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Showing 4 results for Mammography
FORTRAN Code for Glandular Dose Calculation in Mammography Using Sobol-Wu Parameters
Mowlavi A A,
Volume 65, Issue 3 (6-2007)
Abstract
Background: Accurate computation of the radiation dose to the breast is essential to mammography. Various the thicknesses of breast, the composition of the breast tissue and other variables affect the optimal breast dose. Furthermore, the glandular fraction, which refers to the composition of the breasts, as partitioned between radiation-sensitive glandular tissue and the adipose tissue, also has an effect on this calculation. Fatty or fibrous breasts would have a lower value for the glandular fraction than dense breasts. Breast tissue composed of half glandular and half adipose tissue would have a glandular fraction in between that of fatty and dense breasts. Therefore, the use of a computational code for average glandular dose calculation in mammography is a more effective means of estimating the dose of radiation, and is accurate and fast.
Methods: In the present work, the Sobol-Wu beam quality parameters are used to write a FORTRAN code for glandular dose calculation in molybdenum anode-molybdenum filter (Mo-Mo), molybdenum anode-rhodium filter (Mo-Rh) and rhodium anode-rhodium filter (Rh-Rh) target-filter combinations in mammograms. The input parameters of code are: tube voltage in kV, half-value layer (HVL) of the incident x-ray spectrum in mm, breast thickness in cm (d), and glandular tissue fraction (g).
Results: The average glandular dose (AGD) variation against the voltage of the mammogram X-ray tube for d = 4 cm, HVL = 0.34 mm Al and g=0.5 for the three filter-target combinations, as well as its variation against the glandular fraction of breast tissue for kV=25, HVL=0.34, and d=4 cm has been calculated. The results related to the average glandular absorbed dose variation against HVL for kV = 28, d=4 cm and g= 0.6 are also presented. The results of this code are in good agreement with those previously reported in the literature.
Conclusion: The code developed in this study calculates the glandular dose quickly, and it is complete and accurate. Furthermore, it is user friendly and useful for dose optimizing in mammography imaging.
Imaging features of estrogen-negative breast cancers: a correlation study with human epidermal growth factor type II overexpression
Masumeh Gity , Ali Borhani , Mehrdad Mokri , Majid Shakiba , Morteza Atri , Nasim Batavani ,
Volume 76, Issue 8 (11-2018)
Abstract
Background: Estrogen-negative breast cancers have different clinical course, prognostic features and treatment response in comparison to estrogen receptor-positive (ER-positive) breast cancers. Human epidermal growth factor receptor 2 (HER2) oncoprotein has found to have a pivotal role in natural cell growth and cell division and is suggested to be directly related to tumor invasiveness in breast cancer patients. The purpose of this study was to retrospectively assess the mammography, ultrasound, and magnetic resonance imaging (MRI) features of estrogen negative breast cancers with and without overexpression of HER2/neu receptor.
Methods: In this cross-sectional retrospective study, mammographic, ultrasound and MRI features as well as HER2 status were assessed in patients with ER-negative breast cancer that were referred to Cancer Institute of Imam Khomeini Hospital Complex in Tehran from October 2015 to October 2017. Clinicopathologic data and mammography, ultrasound, and MRI features were reviewed and were correlated with HER2 status of estrogen-negative tumors.
Results: Of the 172 patients with ER-negative breast cancer, 101 patients were positive for HER2/neu receptor (58.8%). There was a significant correlation between HER2-positivity and tumor type (P=0.004). Among estrogen negative breast cancers, significant association were found between HER2 and tumor histologic grade (P=0.024) and TNM stage (P=0.021). HER2-positive tumors were more likely to present with microcalcification (P=0.007) and have irregular shapes (P=0.034) in mammography than HER2-negative tumors. No association was found between HER-2 status and tumor size, shape, margin, posterior feature, halo or orientation of the tumor in ultrasound. We also found no correlation between HER2 status and MRI features including mass shape or margin, internal enhancement pattern or curve type among estrogen-negative breast cancers.
