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Simple Summary: Breast cancer is a significant cause of female cancer-related deaths in the US. Checking how severe the cancer is helps in planning treatment. Modern AI methods are good at grading cancer, but they are not used much in hospitals yet. We developed and utilized ensemble deep learning algorithms for addressing the tasks of classifying (1) breast cancer subtype and (2) breast cancer invasiveness from whole slide image (WSI) histopathology slides. The ensemble models used were based on convolutional neural networks (CNNs) known for extracting distinctive features crucial for accurate classification. In this paper, we provide a comprehensive analysis of these models and the used methodology for breast cancer diagnosis tasks. Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, a comprehensive approach is presented utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets were based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH dataset, a proposed ensemble strategy was employed, incorporating VGG16 and ResNet50 architectures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching technique to preprocess a high-resolution image facilitated a focused analysis of localized regions of interest. The annotated BACH dataset encompassed 400 WSIs across four distinct classes: Normal, Benign, In Situ Carcinoma, and Invasive Carcinoma. In addition, the proposed ensemble was used on the BreakHis dataset, utilizing VGG16, ResNet34, and ResNet50 models to classify microscopic images into eight distinct categories (four benign and four malignant). For both datasets, a five-fold cross-validation approach was employed for rigorous training and testing. Preliminary experimental results indicated a patch classification accuracy of 95.31% (for the BACH dataset) and WSI image classification accuracy of 98.43% (BreakHis). This research significantly contributes to ongoing endeavors in harnessing artificial intelligence to advance breast cancer diagnosis, potentially fostering improved patient outcomes and alleviating healthcare burdens. [ABSTRACT FROM AUTHOR]

Background: With the development of digital pathology and the renewal of deep learning algorithm, artificial intelligence (AI) is widely applied in tumor pathology. Previous researches have demonstrated that AI-based tumor pathology may help to solve the challenges faced by traditional pathology. This technology has attracted the attention of scholars in many fields and a large amount of articles have been published. This study mainly summarizes the knowledge structure of AI-based tumor pathology through bibliometric analysis, and discusses the potential research trends and foci.Methods: Publications related to AI-based tumor pathology from 1999 to 2021 were selected from Web of Science Core Collection. VOSviewer and Citespace were mainly used to perform and visualize co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references and keywords in this field.Results: A total of 2753 papers were included. The papers on AI-based tumor pathology research had been continuously increased since 1999. The United States made the largest contribution in this field, in terms of publications (1138, 41.34%), H-index (85) and total citations (35,539 times). We identified the most productive institution and author were Harvard Medical School and Madabhushi Anant, while Jemal Ahmedin was the most co-cited author. Scientific Reports was the most prominent journal and after analysis, Lecture Notes in Computer Science was the journal with highest total link strength. According to the result of references and keywords analysis, "breast cancer histopathology" "convolutional neural network" and "histopathological image" were identified as the major future research foci.Conclusions: AI-based tumor pathology is in the stage of vigorous development and has a bright prospect. International transboundary cooperation among countries and institutions should be strengthened in the future. It is foreseeable that more research foci will be lied in the interpretability of deep learning-based model and the development of multi-modal fusion model. [ABSTRACT FROM AUTHOR]

This paper provides a narrative review of the existing literature on differences in demographic and biological features of breast cancer at time of diagnosis between Black and White women in Canada, the United Kingdom and the United States. Electronic database searches for published peer-reviewed articles on this topic were conducted, and 78 articles were included in the final narrative review. Differences between Black and White women were compared for eight categories including age, tumour stage, size, grade, lymph node involvement, and hormone status. Black women were significantly more likely to present with less favourable tumour features at the time of diagnosis than White women. Significant differences were reported in age at diagnosis, tumour stage, size, grade and hormone status, particularly triple negative breast cancer. Limitations on the generalizability of the review findings are discussed, as well as the implications of these findings on future research, especially within the Canadian context. [ABSTRACT FROM AUTHOR]

In this article, we present a variety of measures that quantify equity in cancer mortality outcomes, demonstrate how the measures perform with various cancer types, and identify counties, or "bright spots," that meet the criteria of those measures. Using county-level age-adjusted mortality rates for 2007–2016 from the National Center for Health Statistics, we identified counties that had both equitable and optimal outcomes for Black and White death rates across 5 types of cancer: cancers of the lung/bronchus, prostate, female breast, colorectum, and liver. The number of counties that met the criteria ranged from 0 to 442, depending on cancer type and measure used. Prostate cancer and male liver cancer consistently had the lowest number of "bright spots," with a maximum of 3 counties meeting the most lenient criteria. This paper presents several ways to examine equity, using rate ratios and standard error measures, in cancer mortality outcomes. It highlights areas with positive progress toward equity and areas with a potential need for equity-focused cancer-control planning. Examining local areas of positive deviance can inform cancer-control programming and planning around health equity. [ABSTRACT FROM AUTHOR]

