Co-design of an intervention to optimize mammographic screening participation in women with obesity and/or physical disabilities

BACKGROUND

Mammographic breast screening/rescreening rates are suboptimal for women with obesity and/or physical disabilities. This study describes development of an intervention framework targeting obesity- and disability-related barriers to improve participation.

METHODS

Mixed methods combined a systematic review with first-person perspectives to optimise screening engagement among women with obesity and/or physical disabilities. Phase 1 (systematic review) was conducted following the PRISMA framework. Phase 2 involved in-depth interviews with n = 8 women with lived experience of obesity and/or physical disabilities. An inductive coding approach was applied to the data which was then combined with Phase 1 results to develop the intervention framework.

RESULTS

Six studies were included in the systematic review. Tailored education based on individual risk increased willingness to undergo mammographic screening. Recommendations to improve the screening experience included partnerships with consumers, targeted messaging, and enhanced professional development for breast screening staff. Participants also identified strategies to improve the uptake of screening and the experience itself.

CONCLUSION

Development and evaluation of interventions informed by frameworks like the one developed in this study are needed to improve engagement in screening to promote regular participation among women with physical disabilities and/or obesity.

IMPLICATIONS FOR PRACTICE

Successful implementation of practice interventions co-designed by women with obesity and/or physical disabilities are likely to improve their breast screening participation. Enhanced training of radiographers aimed at upskilling in empathetic communication around required manoeuvring and potentially longer screening times for clients with obesity and/or physical disabilities may encourage more positive client practitioner interactions. Client information aimed at women with obesity should include information on how to prepare for the appointment and explain there may be equipment limitations compromising imaging which may not be completed at an initial appointment.

GravityNet for end-to-end small lesion detection

This paper introduces a novel one-stage end-to-end detector specifically designed to detect small lesions in medical images. Precise localization of small lesions presents challenges due to their appearance and the diverse contextual backgrounds in which they are found. To address this, our approach introduces a new type of pixel-based anchor that dynamically moves towards the targeted lesion for detection. We refer to this new architecture as GravityNet, and the novel anchors as gravity points since they appear to be "attracted" by the lesions. We conducted experiments on two well-established medical problems involving small lesions to evaluate the performance of the proposed approach: microcalcifications detection in digital mammograms and microaneurysms detection in digital fundus images. Our method demonstrates promising results in effectively detecting small lesions in these medical imaging tasks.

DeepMiCa: Automatic segmentation and classification of breast MIcroCAlcifications from mammograms

BACKGROUND AND OBJECTIVE

Breast cancer is the world's most prevalent form of cancer. The survival rates have increased in the last years mainly due to factors such as screening programs for early detection, new insights on the disease mechanisms as well as personalised treatments. Microcalcifications are the only first detectable sign of breast cancer and diagnosis timing is strongly related to the chances of survival. Nevertheless microcalcifications detection and classification as benign or malignant lesions is still a challenging clinical task and their malignancy can only be proven after a biopsy procedure. We propose DeepMiCa, a fully automated and visually explainable deep-learning based pipeline for the analysis of raw mammograms with microcalcifications. Our aim is to propose a reliable decision support system able to guide the diagnosis and help the clinicians to better inspect borderline difficult cases.

METHODS

DeepMiCa is composed by three main steps: (1) Preprocessing of the raw scans (2) Automatic patch-based Semantic Segmentation using a UNet based network with a custom loss function appositely designed to deal with extremely small lesions (3) Classification of the detected lesions with a deep transfer-learning approach. Finally, state-of-the-art explainable AI methods are used to produce maps for a visual interpretation of the classification results. Each step of DeepMiCa is designed to address the main limitations of the previous proposed works resulting in a novel automated and accurate pipeline easily customisable to meet radiologists' needs.

RESULTS

The proposed segmentation and classification algorithms achieve an area under the ROC curve of 0.95 and 0.89 respectively. Compared to previously proposed works, this method does not require high performance computational resources and provides a visual explanation of the final classification results.

