18F-fluorocholine positron emission tomography-computed tomography (18F-FCH PET/CT) for staging of high-risk prostate cancer patients
Bibliographic record
Abstract
INTRODUCTION: F-FCH PET/CT to conventional imaging modalities and to evaluate its clinical impact. METHODS: F-FCH PET/CT for initial staging of high-risk prostate cancer. Using pre-established validation criteria, sensitivity and specificity were determined for metastatic disease. Results were compared to findings on magnetic resonance imaging (MRI), computed tomography (CT), and bone scan (BS) when available. RESULTS: Twenty-two (29%) PET/CT scans were positive, 49 (64%) negative, and five (7%) equivocal for nodal or metastatic disease. Of the positive scans, 17 showed regional lymph node involvement, 12 distant nodes, five bone metastases, and three lung metastases. Overall per-patient sensitivity, specificity, positive and negative predictive values for metastatic disease were 65%, 100%, 100%, and 78%, respectively. Sensitivity, specificity, and positive and negative predictive values were 64%, 100%, 100%, and 80%, respectively, for nodal involvement and 86%, 100%, 100%, and 98%, respectively, for bone and other metastases. Conventional imaging was negative for the lesion(s) found on PET/CT in five patients. PET/CT changed the clinical management in nine patients (12%). CONCLUSIONS: F-FCH PET/CT offers some benefits over conventional imaging and demonstrates a high specificity, it remains limited by its sensitivity in the context of high-risk prostate cancer staging. PET with novel urea-based small molecule prostate-specific membrane antigen (PSMA) inhibitors may overcome some of these limitations. However, the interpretation of the study result is limited by the lack of available histological gold standard, the inclusion of several patients who received androgen-deprivation therapy (ADT) prior to PET/CT, our retrospective design, and a relatively small sample size.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".