The diagnostic accuracy and value of 18F-FDOPA PET/CT in assessment of neuroendocrine tumours of gastroenteropancreatic and thoracic origin
Bibliographic record
Abstract
1504 Objectives Neuroendocrine tumours (NET) represent a widely heterogeneous group of tumours, with the majority arising from the gastrointestinal tract or lung. This study is aimed at evaluating the clinical utility of 18F-FDOPA PET/CT in suspected or proven NET arising from these sites. Methods 70 patients with suspected or proven gastroenteropancreatic or thoracic NET were assessed with 18F-FDOPA PET/CT. Patients who had more than one 18F-FDOPA PET/CT scan only had the initial study analysed. The findings at 18F-FDOPA PET/CT were compared to a composite reference standard including conventional anatomical and functional imaging findings, histological results and clinical follow up. The clinical impact of 18F-FDOPA PET/CT findings in patient management was assessed. Results The overall sensitivity, specificity and diagnostic accuracy of 18F-FDOPA PET/CT in detection of disease was 88%, 100% and 91% respectively. 18F-FDOPA PET/CT correctly identified disease in 43 of 70 patients and was correctly negative in 21 of 70 patients. False negative 18F-FDOPA PET/CT was seen in 6 patients who had a NET arising from the foregut; 2 patients had FDOPA negative metastases and 4 patients had a FDOPA negative primary. There were no false positives. There were 6 patients who had negative findings on conventional anatomical or functional imaging but 18F-FDOPA PET/CT was pursued based on high clinical suspicion of disease. All 6 patients demonstrated FDOPA positive disease. In patients where a primary site was not previously known, 18F-FDOPA PET/CT identified an occult primary in 7 of 14 patients (50%). In 24 of the 70 patients (34%), the 18F-FDOPA PET/CT findings resulted in a relevant change in clinical management. Conclusions 18F-FDOPA PET/CT is a valuable imaging tool with high sensitivity, specificity and diagnostic accuracy for assessment of gastroenteropancreatic and thoracic NET. It also has high clinical impact and results in a relevant change in management in over a third of patients.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".