Optimal Number of Needle Punctures in EUS-FNA/B with ROSE for Solid Pancreatic Lesions.

Diagnostics (Basel)

Section of Oncopathology and Morphological Pathology, Department of Pathology, Faculty of Medicine, University of Miyazaki, Miyazaki 889-1692, Japan.

Published: July 2025


Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Endoscopic ultrasonography (EUS)-guided fine-needle aspiration/biopsy (FNA/B) is widely used for solid pancreatic lesions; however, the optimal number of needle punctures required to achieve high diagnostic accuracy remains unclear. This study aimed to identify the ideal number of punctures required for solid pancreatic lesions using EUS-FNA/B. This single-center retrospective study included 598 patients who underwent EUS-FNA/B for solid pancreatic lesions. We analyzed the cumulative tissue acquisition rates and diagnostic accuracy rates for cytology and histology, and identified the factors associated with diagnostic accuracy using univariate and multivariate analyses. Rapid on-site cytological evaluation was performed in all cases. Cumulative tissue acquisition rates were 95.6% and 92.5% for cytology and histology, respectively. The diagnostic accuracy for cytology increased from 72.6% in the first puncture to 78.8% in the second puncture ( = 0.0233). In contrast, the diagnostic accuracy of histology increased from 72.0% at the first puncture to 83.2% at the third puncture ( = 0.0412). Statistically significant differences were noted between the first and second punctures for cytology, and between the first, second, and third punctures for histology. Univariate and multivariate analyses were conducted to identify factors associated with diagnostic accuracy. In cytology, sex was identified as a significant contributing factor, whereas no independent predictors were found in histology. These findings suggest that two-needle punctures are optimal for cytology, and three-needle punctures are optimal for the histological diagnosis of solid pancreatic lesions using EUS-FNA/B.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12248492PMC
http://dx.doi.org/10.3390/diagnostics15131692DOI Listing

Publication Analysis

Top Keywords

diagnostic accuracy
24
solid pancreatic
20
pancreatic lesions
20
optimal number
8
number needle
8
needle punctures
8
punctures required
8
lesions eus-fna/b
8
cumulative tissue
8
tissue acquisition
8

Similar Publications

Leveraging GPT-4o for Automated Extraction and Categorization of CAD-RADS Features From Free-Text Coronary CT Angiography Reports: Diagnostic Study.

JMIR Med Inform

September 2025

Departments of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, Guangdong, 510630, China, 86 18922109279, 86 20852523108.

Background: Despite the Coronary Artery Reporting and Data System (CAD-RADS) providing a standardized approach, radiologists continue to favor free-text reports. This preference creates significant challenges for data extraction and analysis in longitudinal studies, potentially limiting large-scale research and quality assessment initiatives.

Objective: To evaluate the ability of the generative pre-trained transformer (GPT)-4o model to convert real-world coronary computed tomography angiography (CCTA) free-text reports into structured data and automatically identify CAD-RADS categories and P categories.

View Article and Find Full Text PDF

Background: Circumcision is a widely practiced procedure with cultural and medical significance. However, certain penile abnormalities-such as hypospadias or webbed penis-may contraindicate the procedure and require specialized care. In low-resource settings, limited access to pediatric urologists often leads to missed or delayed diagnoses.

View Article and Find Full Text PDF

This study aimed to develop a deep-learning model for the automatic classification of mandibular fractures using panoramic radiographs. A pretrained convolutional neural network (CNN) was used to classify fractures based on a novel, clinically relevant classification system. The dataset comprised 800 panoramic radiographs obtained from patients with facial trauma.

View Article and Find Full Text PDF

Background: Breast cancer is the most common cancer among women and a leading cause of mortality in Europe. Early detection through screening reduces mortality, yet participation in mammography-based programs remains suboptimal due to discomfort, radiation exposure, and accessibility issues. Thermography, particularly when driven by artificial intelligence (AI), is being explored as a noninvasive, radiation-free alternative.

View Article and Find Full Text PDF

Predicting Unplanned Readmission Risk in Patients With Cirrhosis: Complication-Aware Dynamic Classifier Selection Approach.

JMIR Med Inform

September 2025

College of Medical Informatics, Chongqing Medical University, 1 Yixueyuan Road, Yuzhong District, Chongqing, 400016, China, 86 13500303273.

Background: Cirrhosis is a leading cause of noncancer deaths in gastrointestinal diseases, resulting in high hospitalization and readmission rates. Early identification of high-risk patients is vital for proactive interventions and improving health care outcomes. However, the quality and integrity of real-world electronic health records (EHRs) limit their utility in developing risk assessment tools.

View Article and Find Full Text PDF