CONCLUSION

To conclude, we designed a novel fully automated pipeline for detection and classification of breast microcalcifications. We believe that the proposed system has the potential to provide a second opinion in the diagnosis process giving the clinicians the opportunity to quickly visualise and inspect relevant imaging characteristics. In the clinical practice the proposed decision support system could help reduce the rate of misclassified lesions and consequently the number of unnecessary biopsies.

Computer-Aided Breast Cancer Diagnosis: A Study of Breast Imaging Modalities and Mammogram Repositories

The accurate assessment or diagnosis of breast cancer depends on image acquisition and image analysis and interpretation. The accurate assessment or diagnosis of breast cancer depends on image acquisition and image analysis and interpretation. The expert radiologist makes image interpretation, and this process has been greatly benefited by computer technology. For image acquisition, various imaging modalities have been developed and used over the years. This research examines several imaging modalities and their associated benefits and drawbacks. Commonly used parameters such as sensitivity and specificity are also offered to evaluate the usefulness of different imaging modalities. The main focus of the research is on mammograms. Despite the availability of breast cancer datasets of imaging modalities such as MRI, ultrasounds, and thermograms, mammogram datasets are used mainly by the domain researcher. They are considered an international gold standard for the early detection of breast cancer. We discussed and analyzed widely used and publicly available mammogram repositories. We further discussed some common key constraints related to mammogram datasets to develop the deep learning based computer-aided diagnosis (CADx) systems for breast cancer. The ideas for their improvements have also been presented.

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.

Towards non-data-hungry and fully-automated diagnosis of breast cancer from mammographic images

Analysing local texture and generating features are two key issues for automatic cancer detection in mammographic images. Recent researches have shown that deep neural networks provide a promising alternative to hand-driven features which suffer from curse of dimensionality and low accuracy rates. However, large and balanced training data are foremost requirements for deep learning-based models and these data are not always available publicly. In this work, we propose a fully-automated method for breast cancer diagnosis that performs training using small sets of data. Feature extraction from mammographic images is performed using a genetic-programming-based descriptor that exploits statistics on a local binary pattern-like local distribution defined in each pixel. The effectiveness of the suggested method is demonstrated on two challenging datasets, (1) the digital database for screening mammography and (2) the mammographic image analysis society digital mammogram database, for content-based image retrieval as well as for abnormality/malignancy classification. The experimental results show that the proposed method outperforms or achieves comparable results with deep learning-based methods even those with transfer learning and/or data-augmentation.

FS-UNet: Mass segmentation in mammograms using an encoder-decoder architecture with feature strengthening

Breast mass segmentation in mammograms is still a challenging and clinically valuable task. In this paper, we propose an effective and lightweight segmentation model based on convolutional neural networks to automatically segment breast masses in whole mammograms. Specifically, we first developed feature strengthening modules to enhance relevant information about masses and other tissues and improve the representation power of low-resolution feature layers with high-resolution feature maps. Second, we applied a parallel dilated convolution module to capture the features of different scales of masses and fully extract information about the edges and internal texture of the masses. Third, a mutual information loss function was employed to optimise the accuracy of the prediction results by maximising the mutual information between the prediction results and the ground truth. Finally, the proposed model was evaluated on both available INbreast and CBIS-DDSM datasets, and the experimental results indicated that our method achieved excellent segmentation performance in terms of dice coefficient, intersection over union, and sensitivity metrics.

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.

Attending to the Psychosocial Needs of Older Hispanic, Black and Non-Hispanic White Women and Their Breast Cancer Screening Behaviors

BACKGROUND

Cancer risk increases with age. Despite breast cancer screening guidelines, older minorities are less likely to obtain screenings. Many factors influence cancer screening participation, though the literature rarely examines factors influencing cancer screening in older adult minority populations.

METHODS

Using 2008 and 2012 waves of data from the Health and Retirement study, we examined and compared the relationships between psychosocial factors and breast screening participation among older African American, Hispanic and non-Hispanic White women. We utilized logistic regression to determine the influence of psychosocial factors (satisfaction with aging, religiosity, perceived control, emotions, purpose in life) in 2008 predicting breast cancer screening participation in 2012, given the increasing importance of understanding health behaviors as predicted by prior circumstances. While controlling for other variables, the major findings demonstrated that the odds of having a mammogram among Hispanics decreased as feelings that 'things were getting worse' with age intensified; and screening was more likely among Hispanic religious women. The odds of obtaining a mammogram increased with increasing purpose in life for Hispanics.

