Background: To assess the role of endoscopic ultrasound (EUS) in the diagnosis of upper gastrointestinal subepithelial lesions (SELs) and to investigate EUS combined with a grayscale histogram analysis for the differentiation of leiomyomas and gastrointestinal stromal tumors (GISTs).
Methods: A retrospective study of 709 patients with upper gastrointestinal SELs was conducted by EUS before endoscopic resection. The EUS findings of SELs and pathological results after endoscopic resection were compared. The EUS images of SELs, particularly, leiomyoma and GIST, were further analyzed via a grayscale histogram to differentiate between the two tumors.
Results: Of the 709 patients, 47 cases were pathologically undetermined. The diagnostic consistency of EUS with endoscopic resection was 88.2% (584/662), including 185 muscularis mucosa, 61 submucosa, and 338 muscularis propria, respectively. The diagnostic consistency of EUS with pathology was 80.1% (530/662). The gray value of GISTs was significantly higher than that of leiomyomas (58.9 ± 8.3 vs. 39.5 ± 5.9, = 57.0, < 0.0001). The standard deviation of leiomyomas was significantly lower than that of GISTs (20.6 ± 7.0 vs. 39.8 ± 9.3, = 23.7, < 0.0001). The grayscale histogram analysis of GISTs showed higher echo ultrasound, and the echo of leiomyoma was more uniform.
Conclusion: EUS is the preferred procedure for the evaluation of upper gastrointestinal SELs. EUS combined with a grayscale histogram analysis is an effective method for the differentiation of leiomyomas and GISTs.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7301246 | PMC |
| http://dx.doi.org/10.1155/2020/6591341 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Utilizing Pseudo Color Image to Improve the Performance of Deep Transfer Learning-Based Computer-Aided Diagnosis Schemes in Breast Mass Classification.
J Imaging Inform Med
October 2024
Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, 73019, USA.
The purpose of this study is to investigate the impact of using morphological information in classifying suspicious breast lesions. The widespread use of deep transfer learning can significantly improve the performance of the mammogram based CADx schemes. However, digital mammograms are grayscale images, while deep learning models are typically optimized using the natural images containing three channels.
View Article and Find Full Text PDFA fundus vessel segmentation method based on double skip connections combined with deep supervision.
Front Cell Dev Biol
October 2024
The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China.
Background: Fundus vessel segmentation is vital for diagnosing ophthalmic diseases like central serous chorioretinopathy (CSC), diabetic retinopathy, and glaucoma. Accurate segmentation provides crucial vessel morphology details, aiding the early detection and intervention of ophthalmic diseases. However, current algorithms struggle with fine vessel segmentation and maintaining sensitivity in complex regions.
View Article and Find Full Text PDFDetection of Road Risk Sources Based on Multi-Scale Lightweight Networks.
Sensors (Basel)
August 2024
China Merchants Chongqing Road Engineering Inspection Center Co., Ltd., Chongqing 400067, China.
Article Synopsis
- Timely detection and disposal of road risks are crucial for safety, but current manual inspection methods are inefficient and slow.* -
- The paper presents a new automated detection method called the multi-scale lightweight network (MSLN), which uses advanced image processing to identify road risks like potholes and debris.* -
- By utilizing MobileNetV2 for feature extraction and incorporating transfer learning, the MSLN improves training speed and detection accuracy while effectively mapping real-world risk dimensions.*
Machine Learning and Deep Learning Methods for Fast and Accurate Assessment of Transthoracic Echocardiogram Image Quality.
Life (Basel)
June 2024
Department of Cardiology and Electrotherapy, Faculty of Medicine, Medical University of Gdansk, Smoluchowskiego 17, 80-213 Gdansk, Poland.
High-quality echocardiogram images are the cornerstone of accurate and reliable measurements of the heart. Therefore, this study aimed to develop, validate and compare machine learning and deep learning algorithms for accurate and automated assessment of transthoracic echocardiogram image quality. In total, 4090 single-frame two-dimensional transthoracic echocardiogram images were used from apical 4-chamber, apical 2-chamber and parasternal long-axis views sampled from 3530 adult patients.
View Article and Find Full Text PDFAn efficient image encryption model based on 6D hyperchaotic system and symmetric matrix for color and gray images.
Heliyon
June 2024
Department of Mathematics, College of Science, Qassim University, Buraydah, 51452, Saudi Arabia.
The security of images is one of the predominant pivotal aspects in the mammoth and still expanding digital domain. Due to chaotic system properties i.e.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!