Dual Somatostatin Receptor/<sup>18</sup>F-FDG PET/CT Imaging in Patients with Well-Differentiated, Grade 2 and 3 Gastroenteropancreatic Neuroendocrine Tumors
Bibliographic record
Abstract
Our purpose was to prospectively assess the distribution of NETPET scores in well-differentiated (WD) grade 2 and 3 gastroenteropancreatic (GEP) neuroendocrine tumors (NETs) and to determine the impact of the NETPET score on clinical management. Methods: This single-arm, institutional ethics review board–approved prospective study included 40 patients with histologically proven WD GEP NETs. 68Ga-DOTATATE PET and 18F-FDG PET were performed within 21 d of each other. NETPET scores were evaluated qualitatively by 2 reviewers, with up to 10 marker lesions selected for each patient. The quantitative parameters that were evaluated included marker lesion SUVmax for each tracer; 18F-FDG/68Ga-DOTATATE SUVmax ratios; functional tumor volume (FTV) and metabolic tumor volume (MTV) on 68Ga-DOTATATE and 18F-FDG PET, respectively; and FTV/MTV ratios. The treatment plan before and after 18F-FDG PET was recorded. Results: There were 22 men and 18 women (mean age, 60.8 y) with grade 2 (n = 24) or grade 3 (n = 16) tumors and a mean Ki-67 index of 16.1%. NETPET scores of P0, P1, P2A, P2B, P3B, P4B, and P5 were documented in 2 (5%), 5 (12.5%), 5 (12.5%) 20 (50%), 2 (5%), 4 (10%), and 2 (5%) patients, respectively. No association was found between the SUVmax of target lesions on 68Ga-DOTATATE and the SUVmax of target lesions on 18F-FDG PET (P = 0.505). 18F-FDG/68Ga-DOTATATE SUVmax ratios were significantly lower for patients with low (P1–P2) primary NETPET scores than for those with high (P3–P5) primary NETPET scores (mean ± SD, 0.20 ± 0.13 and 1.68 ± 1.44, respectively; P < 0.001). MTV on 18F-FDG PET was significantly lower for low primary NETPET scores than for high ones (mean ± SD, 464 ± 601 cm3 and 66 ± 114 cm3, respectively; P = 0.005). A change in the type of management was observed in 42.5% of patients after 18F-FDG PET, with the most common being a change from systemic therapy to peptide receptor radionuclide therapy and from debulking surgery to systemic therapy. Conclusion: There was a heterogeneous distribution of NETPET scores in patients with WD grade 2 and 3 GEP NETs, with more than 1 in 5 patients having a high NETPET score and a frequent change in management after 18F-FDG PET. Quantitative parameters including 18F-FDG/68Ga-DOTATATE SUVmax ratios in target lesions and FTV/MTV ratios can discriminate between patients with high and low NETPET scores.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".