Performance characteristics of 18F-fluciclovine positron emission tomography/computed tomography prior to retroperitoneal lymph node dissection
Bibliographic record
Abstract
Introduction: We aimed to determine whether anti-1-amino-3-18F-fluorocyclobutane-1-carboxylic acid (18F-fluciclovine) positron emission tomography/computed tomography (PET/CT) can accurately detect residual non-seminomatous germ cell tumor (NSGCT) prior to retroperitoneal lymph node dissection (RPLND). There is no reliable way to differentiate between fibrosis/necrosis, teratoma, and viable germ cell tumor in patients receiving post-chemotherapy RPLND. Functional imaging, including 18F-fludeoxyglucose (18F-FDG) PET/CT, has been disappointing. Due to the need for better imaging modalities, our prospective, pilot study aims to investigate the accuracy of 18F-fluciclovine PET/CT in detecting residual tumor prior to RPLND. Methods: From March 2018 to May 2019, 10 eligible patients underwent preoperative 18F-fluciclovine PET/CT prior to undergoing bilateral, full-template RPLND or excision of mass (for one re-do RPLND) in a prospective, phase 2 study. Correlation between 18F-fluciclovine PET/CT and RPLND pathology were evaluated on a per-patient level. Results: A total of 10 patients (mean age 29±7.6 years) underwent 18F-fluciclovine PET/CT prior to surgery. Nine of 10 patients received chemotherapy prior to RPLND. Correlation between 18F-fluciclovine PET/CT and RPLND pathology was seen in 3/10 (30%) patients. Five of 10 patients (50%) with negative 18F-fluciclovine PET/CT were found to have residual disease/teratoma on RPLND. Compared to the reference standard of RPLND, 18F-fluciclovine PET/CT demonstrated 29% sensitivity and 33% specificity. No patients experienced any adverse events due to 18F-fluciclovine PET/CT. Conclusions: Despite a different mechanism of action from 18F-FDG, 18F-fluciclovine has low sensitivity and specificity for residual teratoma in the retroperitoneum.
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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 | 0.004 |
| 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) | 0.002 | 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".