CONCLUSIONS AND IMPLICATIONS

These findings suggest the need for comprehensive geriatric assessments to understand the perspectives of older minority women, and provides formative data to inform shared decision-making interventions.

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.

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 multi-context CNN ensemble for small lesion detection

In this paper, we propose a novel method for the detection of small lesions in digital medical images. Our approach is based on a multi-context ensemble of convolutional neural networks (CNNs), aiming at learning different levels of image spatial context and improving detection performance. The main innovation behind the proposed method is the use of multiple-depth CNNs, individually trained on image patches of different dimensions and then combined together. In this way, the final ensemble is able to find and locate abnormalities on the images by exploiting both the local features and the surrounding context of a lesion. Experiments were focused on two well-known medical detection problems that have been recently faced with CNNs: microcalcification detection on full-field digital mammograms and microaneurysm detection on ocular fundus images. To this end, we used two publicly available datasets, INbreast and E-ophtha. Statistically significantly better detection performance were obtained by the proposed ensemble with respect to other approaches in the literature, demonstrating its effectiveness in the detection of small abnormalities.

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.

Usefulness of the twinkling artifact on Doppler ultrasound for the detection of breast microcalcifications

OBJECTIVE

To determine whether the twinkling artifact on Doppler ultrasound imaging corresponds to microcalcifications previously seen on mammograms and to evaluate the usefulness of this finding in the ultrasound management of suspicious microcalcifications.

MATERIAL AND METHODS

We used ultrasonography to prospectively examine 46 consecutive patients with groups of microcalcifications suspicious for malignancy identified at mammography, searching for the presence of the twinkling artifact to identify the microcalcifications. Once we identified the microcalcifications, we obtained core-needle biopsy specimens with 11G needles and then used X-rays to check the specimens for the presence of microcalcifications. We analyzed the percentage of detection and obtainment of microcalcifications by core-needle biopsy with this technique and the radiopathologic correlation. Microcalcifications that were not detected by ultrasound or discordant lesions were biopsied by stereotaxy at another center. We also used ultrasound guidance for preoperative marking with clips, usually orienting them radially.

RESULTS

We identified and biopsied 41 of the 46 lesions under ultrasound guidance, including 24 of 25 carcinomas (17 in situ). B-mode ultrasound was sufficient for biopsying the microcalcifications in 14 patients, although the presence of the twinkling artifact increased the number of microcalcifications detected and thus enabled more accurate preoperative marking. Thanks to the twinkling sign, we were able to identify 27 additional groups of microcalcifications (89% vs. 30%; p < 0.05). All the surgical specimens had margins free of disease.

CONCLUSIONS

The twinkling artifact is useful for microcalcifications in ultrasound examinations, enabling a significant increase in the yield of ultrasound-guided biopsies and better preoperative marking of groups of microcalcifications.

A deep learning approach for the analysis of masses in mammograms with minimal user intervention

We present an integrated methodology for detecting, segmenting and classifying breast masses from mammograms with minimal user intervention. This is a long standing problem due to low signal-to-noise ratio in the visualisation of breast masses, combined with their large variability in terms of shape, size, appearance and location. We break the problem down into three stages: mass detection, mass segmentation, and mass classification. For the detection, we propose a cascade of deep learning methods to select hypotheses that are refined based on Bayesian optimisation. For the segmentation, we propose the use of deep structured output learning that is subsequently refined by a level set method. Finally, for the classification, we propose the use of a deep learning classifier, which is pre-trained with a regression to hand-crafted feature values and fine-tuned based on the annotations of the breast mass classification dataset. We test our proposed system on the publicly available INbreast dataset and compare the results with the current state-of-the-art methodologies. This evaluation shows that our system detects 90% of masses at 1 false positive per image, has a segmentation accuracy of around 0.85 (Dice index) on the correctly detected masses, and overall classifies masses as malignant or benign with sensitivity (Se) of 0.98 and specificity (Sp) of 0.7.

