk features (International Society of Urological Pathology score ≥4 and/or prostate-specific antigen doubling time ≤10 mo) without metastases by conventional imaging. However, the impact of disease extent determined by PSMA-PET on patient outcomes is unknown. We followed these 200 patients for a median of 43 mo after PSMA-PET and retrospectively assessed the association between patient characteristics, PSMA-PET findings, treatment management, and outcomes using a Kaplan-Meier model and Cox multivariable regressions. Among assessed disease characteristics, polymetastatic disease (five or more distant lesions on PET) was independently associated with shorter overall survival (OS; median 61 mo vs not reached; hazard ratio [95% confidence interval], 1.81 [1.00-3.27]; p = 0.050) and time to new metastases (median 38 vs 60 mo; 1.80 [1.10-2.96]; p = 0.019), and initial pN1 status with shorter OS (55 mo vs not reached; 1.94 [1.12-3.37]; p = 0.019). Following PSMA-PET, locoregional salvage therapies were used most commonly in no/local disease (58%), and androgen receptor signaling inhibitors were used in distant metastatic disease (51%). PSMA-PET provides additional risk stratification for patients with nmCRPC. Polymetastatic disease (five or more distant lesions) is associated with worse outcomes. PATIENT SUMMARY: A novel sensitive imaging technology, called prostate-specific membrane antigen positron emission tomography (PSMA-PET), allows doctors to detect the spread of prostate cancer, known as distant metastases, earlier and more accurately than in the past. In our study, PSMA-PET detected none to many metastases in patients who were considered free of distant metastasis by conventional imaging. These findings predicted outcomes and were used to select appropriate treatment.
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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 | 0.001 |
| 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".