Value of Endoscopic Ultrasonography-Guided Fine Needle Aspiration (FNA) in the Diagnosis of Neoplastic Tumor(-like) Pancreatic Lesions in Daily Clinical Practice
Bibliographic record
Abstract
PURPOSE: To investigate the value of EUS-guided FNA in the diagnosis of solid and cystic pancreatic tumor(-like) lesions as well as metastatic tumor growth within peripancreatic lymph nodes and its impact on therapeutic decision-making. The results of the cytologic and pathohistological investigation were compared with i) each other and ii) the detection rates of various imaging procedures. PATIENTS AND METHODS: Overall, 153 patients (mean age, 56.9 years) underwent EUS-guided FNA from I/ 2000 - III/ 2003. RESULTS: Comparing various imaging procedures such as CT scan (80 %), MRI (57.1 %) and abdominal US (88.8 %), EUS achieved the highest diagnostic accuracy: 100 %. For EUS-based T-staging in 26 patients with malignant tumor lesions undergoing surgical intervention, there was a sensitivity of 73.3 % (specificity, 85.9 %; PPV, 69.2 %; NPV, 84.4 %), while the parameters for N-staging (n = 25) were: sensitivity, 61.5 %; specificity, 75 %; NPV, 64.3 %; PPV, 72.7 %. While the sensitivity of EUS-guided FNA in the group of patients who underwent surgical intervention (n = 55) was 81.4 % (specificity, 75 %; PPV, 92.1 %; NPV, 52.9 %), the parameters were as follows in the subgroup of individuals with chronic pancreatitis (n = 30): sensitivity in detecting a malignant pancreatic tumor lesion, 50 %; specificity, 91.7 %; PPV, 60 %; NPV, 88 %. Based on preoperative characteristics such as suspected diagnosis, TNM stage and tumor entity, a surgical intervention could be avoided in 29 / 153 patients (19 %). CONCLUSION: EUS-guided FNA allows more precise diagnosis clarification (malignant tumor growth and tumor entity) in solid and cystic pancreatic tumor(-like) lesions, which may assist in early and sufficient therapeutic decision-making.
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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 | 0.004 | 0.037 |
| 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 | 0.000 | 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".