Breast cancer was one of the most common reasons for death among the women in the world. Limited awareness of the seriousness of this disease, shortage number of specialists in hospitals and waiting the diagnostic for a long period time that might increase the probability of expansion the injury cases. Consequently, various machine learning techniques have been formulated to decrease the time taken of decision making for diagnoses the breast cancer and that might minimize the mortality rate. The proposed system consists of two phases. Firstly, data pre-processing (data cleaning, selection) of the data mining are used in the breast cancer dataset taken from the University of California, Irvine machine learning repository in this stage we modified the Correlation Feature Selection (CFS) with Best First Search (BFS) established on the Discriminant Index (DI) so as to reduce the complexity of time and get high accuracy. Secondly, Bayesian Rough Set (BRS) classifier is applied to predict the breast cancer and help the inexperienced doctors to make decisions without need the direct discussion with the specialist doctors. The result of experiments showed the proposed system give high accuracy with less time of predication the disease.
Background: Breast cancer is the leading female cancer worldwide and in Iraq .Some mutations, particularly in BRCA1, significantly increase the risk of the disease.
Objectives: To demonstrate the frequency of BRCA1 in a group of high risk women with “positive family history’’ of breast cancer; correlating the immune expression of BRCA1 with some parameters of known prognostic significance.
Patients and Methods: Eighty-two female patients diagnosed with breast cancer (50 familial and 32 non familial) were included in the study .The mean age of the patients was 48.07. Immunohistochemistry was performed to assess the BRCA1 oncogene expression, Estrogen Receptor (ER), Progesterone Receptor (PR), Her 2 neu contents of the tumors.<
Mammography is at present one of the available method for early detection of masses or abnormalities which is related to breast cancer. The most common abnormalities that may indicate breast cancer are masses and calcifications. The challenge lies in early and accurate detection to overcome the development of breast cancer that affects more and more women throughout the world. Breast cancer is diagnosed at advanced stages with the help of the digital mammogram images. Masses appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. The incidence of breast cancer in women has increased significantly in recent years.
This paper proposes a computer aided diagnostic system for the extracti
Interleukin-33 [IL-33] is a specific ligand for the ST2 receptor, and a member of the
IL-1 family. It is a dual-function protein that acts both as an extracellular alarmin cytokine,
and an as an intracellular nuclear factor participates in maintaining barrier function by
regulating gene expression of IL-33 modulating tumor growth and anti-tumor immunity in
cancer patients. The present study aimed to investigate the role of IL-33 serum level and gene
polymorphism in Iraqi women with breast cancer. Materials and methods: Blood samples
were collected from 66 Iraqi patient women diagnosed with breast cancer, which were divided
into two groups: pre-treatment [PT] and under treatment with chemotherapy [UTC] patients in
Angiogenesis is important for tissue during normal physiological processes as well as in a number of diseases, including cancer. Drug resistance is one of the largest difficulties to antiangiogenesis therapy. Due to their lower cytotoxicity and stronger pharmacological advantage, phytochemical anticancer medications have a number of advantages over chemical chemotherapeutic drugs. In the current study, the effectiveness of AuNPs, AuNPs-GAL, and free galangin as an antiangiogenesis agent was evaluated. Different physicochemical and molecular approaches have been used including the characterization, cytotoxicity, scratch wound healing assay, and gene expression of VEGF and ERKI in MCF-7 and MDA-MB-231 human breast cancer cell line. Re
... Show MoreSeventy four Iraqi breast cancer paraffin blocks were collected from patients were attended to center health laboratory, histopathology department, Bagdad, Iraq. The patients information’s which included: name, age, and the pathological stage, grade, tumor size were obtained from the clinical records of the patients also relation with sex hormones was recorded. The cases which has been taken included invasive ductal and invasive lobular carcinoma type Women age were ranged from 24-80 years peak age frequency of tumor occurred in the category of more than 40 years old. Immunohistochemical expression of her-2/neu was from total 74 cases of infiltrative ductal carcinoma cases, 27(36.49%)were positive for Her-2/neu expression, 47(63.51%) were
... Show MoreThe study involved 120 women, who were distributed into two groups of breast tumor patients (30 malignant and 30 benign) and a group of controls (60 women). The patients were referred to the Center for Early Detection of Breast Tumor at Al-Alwayia Hospital for Gynecology and Obstetrics (Baghdad) during the period June-December 2011. They were investigated for the frequency of ABO blood group phenotypes, menopausal status, oral contraceptive use, body mass index and family history of breast cancer or other cancers. The results demonstrated that 60.0% of malignant cases clustered after the age 50 years, while it was 20.0% in benign cases. Fifty percent of malignant breast tumor patients reached menopause, while in benign cases, the corresp
... Show MoreDisease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature
... Show MoreBackground: Multifunctional cytokines play important and only partially defined roles in mammary tumor development and progression. Normal human mammary epithelial cells constitutively produce interleukin 6(IL-6) and a non-secreted form of tumor necrosis factor. Transformation of mammary epithelial cells by different oncogenesis is frequently associated with alterations of cytokine/ growth factor production and responsiveness.
Methods: We measured levels of 1L-6 in 84 females with breast cancer and examined their correlation with clinicopathological variables including stages of the disease and estrogen and
progesterone receptor expression on tumor cell.
Results: Our results revealed significantly higher