Background: Breast cancer ranks the first among the Iraqi population since three decades and is currently forming a major public health problem being the second cause of death women. Novel management of breast cancer depends upon precise evaluation of their molecular subtypes; identified by Hormone (Estrogen and Progesterone) receptors and HER2 contents of the primary tumor.
Objective: To assess the rates of the different molecular breast cancer subtypes in the examined tissue specimens belonging to females diagnosed with breast cancer in Iraq; correlating the findings with those reported in the literature at the regional and global levels.
Patients and Methods: This retrospective study documented the findings of tissue biopsy exam

      Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the  most effective parameter, particularly when Age<49.5. Whereas  Ki67  appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimu

     The human epidermal growth factor receptor-2 (HER2) gene plays a critical role in breast cancer development and progression. HER2 overexpression characterizes a biologically and clinically aggressive breast cancer subtype. In this study, 60 samples from Iraqi women with breast cancer were collected and investigated for HER2 protein in the tissue by immunohistochemistry. Also, 20 samples from healthy Iraqi women were used as a control. The results showed that 18 (30 %) patients expressed the HER2 protein. A molecular study for single nucleotide polymorphism (SNP) was conducted on samples metastasizing to lymph nodes. DNA was extracted and polymerase chain reaction (PCR) was performed to amplify e

Background: although breast cancer in young women is less common and often overlooked, it is still considered a major health concern.
Objectives: to evaluate the demographic, clinical, radiological and histopathological characteristics of breast cancer among a sample of Iraqi women diagnosed under the age of 40 years.
Patients and methods: a retrospective study enrolled 73 females below the age of 40 years with a history of breast cancer. All data was extracted from an established information system database designed by the Principal Investigator of the Iraqi National Breast Cancer Research Project under supervision of the International Agency for Research on Cancer (IARC) over a 4-years period from 2011 to 2014.
Results: sevent

Background: although breast cancer in young women is less common and often overlooked, it is still considered a major health concern.
Objectives: to evaluate the demographic, clinical, radiological and histopathological characteristics of breast cancer among a sample of Iraqi women diagnosed under the age of 40 years.
Patients and methods: a retrospective study enrolled 73 females below the age of 40 years with a history of breast cancer. All data was extracted from an established information system database designed by the Principal Investigator of the Iraqi National Breast Cancer Research Project under supervision of the International Agency for Research on Cancer (IARC) over a 4-years period from 2011 to

The last few years witnessed great and increasing use in the field of medical image analysis. These tools helped the Radiologists and Doctors to consult while making a particular diagnosis. In this study, we used the relationship between statistical measurements, computer vision, and medical images, along with a logistic regression model to extract breast cancer imaging features. These features were used to tell the difference between the shape of a mass (Fibroid vs. Fatty) by looking at the regions of interest (ROI) of the mass. The final fit of the logistic regression model showed that the most important variables that clearly affect breast cancer shape images are Skewness, Kurtosis, Center of mass, and Angle, with an AUCROC of

Wisconsin Breast Cancer Dataset (WBCD) was employed to show the performance of the Adaptive Resonance Theory (ART), specifically the supervised ART-I Artificial Neural Network (ANN), to build a breast cancer diagnosis smart system. It was fed with different learning parameters and sets. The best result was achieved when the model was trained with 50% of the data and tested with the remaining 50%. Classification accuracy was compared to other artificial intelligence algorithms, which included fuzzy classifier, MLP-ANN, and SVM. We achieved the highest accuracy with such low learning/testing ratio.

In this work, Kinetic Phosphorescence Analyzer (KPA) has been used to measure the concentrations of uranium (UC) and Amorphous crystals (AMO) in urine samples of breast cancer patients in Baghdad. Additionally, a relation between UC and AMO with respect to patient's age has been deduced and studied.
Forty one urine samples of patients and five for healthy were taken from females lived in different residential area of Baghdad. The measured maximum UC value for urine samples of patients was 2.35 ± 0.053, the minimum value was 0.86 ± 0.034 μg/L, and an overall average was 1.6 ± 0.027 μg/L while the average UC for healthy females was 1.03 ± 0.020 μg/L.
From these results, AMO concentrations were found for all breast cancer patie