Pathology of Breast Cancer

Tuzlali, Sıtkı

doi:10.1007/978-3-030-04606-4_14

Cited by 4 publications

(2 citation statements)

References 107 publications

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“…Breast cancer (BC) in women involves the progressive accumulation of genetic, hereditary, hormonal, and environmental factors representing a significant health problem worldwide [ 1 ]. BC is the leading cause of cancer-related death among women (521,541 cases) and the most commonly diagnosed cancer (1,679,076 cases) [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Mutational Analysis of Oncogenic AKT1 Gene Associated with Breast Cancer Risk in the High Altitude Ecuadorian Mestizo Population

López‐Cortés

Leone

Freire‐Paspuel

et al. 2018

BioMed Research International

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Breast cancer is the leading cause of cancer-related death among women worldwide. AKT1 encodes the kinase B alpha protein. The rs121434592, rs12881616, rs11555432, rs11555431, rs2494732, and rs3803304 single nucleotide polymorphisms have been identified in the AKT1 kinase gene. Activated AKT1 phosphorylates downstream substrates regulating cell growth, metabolism, apoptosis, angiogenesis, and drug responses. It is essential to know how breast cancer risk is associated with histopathological and immunohistochemical characteristics and genotype polymorphisms in a high altitude Ecuadorian mestizo population. This is a retrospective case-control study. DNA was extracted from 185 healthy and 91 affected women who live 2,800 meters above sea level. Genotypes were determined by genomic sequencing. We found a possible association between the noncoding intronic variant rs3803304 and breast cancer risk development: GG (odds ratio [OR] = 5.2; 95% confidence interval [CI] = 1.3-20.9; P ≤ 0.05; Q > 0.05). Regarding pathologic characteristics, we found significant risk between estrogen receptor, progesterone receptor, and HER2 status and molecular subtypes (P ≤ 0.001; Q ≤ 0.05). On the other hand, we did not find risk between variants and histopathological characteristics. Despite the small sample size, we found that the intronic variant, AKT1 rs3803304, may act as a predictive biomarker in the risk of developing breast cancer in the high altitude Ecuadorian mestizo population.

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Section: Introductionmentioning

confidence: 99%

Mutational Analysis of Oncogenic AKT1 Gene Associated with Breast Cancer Risk in the High Altitude Ecuadorian Mestizo Population

López‐Cortés

Leone

Freire‐Paspuel

et al. 2018

BioMed Research International

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“…Invasive carcinoma can also be divided in two: no special type and special subtype. The invasive ductal carcinoma no special type includes tumors that cannot be categorized as one of the special rare types, which have specific definitions, as lobular, tubular, and papillary invasive carcinomas [36][37][38][39][40].…”

Section: Breast Cancermentioning

confidence: 99%

Evaluation of breast cancer molecular subtypes using artificial intelligence in micro-FTIR hyperspectral images

