Prostate-specific membrane antigen for prostate cancer theranostics: from imaging to targeted therapy
Bibliographic record
Abstract
PURPOSE OF REVIEW: In recent years, major advances in molecular imaging of prostate cancers (PCa) were made with the development and clinical validation of highly accurate PET tracers to stage and restage the disease. Prostate-specific membrane antigen (PSMA) is a transmembrane protein highly expressed in PCa, and its expression has led to the development of PSMA-binding radiopharmaceuticals for molecular imaging or radioligand therapy (RLT). We herein review the recent literature published on diagnostic and therapeutic (i.e. theranostic) PSMA tracers. RECENT FINDINGS: Development in small PSMA-targeted molecules labeled with gallium-68 and fluorine-18 show promising results for primary staging and detection of disease at biochemical recurrence using PET/computed tomography (PET/CT). Studies show a higher sensitivity and specificity, along with an improved detection rate over conventional imaging (CT scan and bone scan) or choline PET tracers, especially for restaging after prostate-specific antigen failure following loco-regional therapy. In addition, some PSMA tracers can be labeled with beta-minus and alpha particle emitters, yielding encouraging response rates and low toxicity, and potentially offering a new line of targeted therapy for metastatic castration-resistant PCa. SUMMARY: PSMA-targeted tracers have shown unprecedented accuracy to stage and restage PCa using PET/CT. Given their specific biodistribution toward PCa tissue, PSMA RLT now offers new therapeutic possibilities to target metastatic PCa. Prospective multicenter randomized studies investigating the clinical impact of PSMA-targeted molecules are urgently needed.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".