Role for <sup>11</sup>C-choline PET in active surveillance of prostate cancer
Bibliographic record
Abstract
INTRODUCTION: Active surveillance (AS) is an increasingly popular management strategy for men diagnosed with low-risk indolent prostate cancer. Current tests (prostate-specific antigen [PSA], clinical staging, and prostate biopsies) to monitor indolent disease lack accuracy. (11)C-choline positron emission tomography (PET) has excellent detection rates in local and distant recurrence of prostate cancer. We examine (11)C-choline PET for identifying aggressive prostate cancer warranting treatment in the AS setting. METHODS: In total, 24 patients on AS had clinical assessment and PSA testing every 6 months and (11)C-choline PET and prostate biopsies annually. The sensitivity and specificity to identify prostate cancer and progressive disease (PD) were calculated for each (11)C-choline PET scan. RESULTS: In total, 62 biopsy-paired, serial (11)C-choline PET scans were analyzed using a series of standard uptake value-maximum (SUVmax) cut-off thresholds. During follow-up (mean 25.3 months), 11 of the 24 low-risk prostate cancer patients developed PD and received definitive treatment. The prostate cancer detection rate with (11)C-choline PET had moderate sensitivity (72.1%), but low specificity (45.0%). PD prediction from baseline (11)C-choline PET had satisfactory sensitivity (81.8%), but low specificity (38.5%). The addition of clinical parameters to the baseline (11)C-choline PET improved specificity (69.2%), with a slight reduction in sensitivity (72.7%) for PD prediction. CONCLUSIONS: Addition of (11)C-choline PET imaging during AS may help to identify aggressive disease earlier than traditional methods. However, (11)C-choline PET alone has low specificity due to overlap of SUV values with benign pathologies. Triaging low-risk prostate cancer patients into AS versus therapy will require further optimization of PET protocols or consideration of alternative strategies (i.e., magnetic resonance imaging, biomarkers).
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".