Mediation analysis allows the examination of effects of a third variable in the pathway between an exposure and an outcome. The general multiple mediation analysis method, proposed by Yu et al, improves traditional methods (eg, estimation of natural and controlled direct effects) to enable consideration of multiple mediators/confounders simultaneously and the use of linear and nonlinear predictive models for estimating mediation/confounding effects. In this paper, we extend the method for time-to-event outcomes and apply the method to explore the racial disparity in breast cancer survivals. Breast cancer is the most common cancer and the second leading cause of cancer death among women of all races. Despite improvement of survival rates of breast cancer in the US, a significant difference between white and black women remains. Previous studies have found that more advanced and aggressive tumors and less than optimal treatment may explain the lower survival rates for black women as compared to white women. Due to limitations of current analytic methods and the lack of comprehensive data sets, researchers have not been able to differentiate the relative effect each factor contributes to the overall racial disparity. We use the CDC-funded Patterns of Care study to examine the determinants of racial disparities in breast cancer survival using a novel multiple mediation analysis. Using the proposed method, we applied the Cox hazard model and multiple additive regression trees as predictive models and found that all racial disparity in survival among Louisiana breast cancer patients were explained by factors included in the study. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is the most frequent localization of malignant process in American women and women of European countries. To date it is not possible to control the morbidity growth due to lack of effective ways of primary prevention. Comparing the incidence of breast cancer in developed countries with the countries of Asia and Africa, there is the fact of population predominance lesion in more urbanized countries. This suggests that the environment along with other factors, occupies a significant place in the initiation and progression of breast neoplasia. The impressive rates of industrial development led to the pollution of soil, surface water and, as a consequence, food by heavy metal salts. The purposes of this paper are as follows: the chemical composition determination of neoplastic breast tissue, evaluation of the DNA methylation level, study of prognostic-important receptors expression in the breast cancer cells, establishing linkages between all the derived indicators. Methods: In our study we used the following methods: studying of the chemical composition of breast cancer tissue by atomic absorption spectrophotometry and energy-dispersion spectrometer; immunohistochemical study of ER, PR, HER2/neu, p53, Ki-67, E-cadherin and MGMT receptors; DNA extraction and investigation by oscillating infrared spectroscopy method. Results: The total amount of heavy metals in breast cancer tissue ranged from 51.21 x 10-3 to 84.86 x 10-3 µg/kg. We have got the following results: the growth of heavy metals in neoplastic tissue is accompanied with the increase of HER2/neu, p53, Ki-67, MGMT expression and decrease of ER and PR expression. The increment of pathological DNA methylation is accompanied with the increasing amount of heavy metals in tumor tissue. Conclusions: Heavy metals through different pathogenetic links stimulate the progression of breast cancer and reduce its sensitivity to treatment. DNA of tumor tissue has a different level of methylation which changes with the amount of heavy metals in cancer cells. This is displayed on the synthesis of prognostically important receptors in neoplastic tissue. [ABSTRACT FROM AUTHOR]

Introduction: This paper presents race-specific breast cancer mortality rates and the corresponding rate ratios for the 50 largest U.S. cities for each of the 5-year intervals between 2005 and 2014.Methods: The 50 largest cities in the U.S. were the units of analysis. Numerator data were abstracted from national death files where the cause was malignant neoplasm of the breast (ICD-10=C50) for women. Population-based denominators were obtained from the U.S. Census Bureau for 2010-2014. To measure the racial disparity, we calculated Black:White rate ratios (RRs) and confidence intervals for each 5-year period. To determine whether changes over time in the disparity were statistically significant, we calculated a 2-sided z score for the change in the relative percent difference between the Black and White rates for 2005-2009 and 2010-2014.Results: At the most recent time point (2010-2014), the RR was significantly greater than 1.00 in the US and 24 cities. The change in the Black:White disparity was statistically significant in five cities and the US. The percent difference increased significantly in Atlanta, GA (from 4.1 to 117.4, p<0.001); San Antonio, TX (from 24.4 to 79.3, p=0.034); and the US (from 39.7 to 43.1, p=0.007). The percent difference decreased significantly in Memphis, TN (from 111.0 to 68.9, p=0.043); Philadelphia, PA (from 43.1 to 23.5, p=0.049); and Boston, MA (from 48.9 to 0.7, p=0.022).Conclusion: This analysis provides updated city-level breast cancer mortality data for Black and White women through 2014, and reveals that in the US and 24 of the 43 largest US cities, Black women continue to die from breast cancer at a higher rate than their White counterparts. Importantly, however, a few cities, Memphis, Boston and Philadelphia, showed a decrease in the Black:White breast cancer mortality disparity between 2005-2009 and 2010-2014. [ABSTRACT FROM AUTHOR]

