Determinants of Breast Cancer Screening Adherence During the COVID-19 Pandemic in a Cohort at Increased Inherited Cancer Risk in the United States.

BACKGROUND: We examined neighborhood characteristics concerning breast cancer screening annual adherence during the COVID-19 pandemic. METHODS: We analyzed 6673 female patients aged 40 or older at increased inherited cancer risk in 2 large health care systems (NYU Langone Health [NYULH] and the University of Utah Health [UHealth]). Multinomial models were used to identify predictors of mammogram screening groups (non-adherent, pre-pandemic adherent, pandemic period adherent) in comparison to adherent females. Potential determinants included sociodemographic characteristics and neighborhood factors. RESULTS: Comparing each cancer group in reference to the adherent group, a reduced likelihood of being non-adherent was associated with older age (OR: 0.97, 95% CI: 0.95, 0.99), a greater number of relatives with cancer (OR: 0.80, 95% CI: 0.75, 0.86), and being seen at NYULH study site (OR: 0.42, 95% CI: 0.29, 0.60). More relatives with cancer were correlated with a lesser likelihood of being pandemic period adherent (OR: 0.89, 95% CI: 0.81, 0.97). A lower likelihood of being pre-pandemic adherent was seen in areas with less education (OR: 0.77, 95% CI: 0.62, 0.96) and NYULH study site (OR: 0.35, 95% CI: 0.22, 0.55). Finally, greater neighborhood deprivation (OR: 1.47, 95% CI: 1.08, 2.01) was associated with being non-adherent. CONCLUSION: Breast screening during the COVID-19 pandemic was associated with being older, having more relatives with cancer, residing in areas with less educational attainment, and being seen at NYULH; non-adherence was linked with greater neighborhood deprivation. These findings may mitigate risk of clinically important screening delays at times of disruptions in a population at greater risk for breast cancer.

Breast Cancer Screening Adherence in the US During COVID-19: We examined predictors of breast cancer screening adherence during COVID-19 at two large healthcare systems. Adherence was associated with older age, having more relatives with a cancer history, and living in areas with less educational attainment. Nonadherence was associated with greater neighborhood deprivation.

Cancer Screening Experiences of Black Breast and Ovarian Cancer Patients and Family Members.

Black women experience disproportionate rates of advanced breast cancer diagnoses and mortality. Mammography is a proven and effective tool in early breast cancer detection and impacts patient outcomes. We interviewed Black women with a personal or family history of breast and/or ovarian cancer to understand their screening experiences and views. N = 61 individuals completed an interview. Interview transcripts were qualitatively analyzed for themes regarding clinical experiences, guideline adherence, and family sharing specific to Black women and their families. Most participants were college educated with active health insurance. Women in this cohort were knowledgeable about the benefits of mammography and described few barriers to adhering to annual mammogram guidelines. Some with first-degree family history were frustrated at insurance barriers to mammography before the age of 40. Participants were generally comfortable encouraging family and friends to receive mammograms and expressed a desire for a similar screening tool for ovarian cancer. However, they expressed concern that factors such as screening awareness and education, lack of insurance coverage, and other systematic barriers might prevent other Black women from receiving regular screening. Black women in this cohort reported high adherence to mammography guidelines, but expressed concern about cultural and financial barriers that may impact cancer screening access in the population more generally and contribute to disparities. Participants noted the importance of frank and open discussions of breast cancer screening in their families and community as a means of improving awareness.

Radiologist Self-training: a Study of Cancer Detection when Reading Mammograms at Work Clinics or Workshops.

