Prostate-specific Membrane Antigen (PSMA): A Diagnostic and Therapeutic Target in Advanced Prostate Cancer
Bibliographic record
Abstract
Prostate cancer is among the most prevalent malignant conditions globally, and both incidence and mortality are expected to increase markedly over the next two decades. Recently, the diagnostic and treatment landscape for managing this disease underwent remarkable advances that led to the incorporation of innovative approaches, such as prostate-specific membrane antigen (PSMA) theranostics. PSMA, which is also known as folate hydroxylase or glutamate carboxypeptidase, is a transmembrane protein 100- to 1000-fold overexpressed by prostate cancer cells compared to healthy cells found in the benign prostate gland, salivary glands, proximal renal tubules, small intestine mucosa, and hepatocytes, amongst others. Since its discovery over 30 years ago (see Figure 1 for this and other milestones), PSMA has caught the attention of the scientific community as a potential therapeutic target, and for the past two decades many efforts have been undertaken to identify and develop PSMA ligands and antibodies that could be exploited as prostate cancer therapeutics. This review aims to provide an overview of available PSMA ligands, their mechanisms of action, diagnostic and therapeutic applications, and future perspectives of PSMA-targeted therapeutic approaches within the field of radioligand therapy (RLT).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".