Current Status of PSMA-Radiotracers for Prostate Cancer: Data Analysis of Prospective Trials Listed on ClinicalTrials.gov
Bibliographic record
Abstract
The recent development of dedicated prostate-specific membrane antigen (PSMA) targeted radioligands shows the potential to change and improve the diagnosis and therapy of prostate cancer. There is an increasing number of prospective trials to further establish these tracers in the clinical setting. We analyzed data from the ClinicalTrials.gov registry including all listed prospective trials with PSMA-ligands for prostate cancer as of October 2019 concerning the different tracers and study characteristics. We found n = 104 eligible studies with a total of n = 25 different tracers in use: most frequently [68Ga]Ga-PSMA-11 (32%), followed by [18F]DCFPyL (24%) and [177Lu]Lu-PSMA-617 (10%). 85% are single-center, 15% multi-center studies. 95% national and 5% international studies. 34% are phase-II, 24% phase-I, 13% phase-I/-II, 12% phase-II/-III and phase-III and 7% early-phase-I. The primary purpose was classified as diagnostic in 72% of cases and therapeutic in 23% of cases. Most studies were executed in the USA (70%), followed by Canada (13%) and France (6%). This quantitative descriptive registry analysis indicates the rapid and global clinical developments and current status of PSMA-radioligands with emphasis on radiopharmaceutical and organizational aspects. It will be very interesting to see which tracers will prevail in the clinical setting.
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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 | 0.105 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".