Provision of online and remote specialist education and general continued professional education in medicine is a growing field. For radiology specifically, the ability to access web-based platforms that house high resolution medical images, and the high fidelity of simulated activities is increasingly growing due to positive changes in technology. This study investigates the differences in providing a self-directed specialist radiology education system in two modes: at clinics and in-person workshops. 335 Australian radiologists completed 562 readings of mammogram test sets through the web-based interactive BREAST platform with 325 at conference workshops and 237 at their workplaces. They engaged with test sets with each comprising of 60 mammogram cases (20 cancer and 40 normal). Radiologists marked the location of any cancers and had their performance measured via 5 metrics of diagnostic accuracy. Results show that the location of engagement with BREAST did not yield any significant difference in the performances of all radiologists and the same radiologists between two reading modes (P > 0.05). Radiologists who read screening mammograms for BreastScreen Australia performed better when they completed the test sets at designated workshops (P < 0.05), as was also the case for radiologists who read > 100 cases per week (P < 0.05). In contrast, radiologists who read less mammograms frequently recorded better performances in specificity and JAFROC at clinics (P < 0.05). Findings show that remotely accessed online education for specialised training and core skills building in radiology can provide a similar learning opportunity for breast radiologists when compared to on-site dedicated workshops at scientific meetings. For readers with high volumes of mammograms, a workshop setting may provide a superior experience while clinic setting is more helpful to less experienced readers.

Provider directory accuracy and timely access to mammograms in California.

Provider directory accuracy and access to timely appointments are crucial determinants of health outcomes. However, to our knowledge, no studies have analyzed provider directory accuracies or network adequacy for mammograms, an important preventive service. We fill that gap using large-scale, random, and representative surveys of provider directories and timely access for all managed care plans in California for mammogram providers, obtained from the Department of Managed Health Care for 2018 and 2019 for commercial, ACA marketplace, and Medicaid plans with more than 33,000 observations. Directory inaccuracies ranged from a low of 23 percent to a high of 38 percent. Consumers were able to schedule appointments with specific providers within 15 days in between 59 percent to 73 percent of cases. Comparisons of accuracy and adequacy between the three markets (commercial, ACA, Medicaid) were inconsistent. Even with one of the nation's strictest and most well-resourced regulatory regimes for provider networks, our findings show substantial inaccuracies and inadequacies exist.

Is it ethical to incentivize mammography screening in medicaid populations?- A policy review and conceptual analysis.

Mammography screening is controversial, as screening decisions are preference-sensitive: equally well-informed women do not universally get mammograms. Offering financial incentives for screening risks unduly influencing the decision-making process and may undermine voluntariness-yet incentives are being used in 4 US states (Arizona, Indiana, Kentucky, Michigan) under Section 1115 waivers. These initiatives are especially problematic in Medicaid populations who typically have lower health literacy and face the potential threat of disenrollment if they opt out. From June 2018 to January 2019, we analyzed publicly-available information on mammography incentives from the Centers for Medicare and Medicaid Services (CMS) and identified criteria (i.e. starting age and frequency of mammography) for incentive eligibility; income brackets of the affected beneficiaries; whether incentives were financial rewards or penalties; and evaluation arrangements. Several ethically relevant differences emerged: all states except Michigan incentivize screening at starting ages and frequencies that conflict with the US Preventive Services Task Force guidelines. Some incentives are rewards (e.g. reduced cost-sharing), and some penalties (e.g. disenrollment). Across states, rewards range from the equivalent of <1 min of work at state minimum wage to 9 days, and penalties range from 2 to 8 h. Political objectives, rather than evidence and ethics, appear to drive mammography incentive design. Programs risk harming vulnerable low-income populations. CMS and US states should therefore review variations and prevent unjustifiable practices, such as incentivizing 35-year-old women. Large incentives should be offered only if accompanied by robust studies. Incentives for using evidence-based mammography decision-aids, instead of mammography completion, better realize the intended goals.

Developing a Culturally and Linguistically Targeted Breast Cancer Educational Program for a Multicultural Population.

Breast cancer is the most commonly diagnosed cancer among women in the USA. Despite the availability of screening mammograms, significant disparities still exist in breast cancer outcomes of racial/ethnic and sexual/gender minorities. To address these disparities, the Mount Sinai Mobile Breast Health Program in New York City collaborated with local organizations to develop culturally and linguistically appropriate breast cancer education programs aimed at increasing screening mammogram utilization. Literature review of the barriers to mammography screening formed the basis to allow us to draft a narrative presentation for each targeted cultural group: African American, African-born, Chinese, Latina, and Muslim women, as well as LGBTQ individuals. The presentations were then tested with focus groups comprised of gatekeepers and members from local community and faith-based organizations which served the targeted populations. Feedback from focus groups and gatekeepers was incorporated into the presentations, and if necessary, the presentations were translated. Subsequently, the presentations were re-tested for appropriateness and reviewed for consistency in message, design, educational information, and slide sequencing. Our experience demonstrated the importance of collaborating with community organizations to provide educational content that is culturally and linguistically appropriate for minority groups facing barriers to uptake of screening mammography.

