Diagnostic performance of 18F-DCFPyL positron emission tomography/computed tomography for biochemically recurrent prostate cancer and change-of-management analysis
Bibliographic record
Abstract
Introduction: Conventional imaging (CI) performs poorly to identify sites of disease in biochemically recurrent prostate cancer. 68Ga-PSMA-11 positron emission tomography/ computed tomography (PET/CT) is most studied but has a very short half-life. This study reports the diagnostic performance of the novel prostate-specific membrane antigen (PSMA) radiotracer 18F-DCFPyL using real-life data, and tumor board simulation to estimate the impact of 18F-DCFPyL PET on patient management. Methods: Ninety-three 18F-DCFPyL PET/CT scans performed for patients previously treated for prostate cancer with a rising prostate-specific antigen (PSA) were retrospectively compared to contemporary CI, and clinical, imaging and PSA followups. A chart review was performed to document prior imaging, pathology results, serial serum PSA measurements, and other pertinent clinical data. Clinical utility of 18F-DCFPyL PET was measured using a simulated tumor board formed by three physicians with extensive prostate cancer experience deciding on management with and without knowledge of PET/CT results. Results: At median PSA 2.27 (interquartile range [IQR] 5.27], 82% of 18F-DCFPyL PET/CT demonstrated at least one site of disease: non-regional lymph nodes (37% of scans), regional lymph node metastases (28%), local recurrence (27%), bone metastases (20%), with higher PET positivity at higher PSA. Compared to 18F-DCFPyL PET/CT, CI showed overall poor performance, with accuracy below 20% for all extent of disease. PET/CT changed management in 44% of cases. The most frequent scenario was a radical change from initiating androgen deprivation therapy (ADT) to stereotactic body radiotherapy (SBRT) of oligo-lesional disease. In univariate and multivariate analysis, no patient characteristic could predict change of management by PET/CT results. Conclusions: 18F-DCFPyL significantly outperforms CI in recurring prostate cancer and is likely to impact management.
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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 | 0.002 | 0.007 |
| 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 | 0.000 | 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".