The Uniform Approach to Breast Fine Needle Aspiration Biopsy was put forward by a learned group of breast physicians in 1997. This landmark manuscript focused predominantly on diagnosis and reporting of mammary epithelial lesions. Today, most American practitioners turn initially to core biopsy rather than aspiration biopsy for the first line diagnosis of solid breast lesions; however, recent efforts from the International Academy of Cytology have produced a system called the Standardized Reporting of Breast Fine Needle Aspiration Biopsy Cytology (colloquially labeled in 2017 as the "Yokohama System"), suggesting a new interest in breast fine needle aspiration (FNA), especially in resource limited settings or clinical practice settings with experienced breast cytopathologists. Fibroepithelial lesions of the breast comprise a heterogeneous group of biphasic tumors with epithelial and stromal elements. Mesenchymal lesions of the breast include a variety of neoplasms of fibroblastic, myofibroblastic, endothelial, neural, adipocytic, muscular, and osteo-cartilaginous derivations. The cytology of mesenchymal breast lesions is infrequently described in the literature and is mainly limited to case reports and small series. This illustrated review highlights the cytologic features of fibroepithelial and mesenchymal mammary proliferations and discusses differential diagnoses and histomorphologic correlates.
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http://dx.doi.org/10.1002/dc.24288 | DOI Listing |
Nat Genet
January 2025
Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
Genome-wide association studies have identified approximately 200 genetic risk loci for breast cancer, but the causal variants and target genes are mostly unknown. We sought to fine-map all known breast cancer risk loci using genome-wide association study data from 172,737 female breast cancer cases and 242,009 controls of African, Asian and European ancestry. We identified 332 independent association signals for breast cancer risk, including 131 signals not reported previously, and for 50 of them, we narrowed the credible causal variants down to a single variant.
View Article and Find Full Text PDFMicrosc Res Tech
January 2025
Artificial Intelligence and Data Analytics (AIDA) lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia.
Microscopic imaging aids disease diagnosis by describing quantitative cell morphology and tissue size. However, the high spatial resolution of these images poses significant challenges for manual quantitative evaluation. This project proposes using computer-aided analysis methods to address these challenges, enabling rapid and precise clinical diagnosis, course analysis, and prognostic prediction.
View Article and Find Full Text PDFInt J Biol Macromol
December 2024
Cancer Hospital of Dalian University of Technology, State Key Laboratory of Fine Chemicals, Dalian R&D Center for Stem Cell and Tissue Engineering, Dalian University of Technology, Dalian 116024, China. Electronic address:
Colorectal cancer (CRC) is now the third most common cancer worldwide. However, the development cycle for anticancer drugs is lengthy and the failure rate is high, highlighting the urgent need for new tumor models for CRC-related research. The decellular matrix (dECM) offers numerous cell adhesion sites, proteoglycan and cytokines.
View Article and Find Full Text PDFAnn Surg Oncol
December 2024
Qingdao Municipal Hospital, Qingdao University, Qingdao, People's Republic of China.
Background: The current study aimed to examine second breast cancer (SBC) risks associated with breast-conserving surgery (BCS) and unilateral mastectomy among breast cancer (BC) survivors.
Methods: The study enrolled patients with diagnoses of stages I to III BC who underwent surgery between 2000 and 2019. Fine-Gray competing risk regression models were used to estimate the cumulative incidence of SBC and to evaluate the associations between clinical factors and SBC development.
NPJ Digit Med
December 2024
School of Computing and Data Science, The University of Hong Kong, Hong Kong SAR, China.
Due to the large size and lack of fine-grained annotation, Whole Slide Images (WSIs) analysis is commonly approached as a Multiple Instance Learning (MIL) problem. However, previous studies only learn from training data, posing a stark contrast to how human clinicians teach each other and reason about histopathologic entities and factors. Here, we present a novel knowledge concept-based MIL framework, named ConcepPath, to fill this gap.
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