Adherence to Mammography and Pap Screening Guidelines Among Medically Underserved Women: the Role of Family Structures and Network-Level Behaviors.

Poor adherence to screening recommendations is an important contributing factor to disparities in breast and cervical cancer outcomes among women in the USA. Screening behaviors are multifactorial, but there has been limited focus on how family network beliefs and behaviors influence individual's likelihood to complete screening. This research aims to fill this gap by evaluating the role of family network composition and screening behaviors on women's likelihood to adhere to mammogram and pap screening recommendations. We used an ego network approach to analyze data from 137 families and their networks. Primary outcomes were whether an individual had received a mammogram in the past year and whether she had received a pap screening in the past 3 years. Network-level predictors included network composition (size of network, average age of network members, satisfaction with family communication) and network screening behaviors. We conducted multivariable logistic regressions to assess the influence of network-level variables on both mammogram and pap smears, adjusting for potential individual-level confounders. Each network had an average age of 47.9 years, and an average size of 3.05 women, with the majority of members being sisters (57.7%). We found differences in network screening behaviors by race, with Arab networks being less likely to have completed self-breast exams (OR = 0.21, 95%CI = 0.05-0.76, p = 0.02), ever a gotten pap screen (OR = 0.11, 95%CI = 0.01-0.85, p = 0.04), and gotten pap screening in the last 3 years (OR = 0.31, 95%CI = 0.10-0.99, p = 0.04) compared with African American networks. Network screening behaviors also strongly influenced the likelihood of an individual completing a similar screening behavior. This analysis sheds light on family network characteristics that influence screening behaviors among medically underserved women. These findings support the development and dissemination of screening interventions among female's family networks.

A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis.

BACKGROUND: Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic binary model for discriminating tissue in digital mammograms, as support tool for the radiologists. In particular, we compared the contribution of different methods on the feature selection process in terms of the learning performances and selected features. RESULTS: For each ROI, we extracted textural features on Haar wavelet decompositions and also interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg). Then a Random Forest binary classifier is trained on a subset of a sub-set features selected by two different kinds of feature selection techniques, such as filter and embedded methods. We tested the proposed model on 260 ROIs extracted from digital mammograms of the BCDR public database. The best prediction performance for the normal/abnormal and benign/malignant problems reaches a median AUC value of 98.16% and 92.08%, and an accuracy of 97.31% and 88.46%, respectively. The experimental result was comparable with related work performance. CONCLUSIONS: The best performing result obtained with embedded method is more parsimonious than the filter one. The SURF and MinEigen algorithms provide a strong informative content useful for the characterization of microcalcification clusters.

Convolutional neural network for automated mass segmentation in mammography.