Valle¹

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I would like to express my deepest appreciation to:• Professor Denise Maria Zezell, my advisor, for her trust, knowledge, academic dedication, and friendship. She is a brave and intelligent woman and a role model for any researcher.• Professor Emerson Soares Bernardes, my co-advisor, for providing ideas and guidance on this important subject of breast cancer.• My parents, Sueli and Alfredo, for their unwavering support throughout this journey. This title is as much theirs as it is mine.• My family, friends, and especially my girlfriend, Marina, for their understanding in times of absence and for the motivational support to keep moving on.• My colleagues at the Laboratory of Biophotonics, especially Moises, Pedro, Ariane and Amanda for their teachings, partnership, and friendship.• My colleagues at the Lasers and Applications Center, for all the shared knowledge.• USP and IPEN for the opportunities and infrastructure provided, and for their contribution to science and education. • FAPESP (CEPID 05/51689-2, 17/50332-0), CAPES (Finance Code 001, PROCAD 88881.068505/2014-01) and CNPq (INCT-465763/2014-6, PQ-309902/2017-7, 142229/2019-9) for their financial support. • Colleagues from my former job at the Health Innovation Techcenter of Hospital Israelita Albert Einstein, for sharing with me their GPU workstation (a.k.a. Vivi) and supporting my academic activities.• Everyone who contributed to this research. Thank you! RESUMO DEL-VALLE, Matheus. Avaliação de subtipos moleculares de câncer de mama utilizando inteligência artificial em imagens hiperespectrais por micro-FTIR. 2023. 70 p. Tese (Doutorado em Tecnologia Nuclear) -Instituto de Pesquisas Energéticas e Nucleares -IPEN -CNEN/SP. São Paulo. O câncer de mama é o mais incidente no mundo. A avaliação do subtipo molecular e seus biomarcadores tem um papel fundamental para o prognóstico. Os biomarcadores utilizados são os Receptores de Estrogênio (ER), de Progesterona (PR), de tipo 2 do fator de Crescimento Epidérmico Humano (HER2), e Ki67. Com base nestes, os subtipos são classificados como Luminal A (LA), Luminal B (LB), subtipo HER2 e Triplo-Negativo (TNBC). O padrão-ouro desta análise é a histologia e imuno-histoquímica, técnicas semiquantitativas que apresentam variações inter-laboratorial e inter-observador. A técnica de micro-espectroscopia no Infravermelho por Transformada de Fourier (FTIR), que fornece imagens hiperspectrais com informações bioquímicas de tecidos biológicos, é aplicada em conjunto de inteligência artificial (IA) para avaliação de cânceres. Nesta tese, foram utilizadas vinte amostras de duas linhagens celulares de câncer de mama, BT-474 e SK-BR-3, para definição do número ótimo de varreduras co-adicionadas para técnicas de aprendizado de máquina (ML). Foram utilizados os modelos de Análise Discriminante Linear (LDA), Análise Discriminante por Mínimos Quadrados Parciais (PLS-DA), K-Vizinhos Mais Próximos (KNN), Máquinas de Vetores de Suporte (SVM), Floresta Aleatória (RF) e Aumento de Gradiente Extremo (XGB). Sessenta imagens hiperespectrais de...

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A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities

Mahmood

Pei

et al. 2020

IEEE Access

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Patients with breast cancer are prone to serious health-related complications with higher mortality. The primary reason might be a misinterpretation of radiologists in recognizing suspicious lesions due to technical issues in imaging qualities and heterogeneous breast densities which increases the false-(positive and negative) ratio. Early intervention is significant in establishing an up-to-date prognosis process which can successfully mitigate complications of disease with higher recovery. The manual screening of breast abnormalities through traditional machine learning schemes misinterpret the inconsistent featureextraction process which poses a problem, i.e., patients being called-back for biopsies to eliminates the suspicions. However, several deep learning-based methods have been developed for reliable breast cancer prognosis and classification but very few of them provided a comprehensive overview of lesions segmentation. This research focusses on providing benefits and risks of breast multi-imaging modalities, segmentation schemes, feature extraction, classification of breast abnormalities through state-of-the-art deep learning approaches. This research also explores various well-known databases using "Breast Cancer" keyword to present a comprehensive survey on existing diagnostic schemes to open-up new research challenges for radiologists and researchers to intervene as early as possible to develop an efficient and reliable breast cancer prognosis system using prominent deep learning schemes.

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Pathology of Breast Cancer

Cited by 4 publications

References 107 publications

Mutational Analysis of Oncogenic AKT1 Gene Associated with Breast Cancer Risk in the High Altitude Ecuadorian Mestizo Population

Mutational Analysis of Oncogenic AKT1 Gene Associated with Breast Cancer Risk in the High Altitude Ecuadorian Mestizo Population

Evaluation of breast cancer molecular subtypes using artificial intelligence in micro-FTIR hyperspectral images

A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities

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