Robust Automatic Pectoral Muscle Segmentation from Mammograms Using Texture Gradient and Euclidean Distance Regression

In computer-aided diagnosis (CAD) of mediolateral oblique (MLO) view of mammogram, the accuracy of tissue segmentation highly depends on the exclusion of pectoral muscle. Robust methods for such exclusions are essential as the normal presence of pectoral muscle can bias the decision of CAD. In this paper, a novel texture gradient-based approach for automatic segmentation of pectoral muscle is proposed. The pectoral edge is initially approximated to a straight line by applying Hough transform on Probable Texture Gradient (PTG) map of the mammogram followed by block averaging with the aid of approximated line. Furthermore, a smooth pectoral muscle curve is achieved with proposed Euclidean Distance Regression (EDR) technique and polynomial modeling. The algorithm is robust to texture and overlapping fibro glandular tissues. The method is validated with 340 MLO views from three databases-including 200 randomly selected scanned film images from miniMIAS, 100 computed radiography images and 40 full-field digital mammogram images. Qualitatively, 96.75 % of the pectoral muscles are segmented with an acceptable pectoral score index. The proposed method not only outperforms state-of-the-art approaches but also accurately quantifies the pectoral edge. Thus, its high accuracy and relatively quick processing time clearly justify its suitability for CAD.

Using mammograms to predict preventive health services behavior and mortality in women

This study examined whether mammography receipt was associated with mortality due to causes other than breast cancer, hypothesizing that mammography screening was a proxy for the predisposition to seek preventive health behaviors. Using data on 89,574 women from the 2000 National Health Interview Survey and National Death Index, a discrete-time hazard model estimated the mortality from any cause except breast cancer as a function of screening status. Receiving a mammogram was associated with a 24% reduction in the likelihood of death all causes except breast cancer. These odds were reduced to 21.1% when demographic and socioeconomic variables are added and reduced further to 20.9% when health resource variables were added. The final adjusted model shows that women who received a mammogram had reduced their probability of death by 20%. These results suggest women who undergo mammograms may be more likely to seek other preventive health services or engage in healthy behaviors that affect mortality. While the use of mammograms to predict breast cancer mortality merits further consideration, if a proxy for a woman's predisposition for additional preventive screenings, encouraging mammography may be a pivotal pathway for preventing mortality due to other causes for women.

Radiology resident mammography training: interpretation difficulty and error-making patterns

RATIONALE AND OBJECTIVES

The purpose of this study was to better understand the concept of mammography difficulty and how it affects radiology resident performance.

MATERIALS AND METHODS

Seven radiology residents and three expert breast imagers reviewed 100 mammograms, consisting of bilateral medial lateral oblique and craniocaudal views, using a research workstation. The cases consisted of normal, benign, and malignant findings. Participants identified abnormalities and scored the difficulty and malignant potential for each case. Resident performance (sensitivity, specificity, and area under the receiver operating characteristic curve [AUC]) was calculated for self- and expert-assessed high and low difficulties.

RESULTS

For cases classified by self-assessed difficulty, the resident AUCs were 0.667 for high difficulty and 0.771 for low difficulty cases (P = .010). Resident sensitivities were 0.707 for high and 0.614 for low difficulty cases (P = .113). Resident specificities were 0.583 for high and 0.905 for low difficulty cases (P < .001). For cases classified by expert-assessed difficulty, the resident AUCs were 0.583 for high and 0.783 for low difficulty cases (P = .001). Resident sensitivities were 0.558 for high and 0.796 for low difficulty cases (P < .001). Resident specificities were 0.714 for high and 0.740 for low difficulty cases (P = .807).

CONCLUSIONS

Increased self- and expert-assessed difficulty is associated with a decrease in resident performance in mammography. However, while this lower performance is due to a decrease in specificity for self-assessed difficulty, it is due to a decrease in sensitivity for expert-assessed difficulty. These trends suggest that educators should provide a mix of self- and expert-assessed difficult cases in educational materials to maximize the effect of training on resident performance and confidence.