BACKGROUND: Automatic segmentation and localization of lesions in mammogram (MG) images are challenging even with employing advanced methods such as deep learning (DL) methods. We developed a new model based on the architecture of the semantic segmentation U-Net model to precisely segment mass lesions in MG images. The proposed end-to-end convolutional neural network (CNN) based model extracts contextual information by combining low-level and high-level features. We trained the proposed model using huge publicly available databases, (CBIS-DDSM, BCDR-01, and INbreast), and a private database from the University of Connecticut Health Center (UCHC). RESULTS: We compared the performance of the proposed model with those of the state-of-the-art DL models including the fully convolutional network (FCN), SegNet, Dilated-Net, original U-Net, and Faster R-CNN models and the conventional region growing (RG) method. The proposed Vanilla U-Net model outperforms the Faster R-CNN model significantly in terms of the runtime and the Intersection over Union metric (IOU). Training with digitized film-based and fully digitized MG images, the proposed Vanilla U-Net model achieves a mean test accuracy of 92.6%. The proposed model achieves a mean Dice coefficient index (DI) of 0.951 and a mean IOU of 0.909 that show how close the output segments are to the corresponding lesions in the ground truth maps. Data augmentation has been very effective in our experiments resulting in an increase in the mean DI and the mean IOU from 0.922 to 0.951 and 0.856 to 0.909, respectively. CONCLUSIONS: The proposed Vanilla U-Net based model can be used for precise segmentation of masses in MG images. This is because the segmentation process incorporates more multi-scale spatial context, and captures more local and global context to predict a precise pixel-wise segmentation map of an input full MG image. These detected maps can help radiologists in differentiating benign and malignant lesions depend on the lesion shapes. We show that using transfer learning, introducing augmentation, and modifying the architecture of the original model results in better performance in terms of the mean accuracy, the mean DI, and the mean IOU in detecting mass lesion compared to the other DL and the conventional models.

Factors associated with being diagnosed with high severity of breast cancer: a population-based study in Queensland, Australia.

PURPOSE: This study explores factors that are associated with the severity of breast cancer (BC) at diagnosis. METHODS: Interviews were conducted among women (n = 3326) aged 20-79 diagnosed with BC between 2011 and 2013 in Queensland, Australia. High-severity cancers were defined as either Stage II-IV, Grade 3, or having negative hormone receptors at diagnosis. Logistic regression models were used to estimate odds ratios (ORs) of high severity BC for variables relating to screening, lifestyle, reproductive habits, family history, socioeconomic status, and area disadvantage. RESULTS: Symptom-detected women had greater odds (OR 3.38, 2.86-4.00) of being diagnosed with high-severity cancer than screen-detected women. Women who did not have regular mammograms had greater odds (OR 1.78, 1.40-2.28) of being diagnosed with high-severity cancer than those who had mammograms biennially. This trend was significant in both screen-detected and symptom-detected women. Screen-detected women who were non-smokers (OR 1.77, 1.16-2.71), postmenopausal (OR 2.01, 1.42-2.84), or employed (OR 1.46, 1.15-1.85) had greater odds of being diagnosed with high-severity cancer than those who were current smokers, premenopausal, or unemployed. Symptom-detected women being overweight (OR 1.67, 1.31-2.14), postmenopausal (OR 2.01, 1.43-2.82), had hormone replacement therapy (HRT) < 2 years (OR 1.60, 1.02-2.51) had greater odds of being diagnosed with high-severity cancer than those of healthy weight, premenopausal, had HRT > 10 years. CONCLUSION: Screen-detected women and women who had mammograms biennially had lower odds of being diagnosed with high-severity breast cancer, which highlighted the benefit of regular breast cancer screening. Women in subgroups who are more likely to have more severe cancers should be particularly encouraged to participate in regular mammography screening.

Deep Learning Computer-Aided Diagnosis for Breast Lesion in Digital Mammogram.

For computer-aided diagnosis (CAD), detection, segmentation, and classification from medical imagery are three key components to efficiently assist physicians for accurate diagnosis. In this chapter, a completely integrated CAD system based on deep learning is presented to diagnose breast lesions from digital X-ray mammograms involving detection, segmentation, and classification. To automatically detect breast lesions from mammograms, a regional deep learning approach called You-Only-Look-Once (YOLO) is used. To segment breast lesions, full resolution convolutional network (FrCN), a novel segmentation model of deep network, is implemented and used. Finally, three conventional deep learning models including regular feedforward CNN, ResNet-50, and InceptionResNet-V2 are separately adopted and used to classify or recognize the detected and segmented breast lesion as either benign or malignant. To evaluate the integrated CAD system for detection, segmentation, and classification, the publicly available and annotated INbreast database is used over fivefold cross-validation tests. The evaluation results of the YOLO-based detection achieved detection accuracy of 97.27%, Matthews's correlation coefficient (MCC) of 93.93%, and F1-score of 98.02%. Moreover, the results of the breast lesion segmentation via FrCN achieved an overall accuracy of 92.97%, MCC of 85.93%, Dice (F1-score) of 92.69%, and Jaccard similarity coefficient of 86.37%. The detected and segmented breast lesions are classified via CNN, ResNet-50, and InceptionResNet-V2 achieving an average overall accuracies of 88.74%, 92.56%, and 95.32%, respectively. The performance evaluation results through all stages of detection, segmentation, and classification show that the integrated CAD system outperforms the latest conventional deep learning methodologies. We conclude that our CAD system could be used to assist radiologists over all stages of detection, segmentation, and classification for diagnosis of breast lesions.

