Incidentalomas of the prostate detected by 18-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography
Bibliographic record
Abstract
INTRODUCTION: Prostate incidentalomas are prostatic lesions suspicious for cancer discovered by imaging patients without a known history of prostatic cancer (PCa) for other reasons. 18-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (FDG PET) is used to diagnose, stage, and assess response to treatment for numerous cancers, but it is not routinely used for PCa. We aimed to determine the rate of detection of prostate incidentalomas in patients undergoing FDG PET and to evaluate the natural history of these lesions. METHODS: A retrospective review was conducted of all FDG PET scans performed between 2005 and 2017 at a single institution. Patients were selected who had prostatic uptake without a history of PCa. Clinical data were collected from electronic medical records to determine how the prostate incidentalomas were further evaluated and to define the rate of malignancy. RESULTS: A prostate incidentaloma was identified in 309 (1.0%) of 31 019 FDG PET scans performed on men. A prostate-specific antigen (PSA) test was obtained in 40.1% of patients within six months of prostate incidentaloma detection. Six patients underwent a multiparametric magnetic resonance imaging (mpMRI) of the prostate, which identified PCa in one case. Overall, PCa was diagnosed in 33 cases, representing 10.7% of the prostate incidentalomas and 0.1% of the scanned patients. PCa was intermediate- or high-risk in 27 (8.7%) of the prostate incidentalomas. CONCLUSIONS: Incidental lesions detected in the prostate by FDG PET may represent clinically significant PCa. Referral to a urologist for further evaluation should be considered if the patient is otherwise in reasonable health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".