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• Case Report: Granular Cell Tumor In Breast

  Ruiz Alcaide Estefania, López Carrizosa Maria Concepción, Gutierrez Pantoja M Aranzazu, Arriaga Piñeiro Jesus M

  4-6

  
  Article ID: 1695
  263  (Abstract) 148  (Download)
  

  Abstract: Granular cell tumor (GCT) of the breast is an unusual neoplasm, tipicallybenign, it represents between 5-6% of all GCT cases. These tumors aremore common in middle-aged premenopausal women with a greater predilection African American race [1]. Nevertheless, there are also cases described in men [2-4]. Almost all of them are favorable, the malignant casesare uncommon... More

  2020-04-30

• The Loss of Heterozygosity of FHIT Gene in Sporadic Breast Cancer

  Lisiane Silveira Zavalhia, Andrea Pires Souto Damin, Grasiela Agnes, Aline Weber, Taís Frederes Kramer Alcalde, Laura Marinho Dorneles, Guilherme Watte, Adriana Vial Roehe

  33-39

  
  Article ID: 3632
  220  (Abstract) 164  (Download)
  

  Abstract: The loss of heterozygosity (LOH) is a genetic event that can change gene function. FHIT is a potential tumor suppressor gene.  Although the precise FHIT molecular mechanism of action is not well understood, evidences suggest that Fhit protein reduced levels are involved in mammary carcinogenesis.  The aim of this study was to investigate if FHIT LOH could influence on sporadic breast... More

  2021-09-13

• Associations between Body Mass Index and Breast Cancer Markers

  Ishita Saha, Poonam Singh, Sunit Kumar Medda, Rabindra Nath Das

  1-7

  
  Article ID: 2007
  483  (Abstract) 133  (Download)
  

  Abstract: Body mass index (BMI) and breast cancer biomarkers (BCBs) such asresistin, leptin adiponectin, monocyte chemoattractant protein-1 (MCP-1)and homeostasis model assessment of insulin resistance (HOMA-IR) arehighly associated with each other. The report has focused the inter-relationship between BMI and BCBs based on probabilistic modeling. It hasbeen shown that mean BMI is directly associated with leptin (P<0.0001)and... More

  2020-07-03

• Machine Learning Algorithms for Breast Cancer Diagnosis: Challenges, Prospects and Future Research Directions

  Rebecca Nyasuguta Arika, Agnes Mindila, W. Cheruiyo

  
  Article ID: 4977
  449  (Abstract) 402  (Download)
  

  Abstract: Early diagnosis of breast cancer does not only increase the chances of survival but also control the diffusion of cancerous cells in the body. Previously, researchers have developed machine learning algorithms in breast cancer diagnosis such as Support Vector Machine, K-Nearest Neighbor, Convolutional Neural Network, K-means, Fuzzy C-means, Neural Network, Principle Component Analysis (PCA)... More

  2022-11-02