ArticleActa endocrinologica (Bucharest, Romania : 2005)
A MODEL TO PREDICT DIAGNOSIS OF PANCREATIC NEUROENDOCRINE TUMORS BASED ON EUS IMAGING FEATURES.
Article in Acta endocrinologica (Bucharest, Romania : 2005). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Endoscopic Ultrasound for the Management of Pancreatic Neuroendocrine Tumors: Diagnosis, Treatment, and Future Perspectives.Journal of gastroenterology and hepatology · 2026Review
- Preoperative prediction of pancreatic neuroendocrine tumors grade based on computed tomography, magnetic resonance imaging and endoscopic ultrasonography.Abdominal radiology (New York) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study aimed to determine predictive clinical and endoscopic ultrasound (EUS) features for pancreatic neuroendocrine tumor (PNET) diagnosis, utilizing EUS-guided tissue acquisition. Methods: A prospective study from 2018-2022 included patients with pancreatic masses undergoing EUS with elastography. Univariate binomial logistic regression followed by multiple logistic regression with significant predictors was employed. A forward selection algorithm identified optimal models based on predictor numbers. Variables encompassed EUS tumor characteristics (e.g., location, size, margins, echogenicity, vascularity on Doppler, main pancreatic duct dilation, elastography appearance, vascular invasion, and hypoechoic rim), alongside demographic and risk factors (smoking, alcohol, diabetes). Results: We evaluated 165 patients (24 PNETs). EUS features significantly linked with PNET diagnosis were well-defined margins (79% Conclusions: EUS appearance can differentiate PNETs from non-PNETs, with the hypoechoic rim being an independent predictor of PNET diagnosis. The most effective predictive model for PNETs combined the homogeneous lesion and presence of the hypoechoic rim.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.