Diagnostic Performance of 18F-DCFPyL-PET/CT in Men with Biochemically Recurrent Prostate Cancer: Results from the CONDOR Phase III, Multicenter Study
Bibliographic record
Abstract
Abstract Purpose: Current FDA-approved imaging modalities are inadequate for localizing prostate cancer biochemical recurrence (BCR). 18F-DCFPyL is a highly selective, small-molecule prostate-specific membrane antigen–targeted PET radiotracer. CONDOR was a prospective study designed to determine the performance of 18F-DCFPyL-PET/CT in patients with BCR and uninformative standard imaging. Experimental Design: Men with rising PSA ≥0.2 ng/mL after prostatectomy or ≥2 ng/mL above nadir after radiotherapy were eligible. The primary endpoint was correct localization rate (CLR), defined as positive predictive value with an additional requirement of anatomic lesion colocalization between 18F-DCFPyL-PET/CT and a composite standard of truth (SOT). The SOT consisted of, in descending priority (i) histopathology, (ii) subsequent correlative imaging findings, or (iii) post-radiation PSA response. The trial was considered a success if the lower bound of the 95% confidence interval (CI) for CLR exceeded 20% for two of three 18F-DCFPyL-PET/CT readers. Secondary endpoints included change in intended management and safety. Results: A total of 208 men with a median baseline PSA of 0.8 ng/mL (range: 0.2–98.4 ng/mL) underwent 18F-DCFPyL-PET/CT. The CLR was 84.8%–87.0% (lower bound of 95% CI: 77.8–80.4). A total of 63.9% of evaluable patients had a change in intended management after 18F-DCFPyL-PET/CT. The disease detection rate was 59% to 66% (at least one lesion detected per patient by 18F-DCFPyL-PET/CT by central readers). Conclusions: Performance of 18F-DCFPyL-PET/CT achieved the study’s primary endpoint, demonstrating disease localization in the setting of negative standard imaging and providing clinically meaningful and actionable information. These data further support the utility of 18F-DCFPyL-PET/CT to localize disease in men with recurrent prostate cancer. See related commentary by True and Chen, p. 3512
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".