Aim: To detect pancreatic neuroendocrine tumors (PNETs) has been varied. This study is undertaken to evaluate the accuracy of endoscopic ultrasound (EUS) in detecting PNETs.
Methods: Only EUS studies confirmed by surgery or appropriate follow-up were selected. Articles were searched in Medline, Ovid journals, Medline nonindexed citations, and Cochrane Central Register of Controlled Trials and Database of Systematic Reviews. Pooling was conducted by both fixed and random effects model).
Results: Initial search identified 2610 reference articles, of these 140 relevant articles were selected and reviewed. Data was extracted from 13 studies (n = 456) which met the inclusion criteria. Pooled sensitivity of EUS in detecting a PNETs was 87.2% (95%CI: 82.2-91.2). EUS had a pooled specificity of 98.0% (95%CI: 94.3-99.6). The positive likelihood ratio of EUS was 11.1 (95%CI: 5.34-22.8) and negative likelihood ratio was 0.17 (95%CI: 0.13-0.24). The diagnostic odds ratio, the odds of having anatomic PNETs in positive as compared to negative EUS studies was 94.7 (95%CI: 37.9-236.1). Begg-Mazumdar bias indicator for publication bias gave a Kendall's tau value of 0.31 (P = 0.16), indication no publication bias. The P for χ² heterogeneity for all the pooled accuracy estimates was > 0.10.
Conclusion: EUS has excellent sensitivity and specificity to detect PNETs. EUS should be strongly considered for evaluation of PNETs.
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http://dx.doi.org/10.3748/wjg.v19.i23.3678 | DOI Listing |
To assess the diagnostic accuracy of self-collected urine and vaginal samples for the identification of precancerous cervical lesions in the referral population using high-risk human papillomavirus (hrHPV) assays based on polymerase chain reaction (PCR). It was a prospective study carried out in China from June 2021 to March 2022. The vaginal and urine samples were collected and analyzed by using a newly developed specific hrHPV PCR test, and matched cervical samples were analyzed by using an approved hrHPV DNA test.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Internal Medicine and Liver Research Institute, Department of Medical Device Development, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.
This study aimed to identify biomolecular differences between benign gastric tissues (gastritis/intestinal metaplasia) and gastric adenocarcinoma and to evaluate the diagnostic power of Raman spectroscopy-based machine learning in gastric adenocarcinoma. Raman spectroscopy-based machine learning was applied in real-time during endoscopy in 19 patients (aged 51-85 years) with high-risk for gastric adenocarcinoma. Raman spectra were captured from suspicious lesions and adjacent normal mucosa, which were biopsied for matched histopathologic diagnosis.
View Article and Find Full Text PDFGastrointest Endosc
January 2025
Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN. Electronic address:
Background And Aims: An irregular z-line is characterized by a squamocolumnar junction (SCJ) that extends proximally above the gastroesophageal junction (GEJ) by < 1 centimeter (cm), while Barrett's esophagus (BE) is defined as a columnar lined esophagus (CLE) that extends proximally by ≥1 cm with the presence of specialized intestinal metaplasia (IM) on biopsy. Measurement of CLE is most accurate for lengths ≥1 cm, and as such, guidelines do not recommend biopsy of an irregular z-line when seen on endoscopy. However, a CLE is often estimated by visual inspection rather than direct measurement, making this characterization imprecise.
View Article and Find Full Text PDFBMC Med Imaging
January 2025
Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Objectives: The objective is to develop and validate intratumoral and peritumoral ultrasomics models utilizing endoscopic ultrasonography (EUS) to predict pathological grading in pancreatic neuroendocrine tumors (PNETs).
Methods: Eighty-one patients, including 51 with grade 1 PNETs and 30 with grade 2/3 PNETs, were included in this retrospective study after confirmation through pathological examination. The patients were randomly allocated to the training or test group in a 6:4 ratio.
J Imaging Inform Med
January 2025
School of Control Science and Engineering, Shandong University, Jinan, 250012, Shandong, China.
Early detection of colorectal cancer is vital for enhancing cure rates and alleviating treatment burdens. Nevertheless, the high demand for screenings coupled with a limited number of endoscopists underscores the necessity for advanced deep learning techniques to improve screening efficiency and accuracy. This study presents an innovative convolutional neural network (CNN) model, trained on 8260 images from screenings conducted at four medical institutions.
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