Preoperative<i>GNAS</i>and<i>KRAS</i>Testing in the Diagnosis of Pancreatic Mucinous Cysts
Bibliographic record
Abstract
PURPOSE: Management guidelines for pancreatic intraductal papillary mucinous neoplasms (IPMN) and mucinous cystic neoplasms (MCN) are based on the assumption that mucinous cysts can be accurately distinguished from other pancreatic cystic lesions. Previous studies using surgical material have identified recurrent mutations in GNAS and KRAS in pancreatic mucinous neoplasms. Yet, the diagnostic utility of testing for both genes in pancreatic cyst fluid obtained by endoscopic ultrasound-fine-needle aspiration (EUS-FNA) remains unclear. EXPERIMENTAL DESIGN: GNAS and KRAS testing was performed on EUS-FNA pancreatic cyst fluid from 91 pancreatic cysts: 41 IPMNs, 9 IPMNs with adenocarcinoma, 16 MCNs, 10 cystic pancreatic neuroendocrine tumors (PanNET), 9 serous cystadenomas (SCA), 3 retention cysts, 2 pseudocysts, and 1 lymphoepithelial cyst. RESULTS: Mutations in GNAS were detected in 16 (39%) IPMNs and 2 (22%) IPMNs with adenocarcinoma. KRAS mutations were identified in 28 (68%) IPMNs, 7 (78%) IPMNs with adenocarcinoma, and 1 (6%) MCN. Mutations in either gene were present in 34 (83%) IPMNs, 8 (89%) IPMNs with adenocarcinoma, and 1 (6%) MCN. No mutations were found in cystic PanNETs, SCAs, retention cysts, pseudocysts, and a lymphoepithelial cyst. GNAS and KRAS mutations had 100% specificity [95% confidence interval (CI), 0.83-1.00] but 65% sensitivity (95% CI, 0.52-0.76) for mucinous differentiation. Among IPMNs, mutations in either gene had 98% specificity (95% CI, 0.86-1.00) and 84% sensitivity (95% CI, 0.70-0.92). CONCLUSIONS: The combination of GNAS and KRAS testing was highly specific and sensitive for IPMNs; however, the lack of sensitivity for MCNs highlights the need for additional markers to improve the detection of pancreatic mucinous neoplasms.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".