Fine Needle Aspiration Biopsy for Preoperative Workup of Pancreatic Cystic Neoplasms
Bibliographic record
Abstract
BACKGROUND: Cystic neoplasms of the pancreas comprise a pathologically heterogeneous group of lesions that usually present with similar, nonspecific clinical features. Based on the diagnosis, treatment varies from watchful observation of the lesion to total surgical resection of the pancreas. Therefore the importance of a precise and accurate diagnosis on fine needle aspiration (FNA) biopsy cannot be overemphasized from the patient management standpoint. There is debate regarding the accuracy of FNA diagnosis of cystic lesions of the pancreas. We report 4 cases and review the literature to explore and highlight the cytologic findings and diagnostic pitfalls that may help the cytopathologist accurately distinguish mucinous cystic neoplasm (MCN), intraductal papillary mucinous neoplasm (IPMN), serous cystadenoma (SCA) and ductal adenocarcinoma (DAC). CASES: We present 4 cases of patients with abdominal masses who underwent either computed tomography (CT)-guided or endoscopic ultrasound (EUS)-guided FNA biopsy as preoperative workup. Based on the cytologic diagnosis, the patients underwent surgery. CONCLUSION: Our cases illustrate the cytologic criteria that help the cytopathologist distinguish among MCN, IPMN, SCA and DAC. Correlation with clinical and radiologic findings is strongly advocated for accurate diagnosis. We describe the diagnostic pitfalls frequently encountered in these cases and how to avoid them.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".