Endoscopic ultrasound for pancreatic cystic lesions: a narrative review
Bibliographic record
Abstract
The incidence of incidental pancreatic cystic lesions (PCLs) has risen in recent years, largely due to advances in and increased use of imaging techniques. Endoscopic ultrasound (EUS) has become a crucial tool for evaluating and characterising PCLs, allowing for detailed morphological assessment and aiding in the identification of lesions with a higher risk of progression to high-grade dysplasia or invasive pancreatic carcinoma. This review aims to outline the key aspects of EUS in the evaluation of PCLs, covering a range of modalities from morphological assessment and contrast-enhanced imaging to elastography, fine-needle aspiration for biomarker analysis, cytology, DNA sequencing, histological evaluation and the emerging role of confocal laser endomicroscopy or artificial intelligence. Additionally, we address therapeutic EUS modalities for PCLs, the current limitations of EUS, anticipated technological advancements and the diverse management strategies recommended by leading scientific societies for the clinical handling of PCLs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".