Barriers to Mammograms Among Women Who are Homeless

Purpose: The purpose of the study was to identify barriers to mammogram screening among women who are homeless. Knowing the barriers to mammogram screening will be useful to advanced practice nurses for it provides insight to understanding the perceived susceptibility, benefits, and barriers of women potentially amendable to intervention. Data sources: A descriptive survey was used with a convenience sample of 41 women who were homeless, between the ages of 20-70 years, and agreed to participate in this study. The research was conducted at two homeless shelters in an urban county in Northern California. Findings: Findings reflected positive perceptions recognizing the benefits of mammogram screenings, and minimal concern about potential negative aspects of having mammogram screenings. Additional data indicated that the sample believed they were less likely to get breast cancer during their life. The majority had no fmancial resources for a mammogram and did not know how to obtain a mammogram. However, if a free mammogram was available, 95% responded that they would take advantage of this essential screening test. Conclusions: Breast cancer is the second leading cause of death for all racial and ethnic populations in the United States. Since 1991, the National Health Care for the Homeless Council has integrated a human rights viewpoint to assure healthcare for everyone (National Health Care for the Homeless Council, 2006). Therefore, it is up to the community and healthcare providers to make sure that everyone, including women who are homeless, have access to mammography screening by eliminating barriers that prevent access. Implication for practice: Advanced practice clinicians, with their vast knowledge of community resources, must advocate for everyone, including women who are homeless, to promote access to mammography screening. The goal is to eliminate barriers that prevent this population from having a valuable screening procedure

Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification

Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods rely on regions of interest (ROIs) which require great efforts to annotate. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning (MIL) for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned ROIs. We explore three different schemes to construct deep multi-instance networks for whole mammogram classification. Experimental results on the INbreast dataset demonstrate the robustness of proposed networks compared to previous work using segmentation and detection annotations.Comment: MICCAI 2017 Camera Read

Reactions to uncertainty and the accuracy of diagnostic mammography.

BackgroundReactions to uncertainty in clinical medicine can affect decision making.ObjectiveTo assess the extent to which radiologists' reactions to uncertainty influence diagnostic mammography interpretation.DesignCross-sectional responses to a mailed survey assessed reactions to uncertainty using a well-validated instrument. Responses were linked to radiologists' diagnostic mammography interpretive performance obtained from three regional mammography registries.ParticipantsOne hundred thirty-two radiologists from New Hampshire, Colorado, and Washington.MeasurementMean scores and either standard errors or confidence intervals were used to assess physicians' reactions to uncertainty. Multivariable logistic regression models were fit via generalized estimating equations to assess the impact of uncertainty on diagnostic mammography interpretive performance while adjusting for potential confounders.ResultsWhen examining radiologists' interpretation of additional diagnostic mammograms (those after screening mammograms that detected abnormalities), a 5-point increase in the reactions to uncertainty score was associated with a 17% higher odds of having a positive mammogram given cancer was diagnosed during follow-up (sensitivity), a 6% lower odds of a negative mammogram given no cancer (specificity), a 4% lower odds (not significant) of a cancer diagnosis given a positive mammogram (positive predictive value [PPV]), and a 5% higher odds of having a positive mammogram (abnormal interpretation).ConclusionMammograms interpreted by radiologists who have more discomfort with uncertainty have higher likelihood of being recalled

Breast Cancer: Modelling and Detection

This paper reviews a number of the mathematical models used in cancer modelling and then chooses a specific cancer, breast carcinoma, to illustrate how the modelling can be used in aiding detection. We then discuss mathematical models that underpin mammographic image analysis, which complements models of tumour growth and facilitates diagnosis and treatment of cancer. Mammographic images are notoriously difficult to interpret, and we give an overview of the primary image enhancement technologies that have been introduced, before focusing on a more detailed description of some of our own recent work on the use of physics-based modelling in mammography. This theoretical approach to image analysis yields a wealth of information that could be incorporated into the mathematical models, and we conclude by describing how current mathematical models might be enhanced by use of this information, and how these models in turn will help to meet some of the major challenges in cancer detection

Predicting Nonadherence Behavior Towards Mammography Screening Guidelines

The goal of this research is to examine factors associated with nonadherence behavior toward mammography screening among U.S. women. The 2014 Behavioral Risk Factor Surveillance System (BRFSS) survey data was used for this study, allowing the model to represent a robust sample. A logistic regression model was developed to gain an understanding of influencing factors, including demographic, health-related and behavioral characteristics. Further analysis with logistic regression models stratified by age were conducted to control for the effect of age. The results show that demographic and health related information such as income, number of children, and BMI category can help intervention programs recognize women who are less likely to adhere to mammography screening guidelines. Behavioral factors are the strongest predictor for screening behaviors. It is crucial for women to have a personal physician or health professional that they can routinely see every year. Tracking frequency of doctor visits and routine medical procedures can give great insight into mammography nonadherence, which could ultimately help reduce breast cancer mortality in the U.S

Abnormality Detection in Mammography using Deep Convolutional Neural Networks

Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be trained directly on full mammogram images because of the loss of image details from resizing at input layers. Instead, our classifiers are trained on labelled image patches and then adapted to work on full mammogram images for localizing the abnormalities. State-of-the-art deep convolutional neural networks are compared on their performance of classifying the abnormalities. Experimental results indicate that VGGNet receives the best overall accuracy at 92.53\% in classifications. For localizing abnormalities, ResNet is selected for computing class activation maps because it is ready to be deployed without structural change or further training. Our approach demonstrates that deep convolutional neural network classifiers have remarkable localization capabilities despite no supervision on the location of abnormalities is provided.Comment: 6 page

A scalable Computer-Aided Detection system for microcalcification cluster identification in a pan-European distributed database of mammograms

A computer-aided detection (CADe) system for microcalcification cluster identification in mammograms has been developed in the framework of the EU-founded MammoGrid project. The CADe software is mainly based on wavelet transforms and artificial neural networks. It is able to identify microcalcifications in different kinds of mammograms (i.e. acquired with different machines and settings, digitized with different pitch and bit depth or direct digital ones). The CADe can be remotely run from GRID-connected acquisition and annotation stations, supporting clinicians from geographically distant locations in the interpretation of mammographic data. We report the FROC analyses of the CADe system performances on three different dataset of mammograms, i.e. images of the CALMA INFN-founded database collected in the Italian National screening program, the MIAS database and the so-far collected MammoGrid images. The sensitivity values of 88% at a rate of 2.15 false positive findings per image (FP/im), 88% with 2.18 FP/im and 87% with 5.7 FP/im have been obtained on the CALMA, MIAS and MammoGrid database respectively.Comment: 6 pages, 5 figures; Proceedings of the ITBS 2005, 3rd International Conference on Imaging Technologies in Biomedical Sciences, 25-28 September 2005, Milos Island, Greec