Osteoporosis, characterized by reduced bone mass and increased bone fragility, is a disease prevalent in women. Likewise, breast cancer is a multifactorial disease and considered the major cause of mortality in premenopausal and postmenopausal women worldwide. Our data demonstrated the association of the gene and gene variants with osteoporosis and benign breast tumor risk and the impact of ovariectomy on osteoporosis in Korean women. We performed a genome-wide association study (GWAS) of women with osteoporosis and benign breast tumors. There were 60 single nucleotide polymorphisms (SNPs) and 12 SNPs in the and genes, associated with benign breast tumors and osteoporosis. Our study showed that women with homozygous rs12163585 major alleles had an increased risk of osteoporosis following ovariectomy compared to those with minor alleles. Women carrying the minor rs1213265 allele and not treated via ovariectomy carried a higher risk of osteoporosis than those who underwent ovariectomy with a homozygous genotype at the major alleles. Our results suggest that both the and genes are genetic factors associated with the phenotypes, and these associations appear to be modulated by ovariectomy.
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http://dx.doi.org/10.3390/genes12030378 | DOI Listing |
Magn Reson Imaging
January 2025
Background: Huai-he Hospital of Henan University, Kaifeng, China. Electronic address:
Objective: To explore the application value of MRI-based imaging histology and deep learning model in the identification and classification of breast phyllodes tumors.
Methods: Seventy-seven patients diagnosed as breast phyllodes tumors and fibroadenomas by pathological examination were retrospectively analyzed, and traditional radiomics features, subregion radiomics features, and deep learning features were extracted from MRI images, respectively. The features were screened and modeled using variance selection method, statistical test, random forest importance ranking method, Spearman correlation analysis, least absolute shrinkage and selection operator (LASSO).
Radiographics
February 2025
From the Washington University School of Medicine, Mallinckrodt Institute of Radiology, 510 S Kingshighway Blvd, St. Louis, MO 63110.
Annual review of false-negative (FN) mammograms is a mandatory and critical component of the Mammography Quality Standards Act (MQSA) annual mammography audit. FN review can help hone reading skills and improve the ability to detect cancers at mammography. Subtle architectural distortion, asymmetries (seen only on one view), small lesions, lesions with probably benign appearance (circumscribed regular borders), isolated microcalcifications, and skin thickening are the most common mammographic findings when the malignancy is visible at retrospective review of FN mammograms.
View Article and Find Full Text PDFJNCI Cancer Spectr
December 2024
Quantitative Health Sciences, Jacksonville, Florida.
Background: Benign breast disease (BBD) increases breast cancer (BC) risk progressively for women diagnosed with non-proliferative (NP) change, proliferative disease without atypia (PDWA), and atypical hyperplasia (AH). Leveraging data from 18,704 women in the Mayo BBD Cohort (1967-2013), we evaluated temporal trends in BBD diagnoses and how they have influenced associated BC risk over four decades.
Methods: BC risk trends associated with BBD were evaluated using standardized incidence ratios (SIRs) and age-period-cohort modeling across four eras-pre-mammogram (1967-1981), pre-core needle biopsy (CNB) (1982-1992), transition to CNB (1993-2001), and CNB era (2002-2013).
Explor Target Antitumor Ther
January 2025
Department of Medical Biochemistry, Faculty of Medicine, Umm Al-Qura University, Makkah 21955, Saudi Arabia.
Aim: Breast cancer (BC), a disease in which abnormal breast cells grow out of control and form tumors, is a prevalent life-threatening disease worldwide. Oxidative stress has been implicated in the development and progression of various cancers, including BC. Assessing lipid peroxidation and overall antioxidant status in BC offers valuable information on disease progression, patient prognosis, and the effectiveness of therapeutic options.
View Article and Find Full Text PDFBiomed Eng Lett
January 2025
Electronics and Communication Engineering, IFET College of Engineering, Villupuram, Tamilnadu India.
Unlabelled: Breast cancer (BC) remains a significant global health issue, necessitating innovative methodologies to improve early detection and diagnosis. Despite the existence of intelligent deep learning models, their efficacy is often limited due to the oversight of small-sized masses, leading to false positive and false negative outcomes. This research introduces a novel segmentation-guided classification model developed to increase BC detection accuracy.
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