PET/CT using 18F-FDOPA provides improved staging of carcinoid tumor patients in a Canadian setting
Bibliographic record
Abstract
AIM: In Canada, staging of carcinoid tumors is largely based on computed tomography (CT) imaging sometimes complemented with somatostatin receptor scintigraphy (SRS). This study assessed the diagnostic accuracy of 6-[¹⁸F]fluoro-3,4-dihydroxyphenylalanine (¹⁸F-FDOPA) PET/CT in neuroendocrine tumors. METHODS: We prospectively included 27 patients with either suspected carcinoid (n=6, with all prior tests negative) or with an established diagnosis of intestinal carcinoid tumor (n=21) from two Canadian treatment centers. Findings of ¹⁸F-FDOPA PET/CT were compared with SRS, CT, and combined SRS/CT using a composite reference standard comprising all available imaging, biochemistry, surgery, and follow-up data. Sensitivity was calculated per patient, per body region, and per lesion. The contribution to patient management was estimated from the feedback of attending physicians. RESULTS: In documented carcinoid patients, ¹⁸F-FDOPA PET/CT identified disease in 20 of 21 patients (patient-based sensitivity 95%). In 56 positive regions, ¹⁸F-FDOPA PET/CT detected 53, CT detected 34, SRS detected 34, and CT+SRS detected 39 regions, leading to region-based sensitivities of 95, 61, 62, and 71%, respectively. Lesion-based sensitivities were 96, 69, 50, and 72%, respectively. In the six patients with suspected disease only, one CT scan was positive, but ¹⁸F-FDOPA PET/CT was negative for all. ¹⁸F-FDOPA PET contributed to patient management in 12/21 patients (57%). CONCLUSION: ¹⁸F-FDOPA PET/CT proved to be an excellent modality for staging of carcinoid tumor patients, with superior performance compared with currently applied methods in Canada. In patients with suspected disease with negative prior imaging investigations, ¹⁸F-FDOPA was not helpful.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".