68Ga-PSMA-PET/CT Ηas a Role in Detecting Prostate Cancer Lesions in Patients with Recurrent Disease
Bibliographic record
Abstract
BACKGROUND/AIM: Early detection of recurrent πrostate cancer (PCa) lesions is paramount to allow patients to avail of localised salvage therapy options. The most significant reason for failure of salvage therapy is undetected metastatic disease. This demonstrates the need for a more accurate monitoring tool. The prostate-specific membrane antigen (PSMA) is increasingly investigated as a novel tracer for gallium 68 PET/CT to detect PCa lesions in patients with recurrent disease. MATERIALS AND METHODS: Ga-PSMA-PET/CT in detecting PCa lesions. Studies were analysed with regards to image analysis, sensitivity, specificity and detection rates; compared to conventional methods and with the effects of contributing characteristics. RESULTS: Ga-PSMA-PET/CT was associated with sensitivity and specificity values of 33-93%, and >99% respectively. The tracer produced excellent contrast 1 h post injection. Probability of detection increases with increasing prostate-specific antigen (PSA), and at low PSA levels, is greater than that of current choline tracers. Early detection of lesions by the tracer allows alterations in follow up treatment. However, detectability may be affected by tracer trapping, androgen deprivation therapy and levels of PSMA expression. CONCLUSION: Ga-PSMA PET/CT shows promise as a tool for the detection of PCa lesions in patients with suspected recurrence. However further studies with more reports on sensitivity and specificity with longer follow-up times are 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".