68Ga-DOTATATE PET/CT in the Initial Diagnosis of Patients With Clinical, Imaging, and/or Biochemical Suspicion of a Neuroendocrine Tumor
Bibliographic record
Abstract
PURPOSE: The aim of this study was to assess the yield of somatostatin receptor PET in patients with clinical, imaging, and/or biochemical suspicion of a neuroendocrine tumor (NET). PATIENTS AND METHODS: This analysis includes patients referred for the initial diagnosis of an unconfirmed NET, as part of a prospective, single-arm registry study (NCT03873870) assessing the utility of 68 Ga-DOTATATE PET/CT in the management of NETs. Inclusion criteria to this cohort consisted of elevated biomarkers and/or clinical presentation suspicious for a NET, with negative conventional cross-sectional imaging, or presence of a lesion suspicious for a NET on conventional imaging, not amenable for biopsy. Patients with histological confirmation of a NET were excluded. RESULTS: There were 220 patients included between April 2019 and March 2022 with a mean age ± SD of 59.5 ± 16.1 years with biochemical, morphological, and/or clinical suspicion of a NET. Overall, 132/220 patients (60%) had a positive 68 Ga-DOTATATE PET/CT. 68 Ga-DOTATATE PET/CT confirmed a type 2 somatostatin receptor overexpressing tumor in 123/171 (71.9%) of patients with a radiographically suspicious abnormality. The positivity rate for pancreatic, small bowel/mesenteric, adrenal, and other sites was 78/96 (81.2%), 38/57 (66.7%), 7/7 (100%), and 1/11 (9.1%), respectively. 68 Ga-DOTATATE PET/CT was positive in 9/49 (18.4%) of those with a biochemical and/or clinical suspicion of a NET. CONCLUSIONS: 68 Ga-DOTATATE PET/CT is positive in nearly 3 of 4 patients with morphological suspicion of a NET, with the highest yield in those with pancreatic and small bowel or mesenteric masses, and in approximately 1 of 6 patients with biochemical and/or clinical suspicion of a NET.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".