Bibliographic record
Abstract
PURPOSE OF REVIEW: Prostate cancer (PCa) is the most commonly diagnosed noncutaneous cancer and second leading cause of death in men. Imaging evaluation of PCa is challenging because of the prostate's deep pelvic location, its complex zonal anatomy and its multifocal nature. Novel imaging modalities are needed to improve detection, reassessment in biochemical relapse, and disease progression in advanced metastatic stages. RECENT FINDINGS: Current imaging modalities have distinct strengths. However, all lack the ability to diagnose micrometastases, differentiate high from low-grade disease and diagnose advanced disease, especially at low prostate specific antigen values. There is a need to combine the existing imaging methods with concepts utilizing tumor biology to differentiate biologically aggressive from indolent tumors. PET imaging with novel tracers facilitate improved imaging of PCa, but also usher in new compounds that could be useful for directing treatment as well. Most tracers have limited sensitivity, with the exception of prostate-specific membrane antigen (PSMA)-targeting tracers, that offer relatively higher sensitivity and specificity. SUMMARY: PSMA-PET appears promising in improving the imaging yield particularly in recurrent and advanced disease states. Incorporating PSMA-PET in these settings could open or prolong windows along the trajectory of the disease that could allow new treatments or more effective use of currently existing treatments. Prospective studies focusing on novel imaging enhancement and integration with therapeutic applications are needed.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".