Breast lumps

About: Breast lumps is a research topic. Over the lifetime, 836 publications have been published within this topic receiving 10075 citations.

Breast Cancer Diagnosis and Prognosis Via Linear Programming

Abstract: Two medical applications of linear programming are described in this paper. Specifically, linear programming-based machine learning techniques are used to increase the accuracy and objectivity of breast cancer diagnosis and prognosis. The first application to breast cancer diagnosis utilizes characteristics of individual cells, obtained from a minimally invasive fine needle aspirate, to discriminate benign from malignant breast lumps. This allows an accurate diagnosis without the need for a surgical biopsy. The diagnostic system in current operation at University of Wisconsin Hospitals was trained on samples from 569 patients and has had 100% chronological correctness in diagnosing 131 subsequent patients. The second application, recently put into clinical practice, is a method that constructs a surface that predicts when breast cancer is likely to recur in patients that have had their cancers excised. This gives the physician and the patient better information with which to plan treatment, and may elimin...

Women's health beliefs about breast cancer and breast self-examination

Abstract: To investigate the nature of women's health beliefs about breast cancer and breast self-examination (BSE) and the extent of BSE practice, a questionnaire was administered to 122 women Ninety-seven percent (118) scored high in perceived benefits of BSE in reducing the threat of breast cancer and 87 percnet (106) scored high in perceived susceptibility to breast cancer Forty percent (48) practiced BSE monthly, but over 20 percent of the sample had high beliefs and were nonpracticers Thus, it cannot be concluded that beliefs cause behavior Other factors such as embarrassment or religious upbringing influence health beliefs and practices, it was found A majority of women who did practice BSE, furthermore, were unsure of their ability to detect abnormalities A separate group of 20 women with a history of breast lumps or cancer surgery had higher susceptibility beliefs, a higher rate of practice, no embarrassment in examining themselves, and more confidence in ability to detect abnormalities than the remainder of the sample

Breast-cancer early detection in low-income and middle-income countries: do what you can versus one size fits all

Abstract: Summary In general, rates of breast cancer are lower in low-income and middle-income countries (LMCs) than they are in more industrialised countries of North America and Europe. This lower incidence means that screening programmes aimed at early detection in asymptomatic women would have a lower yield—ie, substantially more women would need to be examined to find a true case of breast cancer. Because the average age of breast cancer is generally younger in LMCs, it has been suggested that breast-cancer screening programmes begin at an earlier age in these settings. However, the younger average age of breast cancer is mainly driven by the age distribution of the population, and fewer older women with breast cancer, rather than by higher age-specific incidence rates in younger women. Resources in LMCs might be better used to raise awareness and encourage more women with palpable breast lumps to seek and receive treatment in a timely manner.

Data mining classification techniques applied for breast cancer diagnosis and prognosis

Abstract: Breast Cancer Diagnosis and Prognosis are two medical applications pose a great challenge to the researchers. The use of machine learning and data mining techniques has revolutionized the whole process of breast cancer Diagnosis and Prognosis. Breast Cancer Diagnosis distinguishes benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast Cancer is likely to recur in patients that have had their cancers excised. Thus, these two problems are mainly in the scope of the classification problems. This study paper summarizes various review and technical articles on breast cancer diagnosis and prognosis. In this paper we present an overview of the current research being carried out using the data mining techniques to enhance the breast cancer diagnosis and prognosis. Breast cancer has become the leading cause of death in women in developed countries. The most effective way to reduce breast cancer deaths is detect it earlier. Early diagnosis requires an accurate and reliable diagnosis procedure that allows physicians to distinguish benign breast tumors from malignant ones without going for surgical biopsy. The objective of these predictions is to assign patients to either a "benign" group that is non- cancerous or a "malignant" group that is cancerous. The prognosis problem is the long-term outlook for the disease for patients whose cancer has been surgically removed. In this problem a patient is classified as a 'recur' if the disease is observed at some subsequent time to tumor excision and a patient for whom cancer has not recurred and may never recur. The objective of these predictions is to handle cases for which cancer has not recurred (censored data) as well as case for which cancer has recurred at a specific time. Thus, breast cancer diagnostic and prognostic problems are mainly in the scope of the widely discussed classification problems. These problems have attracted many researchers in computational intelligence, data mining, and statistics fields. Cancer research is generally clinical and/or biological in nature, data driven statistical research has become a common complement. Predicting the outcome of a disease is one of the most interesting and challenging tasks where to develop data mining applications. As the use of computers powered with automated tools, large volumes of medical data are being collected and made available to the medical research groups. As a result, Knowledge Discovery in Databases (KDD), which includes data mining techniques, has become a popular research tool for medical researchers to identify and exploit patterns and relationships among large number of variables, and made them able to predict the outcome of a disease using the historical cases stored within datasets. The objective of this study is to summarise various review and technical articles on diagnosis and prognosis of breast cancer. It gives an overview of the current research being carried out on various breast cancer datasets using the data mining techniques to enhance the breast cancer diagnosis and prognosis.

Breast biopsy avoidance: the value of normal mammograms and normal sonograms in the setting of a palpable lump.

Abstract: PURPOSE: To review the authors’ experience with patients who presented with breast lumps and had normal mammograms and normal sonograms. MATERIALS AND METHODS: The findings from 600 lumps in 486 women with no focal ultrasonographic (US) mass or mammographic finding in the area of clinical concern were retrospectively studied. Evaluated parameters included the individual reporting the lump, qualitative descriptors for the physical finding, mammographic density, US characteristics in the area of concern, whether there was a change in imaging and/or physical examination results, and whether there were diagnostic biopsy findings at follow-up. The study group included 540 lumps in 435 women who had a minimum mammographic and clinical follow-up of 2 years, as well as 60 additional lumps in 51 patients who underwent biopsy. RESULTS: No patient in the nonbiopsy group developed carcinoma at the initial site of concern during a mean mammographic and clinical follow-up period of 43 months, and all biopsy specimens w...