A similarity measure method fusing deep feature for mammogram retrieval.

BACKGROUND: Breast cancer is one of the most important malignant tumors among women causing a serious impact on women's lives and mammography is one the most important methods for breast examination. When diagnosing the breast disease, radiologists sometimes may consult some previous diagnosis cases as a reference. But there are many previous cases and it is important to find which cases are the similar cases, which is a big project costing lots of time. Medical image retrieval can provide objective reference information for doctors to diagnose disease. The method of fusing deep features can improve the retrieval accuracy, which solves the "semantic gap" problem caused by only using content features and location features. METHODS: A similarity measure method combining deep feature for mammogram retrieval is proposed in this paper. First, the images are pre-processed to extract the low-level features, including content features and location features. Before extracting location features, registration with the standard image is performed. Then, the Convolutional Neural Network, the Stacked Auto-encoder Network, and the Deep Belief Network are built to extract the deep features, which are regarded as high-level features. Next, content similarity and deep similarity are calculated separately using the Euclidean distance between the query image and the dataset images. The location similarity is obtained by calculating the ratio of intersection to union of the mass regions. Finally, content similarity, location similarity, and deep similarity are fused to form the image fusion similarity. According to the similarity, the specified number of the most similar images can be returned. RESULTS: In the experiment, 740 MLO mammograms are used, which are from women in Northeast China. The content similarity, location similarity, and deep similarity are fused by different weight coefficients. When only considering low-level features, the results are better with fusing 60% content feature similarity and 40% lesion location feature similarity. On this basis, CNN deep similarity, DBN deep similarity, and SAE deep similarity are fused separately. The experiments show that when fusing 60% DBN deep feature similarity and 40% low-level feature similarity, the results have obvious advantages. At this time, the precision is 0.745, recall is 0.850, comprehensive evaluation index is 0.794. CONCLUSIONS: We propose a similarity measure method fusing deep feature, content feature, and location feature. The retrieval results show that the precision and recall of this method have obvious advantage, compared with the content-based image retrieval and location-based image retrieval.

Deep convolutional neural networks for mammography: advances, challenges and applications.

BACKGROUND: The limitations of traditional computer-aided detection (CAD) systems for mammography, the extreme importance of early detection of breast cancer and the high impact of the false diagnosis of patients drive researchers to investigate deep learning (DL) methods for mammograms (MGs). Recent breakthroughs in DL, in particular, convolutional neural networks (CNNs) have achieved remarkable advances in the medical fields. Specifically, CNNs are used in mammography for lesion localization and detection, risk assessment, image retrieval, and classification tasks. CNNs also help radiologists providing more accurate diagnosis by delivering precise quantitative analysis of suspicious lesions. RESULTS: In this survey, we conducted a detailed review of the strengths, limitations, and performance of the most recent CNNs applications in analyzing MG images. It summarizes 83 research studies for applying CNNs on various tasks in mammography. It focuses on finding the best practices used in these research studies to improve the diagnosis accuracy. This survey also provides a deep insight into the architecture of CNNs used for various tasks. Furthermore, it describes the most common publicly available MG repositories and highlights their main features and strengths. CONCLUSIONS: The mammography research community can utilize this survey as a basis for their current and future studies. The given comparison among common publicly available MG repositories guides the community to select the most appropriate database for their application(s). Moreover, this survey lists the best practices that improve the performance of CNNs including the pre-processing of images and the use of multi-view images. In addition, other listed techniques like transfer learning (TL), data augmentation, batch normalization, and dropout are appealing solutions to reduce overfitting and increase the generalization of the CNN models. Finally, this survey identifies the research challenges and directions that require further investigations by the community.

