Dual PET Imaging with [68Ga]Ga-DOTA-TOC and [18F]FDG to Localize Neuroendocrine Tumors of Unknown Origin
Bibliographic record
Abstract
Neuroendocrine tumors of unknown primary (CUP-NET) present a diagnostic challenge when conventional imaging fails to localize the primary tumor. This study aimed to evaluate the diagnostic value of concurrent [68Ga]Ga-DOTA-TOC and [18F]FDG PET/CT imaging in localizing primary tumors in patients with histologically confirmed CUP-NET. Thirty-four patients underwent both imaging modalities as part of a prospective imaging protocol after negative conventional imaging or [111In]In-octreotide scintigraphy. Primary tumor detection rates were assessed, and imaging characteristics compared between the two modalities. The overall localization rate was 58.9% (20/34). Of these, 90% (18/20) of primary tumors were identified solely by [68Ga]Ga-DOTA-TOC PET/CT, with the remaining two visualized by both modalities. [18F]FDG PET/CT did not independently localize any primary tumors. Identified primaries were limited to grade 1 (60%) or grade 2 (40%) tumors, predominantly in the small intestine (95%). Among localized cases, 45% (9/20) underwent surgical resection and 15% (3/20) became eligible for peptide receptor radionuclide therapy. [68Ga]Ga-DOTA-TOC PET/CT demonstrated superior detection of metastatic lesions compared to [18F]FDG PET/CT (97.1% vs. 70.6%, p = 0.006). No significant survival differences were observed between patients with localized versus non-localized primaries. These findings support the value of [68Ga]Ga-DOTA-TOC PET/CT for identifying primary tumors in CUP-NET. Further research is warranted to explore the role of [18F]FDG PET/CT in high-grade NETs.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".