Predictors of Malignancy and Recommended Follow-Up for Patients with Negative Endoscopic Ultrasound-Guided Fine-Needle Aspiration of Suspected Pancreatic Lesions
Bibliographic record
Abstract
BACKGROUND: Endoscopic ultrasound (EUS) with fine-needle aspiration (FNA) can characterize and diagnose pancreatic lesions as malignant, but cannot definitively rule out the presence of malignancy. Outcome data regarding the length of follow-up in patients with negative or nondiagnostic EUS-FNA of pancreatic lesions are not well-established. OBJECTIVE: To determine the long-term outcome and provide follow-up guidance for patients with negative EUS-FNA diagnosis of suspected pancreatic lesions based on imaging predictors. METHODS: A retrospective review of patients undergoing EUS-FNA for suspected pancreatic lesions, but with negative or nondiagnostic FNA results was conducted at a tertiary care referral medical centre. Patient demographics, EUS imaging characteristics and follow-up data were examined. RESULTS: Seventeen of 55 patients (30.9%) with negative/nondiagnostic FNA were subsequently diagnosed with pancreatic malignancy. The risk of cancer was significantly higher for patients who had associated lymph nodes on EUS (P<0.001) and vascular involvement on EUS (P=0.001). The mean time to diagnosis in the group with falsenegative EUS-FNA diagnosis was 66 days. The true-negative EUSFNA patients were followed for a mean of 403 days after negative EUS-FNA results without the development of malignancy. CONCLUSION: For patients undergoing EUS-FNA for a suspected pancreatic lesion, a negative or nondiagnostic FNA does not provide conclusive evidence for the absence of cancer. Patients for whom vascular invasion and lymphadenopathy are detected on EUS are more likely to have a true malignant lesion and should be followed closely. When a patient has been monitored for six months or more with no cancer being diagnosed, there appears to be much less chance that a pancreatic malignancy is present.
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".