18F-FDG PET-CT in Cystic Tumors of the Pancreas
Bibliographic record
Abstract
In order to plan treatment or follow-up of pancreatic cystic lesions, is crucial to distinguish benign from malignant cystic tumors with reliable, non-invasive methods. Despite evaluation with several preoperative investigations, a correct pathologic diagnosis rate does not exceed 68%. 2-[18F]-fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG PET-CT) has been found to be a highly sensitive and specific non-invasive procedure to detect malignancy in cystic tumors of the pancreas (CTP). The introduction of hybrid PET/CT scans allowed a more accurate localization of the foci of hypermetabolism. We reviewed 14 series (645 patients) with a CTP who underwent 18F-FDG PET-CT from 2001; four of these studies came from our group of investigators (226 patients). In the last studies, sensitivity in detecting malignancy ranged from 83 to 100% and specificity from 78 to 100%. 18F-FDG PET-CT for a long time was used only when conventional imaging was insufficient to rule out a cancer. In our experience, 18F-FDG PET-CT was found to be reliable to detect "cancer in situ" when no other investigations could detect it, so we stress the use of 18F-FDG PET-CT in the first assessment, as alternative to EUS with FNA, to exclude malignancy. We are lacking data about the use of 18F-FDG PET-CT and timing in the follow-up of patients (un)-operated. We suggest a regular use in the follow up of patients with intraductal papillary mucinous neoplasms (IPMN), due to their multifocality and to the high rate of extra-pancreatic cancers.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
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".