Computer-aided detection of breast cancer on mammograms: a swarm intelligence optimized wavelet neural network approach

Breast cancer is the second leading cause of cancer death in women. Accurate early detection can effectively reduce the mortality rate caused by breast cancer. Masses and microcalcification clusters are an important early signs of breast cancer. However, it is often difficult to distinguish abnormalities from normal breast tissues because of their subtle appearance and ambiguous margins. Computer aided diagnosis (CAD) helps the radiologist in detecting the abnormalities in an efficient way. This paper investigates a new classification approach for detection of breast abnormalities in digital mammograms using Particle Swarm Optimized Wavelet Neural Network (PSOWNN). The proposed abnormality detection algorithm is based on extracting Laws Texture Energy Measures from the mammograms and classifying the suspicious regions by applying a pattern classifier. The method is applied to real clinical database of 216 mammograms collected from mammogram screening centers. The detection performance of the CAD system is analyzed using Receiver Operating Characteristic (ROC) curve. This curve indicates the trade-offs between sensitivity and specificity that is available from a diagnostic system, and thus describes the inherent discrimination capacity of the proposed system. The result shows that the area under the ROC curve of the proposed algorithm is 0.96853 with a sensitivity 94.167% of and specificity of 92.105%.

False-positive reduction in mammography using multiscale spatial Weber law descriptor and support vector machines

In a CAD system for the detection of masses, segmentation of mammograms yields regions of interest (ROIs), which are not only true masses but also suspicious normal tissues that result in false positives. We introduce a new method for false-positive reduction in this paper. The key idea of our approach is to exploit the textural properties of mammograms and for texture description, to use Weber law descriptor (WLD), which outperforms state-of-the-art best texture descriptors. The basic WLD is a holistic descriptor by its construction because it integrates the local information content into a single histogram, which does not take into account the spatial locality of micropatterns. We extend it into a multiscale spatial WLD (MSWLD) that better characterizes the texture micro structures of masses by incorporating the spatial locality and scale of microstructures. The dimension of the feature space generated by MSWLD becomes high; it is reduced by selecting features based on their significance. Finally, support vector machines are employed to classify ROIs as true masses or normal parenchyma. The proposed approach is evaluated using 1024 ROIs taken from digital database for screening mammography and an accuracy of Az = 0.99 ± 0.003 (area under receiver operating characteristic curve) is obtained. A comparison reveals that the proposed method has significant improvement over the state-of-the-art best methods for false-positive reduction problem.

Pathways of Breast Cancer Screening Among Chinese American Women

OBJECTIVE

The purpose of this community-based study was to develop a structural equation model for factors contributing to breast cancer screening among Chinese American women.

METHODS

A cross-sectional design included a sample of 440 Chinese American women aged 40 years and older. The initial step involved use of confirmatory factor analysis, which included the following variables: access/satisfaction with health care, enabling, predisposing, and cultural and health belief factors. Structural equation model analyses were conducted to evaluate factors related to breast cancer screening in Chinese American women.

RESULTS

Initial univariate analyses indicated that women without health insurance were significantly more likely to report being never-screened compared to women with health insurance. Structural equation modeling techniques were used to evaluate the utility of the Sociocultural Health Behavior model in understanding breast cancer screening among Chinese American women. Results indicated that enabling and predisposing factors were significantly and positively related to breast cancer screening. Cultural factors were significantly associated with enabling factors and satisfaction with healthcare. Overall, the proposed model explained 34% of the variance in breast cancer screening among Chinese American women.

CONCLUSIONS

The model highlights the significance of enabling and predisposing factors in understanding breast cancer screening behaviors among Chinese American women. In addition, cultural factors were associated with enabling factors, reinforcing the importance of providing translation assistance to Chinese women with poor English fluency and increasing awareness of the critical role of breast cancer screening. Partnering with community organizations may help to facilitate and enhance the screening rates.