Vascular chest wall lesion mimicking a breast tumor on screening mammograms: Report of a case.

Vascular lesions in the chest wall muscles are extremely rare and can cause diagnostic difficulties on screening mammograms. We describe a case of venous malformation of the pectoralis muscle, diagnosed during routine screening, in a 60-year-old woman. The mammograms showed a mass over the chest wall, projecting in the breast parenchyma. The ultrasound was not diagnostic. The definite diagnosis was made using MRI, and to our knowledge, only 1 similar case has been reported so far, but this is the only asymptomatic case depicted during screening services.

The influence of socio-cultural factors on breast cancer screening behaviors in Lagos, Nigeria.

Culture has been shown to influence health beliefs and health-related behaviors by influencing the type of health information to which women have been exposed and shapes health and illness perceptions and practices. To increase screening rates, cultural influences should be considered as important correlates of screening behaviors for breast cancer. This study used semi-structured interviews of women attending a cancer screening facility in Lagos, Nigeria guided by the PEN-3 model to describe culturally relevant factors that shape attitudes toward breast cancer and breast cancer screening. Religion was the most prominent theme and was shown to have positive, negative and existential effect on breast cancer perceptions. Other major themes observed were related to family and traditional beliefs. The results from this study could be used to develop and implement culturally relevant cancer prevention interventions, strategies, and recommendations to overcome screening barriers in an effort to increase breast cancer participation and awareness among Nigerian women.

Suspicious Lesion Segmentation on Brain, Mammograms and Breast MR Images Using New Optimized Spatial Feature Based Super-Pixel Fuzzy C-Means Clustering.

Suspicious lesion or organ segmentation is a challenging task to be solved in most of the medical image analyses, medical diagnoses and computer diagnosis systems. Nevertheless, various image segmentation methods were proposed in the previous studies with varying success levels. But, the image segmentation problems such as lack of versatility, low robustness, high complexity and low accuracy in up-to-date image segmentation practices still remain unsolved. Fuzzy c-means clustering (FCM) methods are very well suited for segmenting the regions. The noise-free images are effectively segmented using the traditional FCM method. However, the segmentation result generated is highly sensitive to noise due to the negligence of spatial information. To solve this issue, super-pixel-based FCM (SPOFCM) is implemented in this paper, in which the influence of spatially neighbouring and similar super-pixels is incorporated. Also, a crow search algorithm is adopted for optimizing the influential degree; thereby, the segmentation performance is improved. In clinical applications, the SPOFCM feasibility is verified using the multi-spectral MRIs, mammograms and actual single spectrum on performing tumour segmentation tests for SPOFCM. Ultimately, the competitive, renowned segmentation techniques such as k-means, entropy thresholding (ET), FCM, FCM with spatial constraints (FCM_S) and kernel FCM (KFCM) are used to compare the results of proposed SPOFCM. Experimental results on multi-spectral MRIs and actual single-spectrum mammograms indicate that the proposed algorithm can provide a better performance for suspicious lesion or organ segmentation in computer-assisted clinical applications.

Breast mass detection and diagnosis using fused features with density.

BACKGROUND: The morbidity of breast cancer has been increased in these years and ranked the first of all female diseases. Computer-aided diagnosis techniques for mammograms can help radiologists find early breast lesions. In mammograms, the degree of malignancy of the tumor is not only related to its morphology and texture features, but also closely related to the density of the tumor. However, in the current research on breast masses detection and diagnosis, people usually use the fusion feature of morphology and texture but neglect density, or only the density feature is considered. Therefore, this paper proposes a method to detect and diagnose the breast mass using fused features with density. METHODS: In this paper, we first propose a method based on sub-region clustering to detect the breast mass. The breast region is divided into sub-regions of equal size, and each sub-region is extracted based on local density feature, after that, an Unsupervised ELM (US-ELM) is used for clustering to complete the mass detection. Second, the feature model is constructed based on the mass. This model is composed of the mass region density feature, morphology feature and texture feature. And Genetic Algorithm is used for feature selection, and the optimized feature model is formed. Finally, ELM is used to diagnose benign or malignant mass. RESULTS: An experiment on the real dataset of 480 mammograms in Northeast China shows that our proposed method can effectively improve the detection and diagnosis accuracy of breast masses, where we obtained 0.9184 precision in detection of breast masses and 0.911 accuracy in diagnosis of breast masses. CONCLUSIONS: We have proposed a mass detection system, which achieves better detection accuracy performance than the existing state-of-art algorithm. We also propose a mass diagnosis system based on the fused features with density, which is more efficient than other feature model and classifier on the same dataset.

