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2 results on '"Invasive ductal carcinoma"'

1. HPV Detection in Breast Tumors and Associated Risk Factors in Northeastern Brazil.

Author

Nascimento KCG, São Marcos BF, Fontes PHB, Isídio BEO, Leão SL, da Silva GRP, Lussón DB, Dos Santos DL, Leal LRS, Espinoza BCF, de Macêdo LS, de França Neto PL, Silva AJD, Silva Neto JC, Santos VEP, and de Freitas AC

Subjects
Humans, Brazil epidemiology, Female, Middle Aged, Risk Factors, Adult, Aged, Papillomaviridae, Viral Load, Breast Neoplasms virology, Papillomavirus Infections virology, Papillomavirus Infections complications, Papillomavirus Infections epidemiology
Abstract

Breast cancer risk factors include lifestyle, genetic-hormonal influences, and viral infections. Human papillomavirus (HPV), known primarily as the etiological agent of cervical cancer, also appears active in breast carcinogenesis, as evidenced in our study of 56 patients from northeastern Brazil. We assessed the clinical and sociodemographic characteristics, correlating them with various breast cancer tumor types. HPV detection involved amplifying the L1 region, with viral load measured using the E2/E6 ratio and viral activity indicated by E5 oncogene expression. Predominantly, patients over 56 years of age with healthy lifestyles showed a high incidence of invasive ductal carcinoma and triple-negative breast cancer. HPV was detected in 35.7% of cases, mostly HPV16, which is associated with high viral loads (80 copies per cell) and significant E5 expression. These results hint at a possible link between HPV and breast carcinogenesis, necessitating further studies to explore this association and the underlying viral mechanisms.

Published
2024
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2. The Use of Convolutional Neural Networks in the Prediction of Invasive Ductal Carcinoma in Histological Images of Breast Cancer.

Author

de Assis, Érika G., do Patrocínio, Zenilton K. G., and Neri Nobre, Cristiane

Subjects
BREAST cancer diagnosis, DEEP learning, CONFERENCES & conventions, MAMMOGRAMS, MACHINE learning, DUCTAL carcinoma, DATABASE management, BREAST, AUTOMATION, RESEARCH funding, ARTIFICIAL neural networks, HISTOLOGY
Abstract

This paper presents a deep learning approach for automatic detection and visual analysis of Invasive Ductal Carcinoma (IDC) tissue regions. The method proposed in this work is a convolutional neural network (CNN) for visual semantic analysis of tumor regions for diagnostic support. Detection of IDC is a time-consuming and challenging task, mainly because a pathologist needs to examine large tissue regions to identify areas of malignancy. Deep Learning approaches are particularly suitable for dealing with this type of problem, especially when many samples are available for training, ensuring high quality of the learned features by the classifier and, consequently, its generalization capacity. A 3-hidden-layer CNN with data balancing reached both accuracy and F1-Score of 0.85 and outperforming other approaches from the literature. Thus, the proposed method in this article can serve as a support toolfor the identification of invasive breast cancer. [ABSTRACT FROM AUTHOR]

Published
2021
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