Objective: The objective of this study was to assess interobserver uncertainties in power Doppler (PD) examination of the fingers of patients with rheumatoid arthritis (RA), by separating the source of the discrepancy into (1) acquisition of the images and (2) criteria for assessment of the images.
Materials And Methods: Twenty patients who had been diagnosed with RA were enrolled in this study. Ultrasound examinations were performed by one inexperienced and two experienced sonographers. Interobserver variation was measured using a conventional semiquantitative image grading scale. Interobserver variation of the quantitative PD (QPD) index (the summation of the colored pixels in a region of interest) was also assessed.
Results: The agreement was higher between the two experienced sonographers (kappa value of 0.8) than between experienced and inexperienced sonographers (kappa value, 0.6-0.7) in the semiquantitative image grading scale. Results suggest that the difference in the assessment on the image grading scale was due more to the difference in the acquisition of the images than to variations in the grading criteria between sonographers. An excellent relationship was noted between the image grading scale and the QPD index for Doppler signal with a Spearman's coefficient of rank correlation of 0.83 (P < 0.0001).
Conclusions: Interobserver discrepancies in the image grading and QPD index methods were due more to the difference in the acquisition of the image than to the grading criteria used. The QPD index seems to be as reliable as the image grading scale with reasonable interobserver agreement between experienced sonographers.
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http://dx.doi.org/10.1007/s00256-009-0665-2 | DOI Listing |
Indian J Orthop
January 2025
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001 China.
Introduction: The Steinberg classification system is commonly used by orthopedic surgeons to stage the severity of patients with osteonecrosis of the femoral head (ONFH), and it includes mild, moderate, and severe grading of each stage based on the area of the femoral head affected. However, clinicians mostly grade approximately by visual assessment or not at all. To accurately distinguish the mild, moderate, or severe grade of early stage ONFH, we propose a convolutional neural network (CNN) based on magnetic resonance imaging (MRI) of the hip joint of patients to accurately grade and aid diagnosis of ONFH.
View Article and Find Full Text PDFFront Oncol
November 2024
Department of Oncology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Purpose: This study aimed to develop and validate a model for accurately assessing the risk of distant metastases in patients with gastric cancer (GC).
Methods: A total of 301 patients (training cohort, n = 210; testing cohort, n = 91) with GC were retrospectively collected. Relevant clinical predictors were determined through the application of univariate and multivariate logistic regression analyses.
Ethiop J Health Sci
October 2024
Department of Radiology, School of Medicine, Addis Ababa University, Addis Ababa, Ethiopia.
Background: Prostate cancer is a leading cause of cancer-related mortality among men, second only to lung cancer. Prostate magnetic resonance imaging (MRI) utilizing the Prostate Imaging and Reporting Data System (PI-RADS) v2.1 scoring system effectively stratifies patients by risk and correlates significantly with histopathological outcomes.
View Article and Find Full Text PDFEthiop J Health Sci
October 2024
St. Paul Millennium Medical College, Department of Radiology, Addis Ababa, Ethiopia.
Background: Perianal fistula refers to an abnormal connection between the anal canal and the perianal skin or perineum. Magnetic Resonance Imaging (MRI) plays a crucial role in accurately characterizing perianal fistulas, which informs surgical strategies and helps minimize recurrence.
Methods: This cross-sectional study was conducted at a single diagnostic imaging center in Addis Ababa, utilizing retrospectively collected data from May 2023 to June 2024.
Netw Neurosci
December 2024
Department of Clinical Cognition Science, Clinic of Neurology at the RWTH Aachen University Faculty of Medicine, ZBMT, Aachen, Germany.
Networks in the parietal and premotor cortices enable essential human abilities regarding motor processing, including attention and tool use. Even though our knowledge on its topography has steadily increased, a detailed picture of hemisphere-specific integrating pathways is still lacking. With the help of multishell diffusion magnetic resonance imaging, probabilistic tractography, and the Graph Theory Analysis, we investigated connectivity patterns between frontal premotor and posterior parietal brain areas in healthy individuals.
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