Purpose: To retrospectively compare primary three-dimensional (3D) endoluminal analysis with primary two-dimensional (2D) transverse analysis supplemented by computer-assisted reader (CAR) software for computed tomographic (CT) polyp detection and reader reporting times.
Materials And Methods: Ethical permission and patient consent were obtained from all donor institutions for use of CT colonography data sets. Twenty CT colonography data sets from 14 men (median age, 61 years; age range, 52-78 years) with 48 endoscopically proved polyps were selected. Polyp coordinates were documented in consensus by three unblinded radiologists to create a reference standard. Two radiologists read the data sets, which were randomized between primary 3D endoluminal views with 2D problem solving and 2D views supplemented by CAR software. Reading times and diagnostic confidence were documented. The CAR software highlighted possible polyps by superimposing circles on the 2D transverse images. Data sets were reread after 1 month by using the opposing analysis method. Detection rates were compared by using the McNemar test. Reporting times and diagnostic confidence were compared by using the paired t test and Mann-Whitney U test, respectively.
Results: Mean sensitivity values for polyps measuring 1-5, 6-9, and 10 mm or larger were 14%, 53%, and 83%, respectively, for 2D CAR analysis and 16%, 53%, and 67%, respectively, for primary 3D analysis. Overall sensitivity values were 41% for 2D CAR analysis and 39% for primary 3D analysis (P=.77). Reader 1 detected more polyps than reader 2, particularly when using the 3D fly-through method (P=.002). Mean reading times were significantly longer with the 3D method (P=.001). Mean false-positive findings were 1.5 for 2D analysis and 5.5 for 3D analysis. Reader confidence was not significantly different between analysis methods (P=.42).
Conclusion: Two-dimensional CAR analysis is quicker and at least matches the sensitivity of primary 3D endoluminal analysis, with fewer false-positive findings.
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http://dx.doi.org/10.1148/radiol.2392050483 | DOI Listing |
Nutrients
January 2025
Department of Nutrition, School of Public Health, Sun Yat-sen University, 74 Zhong Shan Road 2, Guangzhou 510080, China.
Background: Evidence regarding the individual and combined impact of dietary flavonoids on the risk of metabolic dysfunction associated with steatotic liver disease (MASLD) remains scarce. Our objective is to evaluate the association between individual and multiple dietary flavonoids with MASLD in adults.
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Materials (Basel)
January 2025
College of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China.
Due to the uncertainty of material properties of plate-like structures, many traditional methods are unable to locate the impact source on their surface in real time. It is important to study the impact source-localization problem for plate structures. In this paper, a data-driven machine learning method is proposed to detect impact sources in plate-like structures and its effectiveness is tested on three plate-like structures with different material properties.
View Article and Find Full Text PDFMedicina (Kaunas)
January 2025
Department of Internal Medicine (Nephrology), Faculty of Medicine, Ufuk University, 06510 Ankara, Turkey.
Immunoglobulin G4-related disease (IgG4-RD) is an immune-mediated, fibroinflammatory, multiorgan disease with an obscure pathogenesis. Findings indicating excessive platelet activation have been reported in systemic sclerosis, which is another autoimmune, multisystemic fibrotic disorder. The immune-mediated, inflammatory, and fibrosing intersections of IgG4-RD and systemic sclerosis raised a question about platelets' role in IgG4-RD.
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January 2025
Department of Biostatistics, Data Science, and Epidemiology, School of Public Health, Georgia Cancer Center at Augusta University, Augusta, GA 30912, USA.
: Recent growth in the number and applications of high-throughput "omics" technologies has created a need for better methods to integrate multiomics data. Much progress has been made in developing unsupervised methods, but supervised methods have lagged behind. : Here we present the first algorithm, PLASMA, that can learn to predict time-to-event outcomes from multiomics data sets, even when some samples have only been assayed on a subset of the omics data sets.
View Article and Find Full Text PDFInt J Environ Res Public Health
January 2025
Department of Psychology, Springfield College, 263 Alden Street, Springfield, MA 01109, USA.
Changes in athletic identity have been documented after injury and other sport transitions in nomothetic investigations. Patterns of change in athletic identity after injury have not been examined systematically at the individual level. In the current study, secondary analyses were performed on two data sets ( = 43 and = 80) in which athletic identity values were available for before and at least six months after anterior cruciate ligament (ACL) reconstruction.
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