Screening Mammography

Patient Navigation Can Improve Breast Cancer Outcomes among African American Women in Chicago: Insights from a Modeling Study

African American (AA) women experience much greater mortality due to breast cancer (BC) than non-Latino Whites (NLW). Clinical patient navigation is an evidence-based strategy used by healthcare institutions to improve AA women's breast cancer outcomes. While empirical research has demonstrated the potential effect of navigation interventions for individuals, the population-level impact of navigation on screening, diagnostic completion, and stage at diagnosis has not been assessed. An agent-based model (ABM), representing 50-74-year-old AA women and parameterized with locally sourced data from Chicago, is developed to simulate screening mammography, diagnostic resolution, and stage at diagnosis of cancer. The ABM simulated three counterfactual scenarios: (1) a control setting without any navigation that represents the "standard of care"; (2) a clinical navigation scenario, where agents receive navigation from hospital-affiliated staff; and (3) a setting with network navigation, where agents receive clinical navigation and/or social network navigation (i.e., receiving support from clinically navigated agents for breast cancer care). In the control setting, the mean population-level screening mammography rate was 46.3% (95% CI: 46.2%, 46.4%), the diagnostic completion rate was 80.2% (95% CI: 79.9%, 80.5%), and the mean early cancer diagnosis rate was 65.9% (95% CI: 65.1%, 66.7%). Simulation results suggest that network navigation may lead up to a 13% increase in screening completion rate, 7.8% increase in diagnostic resolution rate, and a 4.9% increase in early-stage diagnoses at the population-level. Results suggest that systems science methods can be useful in the adoption of clinical and network navigation policies to reduce breast cancer disparities.

Deep Vision for Breast Cancer Classification and Segmentation

Simple Summary

Breast cancer misdiagnoses increase individual and system stressors as well as costs and result in increased morbidity and mortality. Digital mammography studies are typically about 80% sensitive and 90% specific. Improvement in classification of breast cancer imagery is possible using deep vision methods, and these methods may be further used to identify autonomously regions of interest most closely associated with anomalies to support clinician analysis. This research explores deep vision techniques for improving mammography classification and for identifying associated regions of interest. The findings from this research contribute to the future of automated assistive diagnoses of breast cancer and the isolation of regions of interest.

Abstract

(1) Background: Female breast cancer diagnoses odds have increased from 11:1 in 1975 to 8:1 today. Mammography false positive rates (FPR) are associated with overdiagnoses and overtreatment, while false negative rates (FNR) increase morbidity and mortality. (2) Methods: Deep vision supervised learning classifies 299 × 299 pixel de-noised mammography images as negative or non-negative using models built on 55,890 pre-processed training images and applied to 15,364 unseen test images. A small image representation from the fitted training model is returned to evaluate the portion of the loss function gradient with respect to the image that maximizes the classification probability. This gradient is then re-mapped back to the original images, highlighting the areas of the original image that are most influential for classification (perhaps masses or boundary areas). (3) Results: initial classification results were 97% accurate, 99% specific, and 83% sensitive. Gradient techniques for unsupervised region of interest mapping identified areas most associated with the classification results clearly on positive mammograms and might be used to support clinician analysis. (4) Conclusions: deep vision techniques hold promise for addressing the overdiagnoses and treatment, underdiagnoses, and automated region of interest identification on mammography.

Is It the Era for Personalized Screening?

Breast cancer screening is a recognized tool for early detection of the disease in asymptomatic women, improving treatment efficacy and reducing the mortality rate. There is raised awareness that a "one-size-fits-all" approach cannot be applied for breast cancer screening. Currently, despite specific guidelines for a minority of women who are at very high risk of breast cancer, all other women are still treated alike. This article reviews the current recommendations for breast cancer risk assessment and breast cancer screening in average-risk and higher-than-average-risk women. Also discussed are new developments and future perspectives for personalized breast cancer screening.

