Tissue biopsy is often not very accurate for the diagnosis of gastric epithelial neoplasia (GEN), and the results differ notably from endoscopic resection (ER) in terms of the pathological diagnosis. The aims of this study were to evaluate the diagnostic performances of biopsy, magnifying endoscopy with narrow-band imaging (ME-NBI), and biopsy plus ME-NBI for GEN.This study retrospectively analyzed 101 cases diagnosed as GEN using ER samples. The discrepancies between biopsy and ER, as well as between biopsy plus ME-NBI and ER in the diagnosis of GEN were evaluated. Factors that contributed to such discrepancies were analyzed. The sensitivity and specificity of biopsy and ME-NBI for the diagnosis of high-grade neoplasia (HGN) were determined.The discrepancy in the pathological diagnosis between biopsy and ER was 39.6% for GEN and 54.2% for HGN. The discrepancy between biopsy combined with ME-NBI and ER was 15.9% for GEN and 10.2% for HGN. Factors that undermined the diagnostic accuracy of biopsy included the lesion size (≤10 mm, odds ratio [OR] 1; 10-20 mm, OR 0.2, 95% confidence interval [CI] 0.1-0.7; >20 mm, OR 0.5, 95% CI 0.1-2.1, P = 0.03) and the number of biopsy fragments (OR 0.6, 95% CI 0.5-0.8, P = 0.001). The sensitivity and specificity for HGN were 45.8% (33.7%-58.3%) and 100% (87.5%-100%) for biopsy, and 88.1% (77.5%-94.1%) and 92.9% (81.0%-97.5%) for ME-NBI, respectively.In conclusion, biopsy-based diagnoses for GEN should be interpreted with caution. Biopsy combined with ME-NBI can contribute to the diagnosis of GEN, which improves diagnostic consistency with pathological result of ER specimens.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4504654 | PMC |
http://dx.doi.org/10.1097/MD.0000000000001092 | DOI Listing |
Sci Rep
December 2024
Department of Diagnostic Radiology, Dalhousie University, Halifax, Canada.
The goal of this study was to determine how radiologists' rating of image quality when using 0.5T Magnetic Resonance Imaging (MRI) compares to Computed Tomography (CT) for visualization of pathology and evaluation of specific anatomic regions within the paranasal sinuses. 42 patients with clinical CT scans opted to have a 0.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Neurobiology and Behavior, University of California Irvine, Irvine, CA, 92697, USA.
Preserving the ability to vividly recall emotionally rich experiences contributes to quality of life in older adulthood. While prior works suggest that moderate-intensity physical activity (MPA) may bolster memory, it is unclear whether this extends to emotionally salient memories consolidated during sleep. In the current study, older adults (mean age = 72.
View Article and Find Full Text PDFSci Rep
December 2024
Division of Genetics, Indian Agricultural Research Institute, New Delhi, 110012, India.
The mungbean yellow mosaic India virus (MYMIV, Begomovirus vignaradiataindiaense) causes Yellow Mosaic Disease (YMD) in mungbean (Vigna radiata L.). The biochemical assays including total phenol content (TPC), total flavonoid content (TFC), ascorbic acid (AA), DPPH (2,2-diphenyl-1-picrylhydrazyl), and FRAP (Ferric Reducing Antioxidant Power) were used to study the mungbean plants defense response to MYMIV infection.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Biochemistry and Molecular Biology, Medical University of Lublin, 20-093, Lublin, Poland.
Using Fourier Transform Infrared spectroscopy (FTIR), it is possible to show chemical composition of materials and / or profile chemical changes occurring in tissues, cells, and body fluids during onset and progression of diseases. For diagnostic application, the use of blood would be the most appropriate in biospectroscopy studies since, (i) it is easily accessible and, (ii) enables frequent analyses of biochemical changes occurring in pathological states. At present, different studies have investigated potential of serum, plasma and sputum being alternative biofluids for lung cancer detection using FTIR.
View Article and Find Full Text PDFSci Rep
December 2024
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!