Conclusion: Findings of this study showed that among estrogen-negative breast cancers, HER2/neu positive tumors are more likely to be diagnosed at higher stage and have higher histologic grade at the time of diagnosis. Tumor mass shape and microcalcification in mammography are found to be associated with HER2 status among patients with estrogen-negative breast cancer. 

A review of neural network detection methods for breast cancer: review article
Homayoon Yektaei, Mohammad Manthouri,
Volume 78, Issue 6 (9-2020)
Abstract
Breast cancer is the most common cancer among women and the earlier it is diagnosed, the easier it is to treat. The most common way to diagnose breast cancer is mammography. Mammography is a simple chest x-ray and a tool for early detection of non-palpable breast cancers and tumors. However, due to some limitations of this method such as low sensitivity especially in dense breasts, other methods such as 3d mammography, ultrasound and magnetic resonance imaging are often suggested to obtain additional useful information. Recently, computer-aided diagnostic or intelligent diagnostic have been developed to assist radiologists to improve diagnostic accuracy. In general, a computer system consists of four steps: pre-processing, dividing areas of interest, extracting and selecting features, and finally classification. Nowadays, the use of imaging techniques in the identification of patterns for diagnosis and automatic determination of breast cancer by mammography and even digital pathology (which is one of the emerging trends in modern medicine) reduces human errors and speeds up the diagnosis. In this article, We reviewed recent findings and their disadvantages and benefits in the diagnosis of breast cancer by neural networks, especially the artificial neural network, which is widely used in the diagnosis of cancers and intelligent breast cancers. This literature review shows that hybrid algorithms have been better at improving classification and detection accuracy. Providing a convenient way to diagnose tumors in the breast by computer-assisted diagnosis systems will be of great help to the physicians. Much work has been done in recent years to diagnose breast cancer, and many advances have been made in improving and diagnosing breast cancer by computer. All methods have a significant error percentage and are different depending on the type of breast, but compared to other types of neural networks, convolution and combining methods with convo have better results. Another advantage of the convoluted network is the automatic extraction of desirable features. Today, the best percentages of accuracy in detecting benign or malignant cancerous mass are achieved by convolution.
Deep learning-based CAD systems for mammography: A review article
Ali Ameri, Mahmoud Shiri, Masoumeh Gity , Mohammad Ali Akhaee,
Volume 79, Issue 5 (8-2021)
Abstract
Breast cancer is one of the most common types of cancer in women. Screening mammography is a low‑dose X‑ray examination of breasts, which is conducted to detect breast cancer at early stages when the cancerous tumor is too small to be felt as a lump. Screening mammography is conducted for women with no symptoms of breast cancer, for early detection of cancer when the cancer is most treatable and consequently greatly reduce the death rate from the breast cancer. Screening mammography should be performed every year for women age 45-54, and every two years for women age 55 and older who are in good health. A mammogram is read by a radiologist to diagnose cancer.
To assist radiologists in reading mammograms, computer-aided detection (CAD) systems have been developed which can identify suspicious lesions on mammograms. CADs can improve the accuracy and confidence level of radiologists in decision making and have been approved by FDA for clinical use. Traditional CAD systems work based on conventional machine learning (ML) and image processing algorithms. With recent advances in software and hardware resources, a great breakthrough in deep learning (DL) algorithms was followed, which revolutionized various engineering areas including medical technologies. Recently, DL models have been applied in CAD systems in mammograms and achieved outstanding performance. In contrast to conventional ML, DL algorithms eliminate the need for the tedious task of human-designed feature engineering, as they are capable of learning useful features automatically from the raw data (mammogram). One of the most common DL frameworks is the convolutional neural network (CNN). To localize lesions in a mammogram, a CNN should be applied in region‑based algorithms such as R‑CNN, Fast R‑CNN, Faster R‑CNN, and YOLO.
Proper training of a DL‑based CAD requires a large amount of annotated mammogram data, where cancerous lesions have been marked by an experienced radiologist. This highlights the importance of establishing a large, annotated mammogram dataset for the development of a reliable CAD system. This article provides a brief review of the state‑of‑the‑art techniques for DL‑based CAD in mammography.

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