There are currently over 2.5 million breast cancer survivors in the United States and, according to the American Cancer Society, 10 to 20 percent of these women will develop recurrent breast cancer. Early detection of recurrence can avoid unnecessary radical treatment. However, self-examination or mammography screening may not discover a recurring cancer if the number of surviving cancer cells is small, while biopsy is too invasive and cannot be frequently repeated. It is therefore important to identify non-invasive biomarkers that can detect early recurrence. The present paper develops a mathematical model of cancer recurrence. The model, based on a system of partial differential equations, focuses on tissue biomarkers that include the plasminogen system. Among them, only uPAR is known to have significant correlation to its concentration in serum and could therefore be a good candidate for serum biomarker. The model includes uPAR and other associated cytokines and cells. It is assumed that the residual cancer cells that survived primary cancer therapy are concentrated in the same location within a region with a very small diameter. Model simulations establish a quantitative relation between the diameter of the growing cancer and the total uPAR mass in the cancer. This relation is used to identify uPAR as a potential serum biomarker for breast cancer recurrence. [ABSTRACT FROM AUTHOR]

There is considerable evidence that a healthy lifestyle consisting of physical activity, healthy diet, and weight control is associated with reduced risk of morbidity and mortality after cancer. However, these behavioral interventions are not widely adopted in practice or community settings. Integrating heath behavior change interventions into standard survivorship care for the growing number of cancer survivors requires an understanding of the current state of the science and a coordinated scientific agenda for the future with focused attention in several priority areas. To facilitate this goal, this paper presents trends over the past decade of the National Cancer Institute (NCI) research portfolio, fiscal year 2004 to 2014, by funding mechanism, research focus, research design and methodology, primary study exposures and outcomes, and study team expertise and composition. These data inform a prioritized research agenda for the next decade focused on demonstrating value and feasibility and creating desire for health behavior change interventions at multiple levels including the survivor, clinician, and healthcare payer to facilitate the development and implementation of appropriately targeted, adaptive, effective, and sustainable programs for all survivors. [ABSTRACT FROM AUTHOR]

The delivery of comprehensive breast care seems to be undergoing a paradigm shift driven by advances in technology, interprofessional collaboration and patient dissatisfaction with interruptions in care. This paradigm shift includes the emergence of new models of care that optimize the use of embedded radiology services and encourage greater interprofessional collaboration. This paper briefly reviews the three drivers (advances in technology, expectations regarding interprofessional collaboration and patient dissatisfaction with interrupted care) underlying the paradigm shift in comprehensive breast care as well as introduces the environment of care model, which describes the proximity of radiology services and interprofessional collaboration between the Women's Wellness Clinic (WWC) and Radiology at the Karmanos Cancer Institute (KCI) - both based in the United States. In addition, this model is proposed as a way to facilitate improved patient satisfaction and early appropriate referral. Finally, plans for evaluating the effectiveness of this model are presented. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer outcomes are variable due to differences in tumor biology, patient biology, and treatment. The likelihood of developing cancer and other diseases increases with age. Thus, many patients with breast cancer have multiple co-morbidities requiring medical management, which increases the probability of polypharmacy and the risk of adverse drug events. Pharmacogenetics is the study of how inherited genetic variants influence drug response. Depending on the genes that a patient inherits, some respond to drugs as expected, some experience debilitating side effects, and others have minimal to no response. In this paper, we discuss the theoretical clinical utility of pharmacogenetics for 225 patients with breast cancer relative to anti-cancer drugs and non-cancer drugs. For this population, 38 drug–gene associations with high levels of evidence for clinical actionability were identified, supporting the concept of pharmacogenetics integration into the routine care of future patients with breast cancer. Patients with breast cancer often receive many drugs to manage the cancer, side effects associated with cancer treatment, and co-morbidities (i.e., polypharmacy). Drug–drug and drug–gene interactions contribute to the risk of adverse events (AEs), which could lead to non-adherence and reduced efficacy. Here we investigated several well-characterized inherited (germline) pharmacogenetic (PGx) targets in 225 patients with breast cancer. All relevant clinical, pharmaceutical, and PGx diplotype data were aggregated into a single unifying informatics platform to enable an exploratory analysis of the cohort and to evaluate pharmacy ordering patterns. Of the drugs recorded, there were 38 for which high levels of evidence for clinical actionability with PGx was available from the US FDA and/or the Clinical Pharmacogenetics Implementation Consortium (CPIC). These data were associated with 10 pharmacogenes: DPYD, CYP2C9, CYP2C19, CYP2D6, CYP3A5, CYP4F2, G6PD, MT-RNR1, SLCO1B1, and VKORC1. All patients were taking at least one of the 38 drugs and had inherited at least one actionable PGx variant that would have informed prescribing decisions if this information had been available pre-emptively. The non-cancer drugs with PGx implications that were common (prescribed to at least one-third of patients) included anti-depressants, anti-infectives, non-steroidal anti-inflammatory drugs, opioids, and proton pump inhibitors. Based on these results, we conclude that pre-emptive PGx testing may benefit patients with breast cancer by informing drug and dose selection to maximize efficacy and minimize AEs. [ABSTRACT FROM AUTHOR]

The article presents abstracts of various research papers on medical sciences including patients with early-stage breast cancer, use of antidepressants during pregnancy and similar reoperation rates of the pressure used to irrigate open wound fractures.