A similarity measure method combining location feature for mammogram retrieval.

BACKGROUND: Breast cancer, the most common malignancy among women, has a high mortality rate in clinical practice. Early detection, diagnosis and treatment can reduce the mortalities of breast cancer greatly. The method of mammogram retrieval can help doctors to find the early breast lesions effectively and determine a reasonable feature set for image similarity measure. This will improve the accuracy effectively for mammogram retrieval. METHODS: This paper proposes a similarity measure method combining location feature for mammogram retrieval. Firstly, the images are pre-processed, the regions of interest are detected and the lesions are segmented in order to get the center point and radius of the lesions. Then, the method, namely Coherent Point Drift, is used for image registration with the pre-defined standard image. The center point and radius of the lesions after registration are obtained and the standard location feature of the image is constructed. This standard location feature can help figure out the location similarity between the image pair from the query image to each dataset image in the database. Next, the content feature of the image is extracted, including the Histogram of Oriented Gradients, the Edge Direction Histogram, the Local Binary Pattern and the Gray Level Histogram, and the image pair content similarity can be calculated using the Earth Mover's Distance. Finally, the location similarity and content similarity are fused to form the image fusion similarity, and the specified number of the most similar images can be returned according to it. RESULTS: In the experiment, 440 mammograms, which are from Chinese women in Northeast China, are used as the database. When fusing 40% lesion location feature similarity and 60% content feature similarity, the results have obvious advantages. At this time, precision is 0.83, recall is 0.76, comprehensive indicator is 0.79, satisfaction is 96.0%, mean is 4.2 and variance is 17.7. CONCLUSIONS: The results show that the precision and recall of this method have obvious advantage, compared with the content-based image retrieval.

Detection and Segmentation of Pectoral Muscle on MLO-View Mammogram Using Enhancement Filter.

The presence of predominant density region of the pectoral muscle in Medio-Lateral Oblique (MLO) view of the mammograms can affect or bias the results of mammograms processing for breast cancer detection using intensity based methods. Therefore, to improve the diagnostic performance of breast cancer detection using computer-aided system, identification and segmentation of pectoral muscle is an important task. This paper presents, an intensity based approach to identify the pectoral region in mammograms. In the presented approach enhancement mask and threshold technique is used to enhance and select the pectoral region and boundary points respectively, to find the boundary of pectoral muscle. Then curve fitting by Least Square Error (LSE) method is used to refine the rough initial boundaries. The proposed approach was applied on 320 mammograms from mini-Mammographic Image Analysis Society (mini-MIAS) database of 322 mammograms, with acceptable rate of 96.56% from radiologist experts. The performance evaluation for pectoral muscle segmentation, based on Hausdorff distance (H d ), False Positive (FP) and False Negative (FN) rate, shows the usefulness and effectiveness of the proposed approach.

Mental health and breast cancer screening utilization among older Hispanic women.

Considerable racial and ethnic differences exist in the way the burden of cancer is experienced in the United States for older Hispanic women. This study utilized data from the 2008 wave of the Health and Retirement Study to investigate the mental health factors associated with older Hispanic women's participation in breast cancer screening services. Logistic regression models were used. Findings indicated that anxiety and positive affect were associated with a greater likelihood of participating in breast cancer screening. Despite ongoing national conversations, evidence indicates there is agreement that underserved women need to be screened, particularly the older Hispanic population.