Automated breast ultrasound is a three-dimensional ultrasonographic technique allowing the evaluation of women with dense glandular breast tissue. In this group of patients, mammography has a low sensitivity because dense breasts can obscure breast cancer on mammogram. On the other hand, women with dense breast tissue, types C and D on the BI-RADS scale, are at an increased risk of developing breast cancer compared to women with fatty breast tissue. Automated breast ultrasound is a standardized and reproducible ultrasound technique which improves breast cancer detection and is promising in the screening and diagnostic settings: it increases the detection of breast cancer, and helps to differentiate benign and malignant lesions. Unfortunately, automated breast ultrasound also has its limitations and disadvantages due to artifacts caused by poor positioning, and lesion and patient characteristics. Many artifacts can be avoided by training and experience of the performing technician. Furthermore, familiarity of the interpreting breast radiologist with these artifacts and pitfalls will decrease false negative diagnosis of true lesions.
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http://dx.doi.org/10.15557/jou.2022.0037 | DOI Listing |
Sci Rep
December 2024
Department of Public Health, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, 1111 Xianxia Road, Shanghai, 200335, China.
Breast ultrasound is recommended for early breast cancer detection in China, but the rapid increase in imaging data burdens sonographers. This study evaluated the agreement between artificial intelligence (AI) software and sonographers in analyzing breast nodule features. Breast ultrasound images from two hospitals in Shanghai were analyzed by both the software and the sonographers for features including echotexture, echo pattern, orientation, shape, margin, calcification, and posterior echo attenuation.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Breast, The First Hospital of Hunan University of Chinese Medicine, Changsha, 410007, China.
The association between the dietary inflammatory index (DII) and visual impairment remains unclear. This study aimed to investigate the relationship between the DII and non-refractive visual impairment among US populations. A cross-sectional analysis was conducted using data from the National Health and Nutrition Examination Survey (NHANES) 2005-2008, including dietary information and visual impairment assessment.
View Article and Find Full Text PDFMethods Protoc
December 2024
Department of Pathology, Herlev University Hospital, 2730 Herlev, Denmark.
High-quality RNA is crucial in clinical diagnostics and precision medicine. Formalin-fixed and paraffin-embedded (FFPE) tissues pose a challenge due to nucleic acid fragmentation and crosslinking. In this pilot study, various commercially available techniques for extracting RNA from small FFPE samples were compared.
View Article and Find Full Text PDFJ Imaging
December 2024
Computer Science and Engineering Department, College of Engineering, University of Nevada, Reno, Main Campus, Reno, NV 89557, USA.
Mammography images are the most commonly used tool for breast cancer screening. The presence of pectoral muscle in images for the mediolateral oblique view makes designing a robust automated breast cancer detection system more challenging. Most of the current methods for removing the pectoral muscle are based on traditional machine learning approaches.
View Article and Find Full Text PDFBioelectricity
December 2024
Department of Electrical Engineering and Industrial Computing, Ecole Nationale Supérieure des Technologies Avancées, Algiers, Algeria.
Background: Early detection of cancerous tumors is a critical factor in improving treatment outcomes. To address this need, this study explores a simple, effective, and cost-efficient method for early cancer detection by measuring the bioimpedance of living tissues. Bioimpedance-based methods hold significant promise for the early detection of cancerous tumors.
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