18F-DCFPyL PET/CT in advanced high-grade epithelial ovarian cancer: A prospective pilot study
Bibliographic record
Abstract
Objectives Glutamate carboxypeptidase-II (GCP-II), a zinc metalloenzyme that resides in cell membrane, has been reported as overexpressed in the neovasculature of ovarian cancers. The study objective was to determine whether GCP-II targeted imaging with 18F-DCFPyL PET/CT can detect disease sites in women with advanced high-grade serous ovarian cancer (HGSOC). Materials and methods Twenty treatment-naïve women with advanced HGSOC were recruited (median age 60 years). Prior to commencing therapy (primary cytoreductive surgery [n=9] or neoadjuvant chemotherapy [n=11]), subjects underwent routine staging with contrast-enhanced abdominopelvic CT (=CT), followed by 18F-DCFPyL PET/CT (=PET). CT and PET were reported independently using a standardized reporting template assessing 25 sites. The performance of PET was compared to CT in all subjects and to surgery and surgical histopathology in 9 patients who underwent primary cytoreductive surgery. Results Of the 25 sites assessed in 20 patients, CT detected disease in 292/500 (58.4%) locations and PET detected disease in 171/500 (34.2%). Compared to CT the sensitivity (95% CI) of PET to detect disease in the upper abdomen, the gastrointestinal tract or the peritoneum was 0.29 (0.20,0.40), 0.21 (0.11,0.33) and 0.74 (0.64,0.82), respectively. In the surgical cohort, 220 sites in 9 patients were evaluated. The sensitivity and specificity of CT and PET were 0.85 versus 0.54 (p<0.001) and 0.73 versus 0.93 (p<0.001), respectively. Conclusion Although 18F-DCFPyL has higher specificity than CT in detecting advanced HGSOC tumor sites, it detects less disease sites than CT, especially in the upper abdomen and along the gastrointestinal tract, likely limiting its clinical utility. Clinical trial registration ClinicalTrials.gov , NCT03811899.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".