68Ga PET Imaging in Patients With Neuroendocrine Tumors
Bibliographic record
Abstract
PURPOSE: The aim of this study was to systematically review the literature to assess the role of Ga PET imaging in neuroendocrine tumors (NETs). MATERIALS AND METHODS: The literature was searched using MEDLINE, EMBASE, and Cochrane Database of Systematic Reviews databases through OVID. Studies comparing PET or PET/CT with conventional imaging in the initial diagnosis, staging and restaging, assessment of treatment response, and routine surveillance of NETs were deemed eligible for inclusion. Risk of bias and applicability concerns were assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. RESULTS: Twenty-two studies met the inclusion criteria. For the initial diagnosis of NETs, PET or PET/CT had a pooled sensitivity of 91% (95% confidence interval [CI], 85%-94%) and a pooled specificity of 94% (95% CI, 86%-98%). In the setting of staging and restaging, the sensitivity of PET or PET/CT for detecting primary and/or metastatic lesions ranged from 78.3% to 100%, whereas specificity ranged from 83% to 100%. Change in management occurred in 45% (95% CI, 36%-55%) of the cases, with majority of the changes involving surgical planning and patient selection for peptide receptor radionuclide therapy. CONCLUSIONS: Ga PET or PET/CT is recommended for initial diagnosis where conventional testing remained equivocal, for staging of patients with localized primary and/or limited metastasis where definitive surgery is planned, to determine somatostatin receptor status and suitability for peptide receptor radionuclide therapy, and for staging of patients where detection of occult disease will alter treatment options and decision making.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".