A277 DISCREPANCIES IN EUS-FNA CYTOPATHOLOGY AND SURGICAL SPECIMEN PATHOLOGY FOR HIGH RISK PANCREATIC MUCINOUS CYSTIC LESIONS: A CASE SERIES
Bibliographic record
Abstract
Abstract Background Endoscopic ultrasound guided fine needle aspiration (EUS-FNA) is the primary method of sampling pancreatic cystic lesions with reported specificity near 100% for diagnosing malignancy. Discrepant positive malignant cytopathology with final surgical pathology of pancreatic cystic lesions has not previously been described. Aims To present a case series and review the literature regarding the implications of positive malignant cytology with discrepant surgical pathology for high risk pancreatic mucinous cystic lesions. Methods Patient demographics, clinical history, procedure details, pathology evaluations and follow-up were collected. A thorough literature review was performed. Results Three patients with high-risk pancreatic cystic lesions on cross-sectional imaging underwent EUS-FNA evaluation. None of these patients had a history of pancreatitis. Cytology was reported as positive for adenocarcinoma in all patients by separate gastrointestinal cytopathologists. All patients underwent surgical resection. The pathology for all resected specimens were reported as intraductal papillary mucinous neoplasm. The resected cysts for two patients demonstrated foci of high-grade dysplasia and the third noted low grade dysplasia. All surgical pathology underwent consensus review by three separate gastrointestinal pathologists. None of the patients were treated with adjuvant chemotherapy. All patients have been followed post-operatively with surveillance magnetic resonance imaging with no evidence of recurrence to date (median follow-up time 239 days, range 133 – 447 days). Conclusions This phenomenon sheds light on the potential for variable interpretations of EUS-FNA cytopathology and surgical resection pathology for high risk pancreatic cystic neoplasms. EUS-FNA may identify foci of adenocarcinoma that is not seen on surgical pathology specimens. Further research is required to examine the long-term outcomes of these patients. Funding Agencies None
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| 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".