PSMA Theranostics: Current Landscape and Future Outlook
Bibliographic record
Abstract
INTRODUCTION: Prostate-specific membrane antigen (PSMA) is a promising novel molecular target for imaging diagnostics and therapeutics (theranostics). There has been a growing body of evidence supporting PSMA theranostics approaches in optimizing the management of prostate cancer and potentially altering its natural history. METHODS: We utilized PubMed and Google Scholar for published studies, and clinicaltrials.gov for planned, ongoing, and completed clinical trials in PSMA theranostics as of June 2021. We presented evolving evidence for various PSMA-targeted radiopharmaceutical agents in the treatment paradigm for prostate cancer, as well as combination treatment strategies with other targeted therapy and immunotherapy. We highlighted the emerging evidence of PSMA and fluorodeoxyglucose (FDG) PET/CT as a predictive biomarker for PSMA radioligand therapy. We identified seven ongoing clinical trials in oligometastatic-directed therapy using PSMA PET imaging. We also presented a schematic overview of 17 key PSMA theranostic clinical trials throughout the various stages of prostate cancer. CONCLUSIONS: In this review, we presented the contemporary and future landscape of theranostic applications in prostate cancer with a focus on PSMA ligands. As PSMA theranostics will soon become the standard of care for the management of prostate cancer, we underscore the importance of integrating nuclear medicine physicians into the multidisciplinary team.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".