What’s to come in PSMA therapies and diagnostics: A summary of clinical trials involving PSMA radioligand-based therapeutic and/or diagnostic approaches with active recruitment
Bibliographic record
Abstract
Prostate-Specific Membrane Antigen (PSMA)-based diagnostics and therapeutics are proving highly valuable in identifying disease sites and providing targeted radioligand therapy (RLT) for disseminated disease in prostate cancer (PC). With successful integration of these tools in limited PC presentations, there is a real need and excitement for trials testing PSMA-based approaches more broadly. We review the ongoing trials registered on ClinicalTrials.gov which aim to evaluate PSMA-PET or PSMA-RLT applications. We outline clinical contexts which have significant ongoing study and therefore may see imminent change, as well as contexts which are lacking in study in the hopes of guiding future research. Trials examining intensification strategies through targeted radiotherapy, combination systemic therapies, and RLTs have the potential to demonstrate improved clinical outcomes using PSMA-PET CT for guidance. We expect that PSMA-PET will become fundamental in the work-up of patients before targeted radiotherapy or surgery. The results of ongoing trials will likely clarify the benefits of PSMA-RLT in metastatic PC including in oligometastatic and hormone-sensitive disease; however, there is a sparsity of trials evaluating PSMA-RLT outside of metastatic PC. Clinical trials with PSMA PET/CT as an endpoint for disease control are emerging and standardized reporting and metrics for PSMA staging and response will facilitate the inclusion of PSMA PET endpoints into therapeutic trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".