Factors Impacting False Positive Recall in Screening Mammography

RATIONALE AND OBJECTIVES

Our objective was to identify factors impacting false positive recalls in screening mammography.

MATERIALS AND METHODS

We retrospectively reviewed our screening mammography database from August 31, 2015 to September 30, 2016, including full field digital mammograms (FFDM) and digital breast tomosynthesis (DBT) mammograms. False positive (FP) exams were defined as Breast Imaging-Reporting and Data System (BI-RADS) 1 or 2 assessments at diagnostic imaging with 1 year cancer-free follow-up, Breast Imaging-Reporting and Data System 3 assessment at diagnostic imaging with 2 years cancer free follow-up, or biopsy with benign pathology. True positives were defined as malignant pathology on biopsy or surgical excision. We evaluated the association of FP recalls with multiple patient-level factors and imaging features.

RESULTS

A total of 22,055 screening mammograms were performed, and 1887 patients were recalled (recall rate 8.6%). Recall rate was lower for DBT than full field digital mammograms (8.0% vs 10.6%, p < 0.001). FP results were lower if prior mammograms were available (90.8% vs 95.8%, p = 0.02), and if there was a previous benign breast biopsy (87.6% vs 92.9%, p = 0.01). Mean age for the FP group was lower than the true positive group (56.1 vs 62.9 years, p < 0.001). There were no significant differences in FP recalls based on history of high-risk lesions, family history of breast or ovarian cancer, hormone use, breast density, race, or body mass index.

CONCLUSION

FP recalls were significantly less likely with DBT, in older women, in patients with prior mammograms available for comparison, and in patients with histories of benign breast biopsy. This study supports the importance of using DBT in the screening setting and obtaining prior mammograms for comparison.

Protease Cargo in Circulating Exosomes of Breast Cancer and Ovarian Cancer Patients

Background: As is known, exosomes play an important role in promoting progression of cancers by increasing its invasive potential. The aim of this study was to evaluate the levels of tetraspanine-associated (ADAM-10) and tetraspanine-nonassociated proteases (20S proteasomes) in exosomes from culture medium, plasma exosomes of patients with breast tumors and plasma and ascites of ovarian tumor patients. Methods: MCF-7 and SVO-3 culture mediums and blood samples from healthy females (n = 30, HFs), patients with diffuse dyshormonal dysplasia of the breast (n=28, BBTPs), breast cancer patients (n=32, BCPs), borderline ovarian tumor patients (n=20, BOTPs) and blood and ascites samples ovarian cancer patients (n=35, OCPs) were included in the study. Exosomes from plasma, ascites and culture mediums were isolated and characterized in according to Extracellular Vesicles Society. The expression levels of 20S proteasome and ADAM-10 in exosomes were determined using flow cytometry and western blot analysis, correspondingly. Results: The subpopulation composition of the exosomes from MCF-7 culture medium and from blood plasma of HFs and breast diseases patients is similar, however CD9/CD24 subpopulation significantly increased at cell supernatant. The similar results was obtained for exosomes from SVO-3 medium and blood plasma and ascites of ovary tumor patients, but CD9/CD24 subpopulation significantly decreased at cells and illness samples, however CD63/CD24 exosomes increased significantly from cell supernatant. 20S proteasome level is significantly increased in exosomes from MCF-7 and SVO-3 culture medium, breast tumor patients and OCPs plasma in comparison to HUVEC culture medium and HFs plasma samples. At CD9-positive exosomes from BCPs plasma and MCF-7 was reveal a high expression of ADAM-10 and low expression is from BBDPs plasma and ovarian tumor patients plasma/ ascites samples. Exosomes from ascites OCP had high expression of ADAM-10 in the CD24-positive subpopulation. Conclusion: Breast and ovarian cancer development is connected with functioning of immune proteasome forms in plasma and ascites exosomes, while increased ADAM10 expression at CD9-positive exosome was associated with breast cancer and at CD24-positive subpopulation – with ovarian cancer. Obtained data confirm role of exosomal proteases in tumor progression.