Prospective evaluation of the utility of concurrent 18F-FDG PET/CT and 68Ga-DOTA-TOC imaging in gastroenteropancreatic neuroendocrine neoplasms (GEPNENs): The PETNET study.
Bibliographic record
Abstract
4022 Background: Somatostatin receptor imaging (SRI) is a standard of care for patients with GEPNETs. The additional value of concurrent 18F-FDG PET/CT (FDG PET) remains unclear. We reviewed a prospective functional imaging study to determine the utility of FDG PET in GEPNENs. Methods: PETNET is a prospective study in British Columbia, Canada, which provides all 68Ga-DOTA-TOC (DOTA PET) imaging in the province. Every patient receives a DOTA PET scan and an FDG PET within 30 days. PETNET enrolls all patients with an indication for SRI. Scans are ordered per treating physician discretion at any point in the disease course. This abstract focuses on the WD-GEPNEN population. Only the first dual functional imaging scans were analyzed and FDG was interpreted qualitatively (positive/negative). Results: From 04/2017-01/2023, 375 patients with NEN were enrolled, 165 (44%) with metastatic GEPNENs. Baseline characteristics are described. Median time between scans was 4 days (IQR 1-11). The proportion of patients with positive FDG PET at baseline increased with WHO grade. For patients with well differentiated G1 to G3 GEPNENs (N=161), overall survival was significantly lower with a positive FDG PET (HR: 4.22; 95%CI 1.61-11.02 p=0.001). FDG remained prognostic when G3 tumors were excluded (N=148) (HR 3.52; 95%CI 1.32-9.42 p= 0.007). When analyzing dual tracer PET imaging, patients with DOTA+/FDG- had reduced risk of dying in comparison with DOTA+/FDG+ (HR:0.26; 95%CI 0.09-0.67 p=0.01). After multivariate analysis, FDG positivity remained independently associated with reduced survival (HR 2.87; 95%CI 1.06-7.75 p=0.04) when controlling for grade of tumor and age. Conclusions: In this prospective cohort of metastatic GEPNENs, a positive FDG PET was significantly associated with reduced overall survival. These results provide additional evidence to support dual tracer functional imaging use in metastatic well differentiated GEPNEN’s. [Table: see text]
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".