Clin Endosc.  2021 Sep;54(5):730-738. 10.5946/ce.2020.251.

Comparison of Endoscopic Ultrasound-Guided Tissue Acquisition Using a 20-Gauge Menghini Needle with a Lateral Forward Bevel and a 22-Gauge Franseen Needle: A Single-Center Large Cohort Study

Affiliations
  • 1Department of Hepato-Biliary-Pancreatic Medicine, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan

Abstract

Background/Aims
Several fine-needle biopsy (FNB) needles are available for endoscopic ultrasound (EUS)-guided tissue acquisition. However, there is disagreement on which type of needle has the best diagnostic yield. The aim of this study was to compare the performance and safety of two commonly used EUS-FNB needles.
Methods
We retrospectively analyzed consecutive patients who underwent EUS-FNB between June 2016 and March 2020 in our hospital. Two types of needles were evaluated: a 20-gauge Menghini needle with a lateral forward bevel and a 22-gauge Franseen needle. Rapid on-site evaluation was performed in all the cases. A multivariate analysis was performed to clarify the negative predictive factors for obtaining a histological diagnosis. Propensity score matching was performed to compare the diagnostic yields of these two needles.
Results
We analyzed 666 patients and 690 lesions. The overall diagnostic rate of histology alone was 88.8%, and the overall adverse event rate was 1.5%. Transduodenal access and small lesions (≤2 cm) were identified as negative predictive factors for obtaining a histological diagnosis. After propensity score matching, 482 lesions were analyzed. The diagnostic accuracy rates of histology in the M and F needle groups were 89.2% and 88.8%, respectively (p=1.00).
Conclusions
Both the needles showed high diagnostic yield, and no significant difference in performance was observed between the two.

Keyword

Biopsy; Endoscopic ultrasonography; Endoscopic ultrasound-guided fine needle aspiration
Full Text Links
  • CE
Actions
Cited
CITED
export Copy
Close
Share
  • Twitter
  • Facebook
Similar articles
    DB Error: unknown error