Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Using histogram analysis of T2 values to detect early involvement of extraocular muscles (EOMs) in patients with thyroid-associated ophthalmopathy (TAO). Five EOMs of each orbit were analyzed for 45 TAO patients and 22 healthy controls (HCs). Patients' EOMs were grouped into involved or normal-appearing EOMs (NAEOMs). Histogram parameters and signal intensity ratios (SIRs) of EOMs were compared; receiver operating characteristic (ROC) curve analysis was performed to differentiate NAEOMs from EOMs of HCs. 24 patients were reassessed following immunosuppressive treatment. For SIRs, involved muscles showed higher values than those of NAEOMs and HCs (p < 0.05); there were no differences between NAEOMs and HCs (p = 0.26). Parameters of involved muscles showed no different from those of NAEOMs excluding 25th, 50th percentiles, and standard deviation (SD) (p < 0.05). NAEOMs displayed higher values of 90th, 95th percentiles, SD, skewness, inhomogeneity, and entropy than HCs (p < 0.05). ROC curve analysis of entropy yielded the best area under the ROC curve (AUC; 0.816) for differentiating NAEOMs and HCs. After treatment, histogram parameters including 5th, 75th, 90th, and 95th percentiles, SD, kurtosis, inhomogeneity, and entropy were reduced in NAEOMs (p < 0.05). T2 histogram analysis could detect early involvement of EOMs in TAO prior to detection on conventional orbital MRI.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7655798PMC
http://dx.doi.org/10.1038/s41598-020-76341-6DOI Listing

Publication Analysis

Top Keywords

histogram analysis
8
early involvement
8
involvement extraocular
8
extraocular muscles
8
patients thyroid-associated
8
thyroid-associated ophthalmopathy
8
eoms
6
analysis mapping
4
mapping detecting
4
detecting early
4

Similar Publications

MRI Assessment of Radiation-Induced Delayed-Onset Microstructural Gray Matter Changes in Nasopharyngeal Carcinoma Patients.

J Magn Reson Imaging

September 2025

School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China.

Background: The dynamic progression of gray matter (GM) microstructural alterations following radiotherapy (RT) in patients, and the relationship between these microstructural abnormalities and cortical morphometric changes remains unclear.

Purpose: To longitudinally characterize RT-related GM microstructural changes and assess their potential causal links with classic morphometric alterations in patients with nasopharyngeal carcinoma (NPC).

Study Type: Prospective, longitudinal.

View Article and Find Full Text PDF

Assessment of yerba mate quality based on branch content via digital image analysis.

Food Chem

September 2025

Group of Chemical Analysis and Chemometrics, Department of Chemistry, Federal University of Paraná, P.O. Box: 19032, Curitiba, PR 81531-980, Brazil. Electronic address:

Yerba mate, a key crop in South America, is prized for its pleasant taste and high organoleptic quality, often linked to lower branch content. To quantify branch content and authenticate high-quality samples (less than 30 % m/m branch content), a Chemometrics-assisted Color Histogram-based Analytical System (CACHAS) was employed. Using Hue-Saturation-Value (HSV) histograms, Partial Least Squares (PLS) demonstrated excellent predictive performance, achieving a root mean square error (RMSEP) of 4.

View Article and Find Full Text PDF

Background: High-dose-rate (HDR) brachytherapy is essential in the treatment of locally advanced cervical cancer. While Iridium-192 (Ir-192) is commonly used, its short half-life imposes logistical and financial constraints, particularly in low- and middle-income countries (LMICs). Cobalt-60 (Co-60), with a longer half-life and lower operational costs, is a viable alternative.

View Article and Find Full Text PDF

Cervical cancer remains a significant cause of female mortality worldwide, primarily due to abnormal cell growth in the cervix. This study proposes an automated classification method to enhance detection accuracy and efficiency, addressing contrast and noise issues in traditional diagnostic approaches. The impact of image enhancement on classification performance is evaluated by comparing transfer learning-based Convolutional Neural Network (CNN) models trained on both original and enhanced images.

View Article and Find Full Text PDF

Objective: This study aims to develop a robust, multi-task deep learning framework that integrates vessel segmentation and radiomic analysis for the automated classification of four retinal conditions- diabetic retinopathy (DR), hypertensive retinopathy (HR), papilledema, and normal fundus-using fundus images.

Materials: AND.

Methods: A total of 2,165 patients from eight medical centers were enrolled.

View Article and Find Full Text PDF