Breast Cancer Rates Research Articles

Association of Socio-Environmental Burden and Inequality With Cancer Screening and Mortality.

Social and environmental injustice may influence accessibility and utilization of health resources, affecting outcomes of patients with cancer. We sought to assess the impact of socio-environmental inequalities on cancer screening and mortality rates for breast, colon, and cervical cancer. Data on cancer screening and environmental justice index social and environmental ranking (SER) was extracted from the CDC PLACES and ATSDR, respectively. Mortality rates were extracted using CDC WONDER. Screening targets were defined by Healthy People 2030. Among census tracts, 14 659 were classified as "low," 29 534 as "moderate," and 15 474 as "high" SER (high SER denoting greater socioenvironmental injustice). Screening targets were achieved by 31.1%, 16.2%, and 88.6% of tracts for colon, breast, and cervical cancers, respectively. High SER tracts were much less likely to reach screening targets compared with low SER tracts for colon (OR: 0.06), breast (OR: 0.24), and cervical cancer (OR: 0.05) (all p < 0.001). Median mortality rates for low and high SER were 16.7, and 21.0, respectively, for colon, 13.4, 14.75, respectively, for breast, and 1.0, 1.6, respectively, for cervical cancer (all p < 0.05). Socioenvironmental disparities negatively influence cancer screening and mortality, underscoring the need to reduce environmental injustices through measures like equitable cancer screening services.

Interprofessional team-based primary care practice and preventive cancer screening: evidence from Family Health Teams in Ontario, Canada.

Health care reforms introduced interprofessional team-based primary care to optimize access to health care and preventive services. In this context, preventive cancer screening represents an important measure as it is essential for the early detection of cancer and treatment. We investigated the effects of Family Health Teams (FHTs), an interprofessional team-based primary care practice setting, on cancer screening rates in Ontario, Canada. By utilizing comprehensive health administrative data from April 1st 2011 to March 31st 2023, we determined the effect of FHT on screening rates for breast, cervical, and colorectal cancer while controlling for relevant physician and patient characteristics. Our analytical framework employs fractional probit models, including the Mundlak procedure, and generalized estimating equations to assess the impact of practicing in FHTs on cancer screening rates, while accounting for unobserved physician heterogeneity. Our results indicate that compared to non-FHTs, physicians practicing in FHTs have higher breast (2.4%), cervical (2%), and colon (0.8%) cancer screening rates per physician per year. The effectiveness of FHTs in promoting cancer screenings is particularly pronounced in smaller practices and among populations in rural and economically deprived areas. Our findings highlight the role of teams in enhancing preventive health care services potentially through task shifting mechanisms and suggest that such models may offer a pathway to improving access to preventive health care, especially in marginalized populations. Our research contributes to the literature by providing empirical evidence on the benefits of interprofessional team-based primary care in improving cancer screening.

Co-Designing the MOSAIC mHealth App With Breast Cancer Survivors: User-Centered Design Approach.

Breast cancer is the world's most prevalent cancer. Although the 5-year survival rate for breast cancer in the United States is 91%, the stress and uncertainty of survivorship can often lead to symptoms of depression and anxiety. With nearly half of breast cancer survivors living with stress and symptoms of depression and anxiety, there are a significant number of unmet supportive care needs. New and potentially scalable approaches to meeting these supportive care needs are warranted. This study aimed to engage breast cancer survivors and acceptance and commitment therapy (ACT) content experts in user-centered design (UCD) to develop a mobile health app (MOSAIC [Mobile Acceptance and Commitment Therapy Stress Intervention]) using stress intervention strategies. We held 5 UCD sessions with 5 breast cancer survivors, 3 ACT content experts, 2 user experience design experts, and 1 stress expert facilitator over the course of 10 weeks. The sessions were developed to lead the 10 co-designers through the 5-step UCD process (eg, problem identification, solution generation, convergence, prototyping, and debriefing and evaluation). Following the fifth session, a prototype was generated and evaluated by the 5 breast cancer survivors and 3 ACT experts using the System Usability Scale, Acceptability E-scale, and a brief set of semistructured interview questions. The 10 co-designers were present for each of the 5 co-design sessions. Co-designers identified 5 design characteristics: simple entry with use reminders (behavioral nudges), a manageable number of intervention choices, highly visual content, skill-building exercises, and social support. A total of 4 features were also identified as critical to the use of the tool: an ACT and breast cancer-specific onboarding process, clean navigation tools, clear organization of the interventions, and once-per-week behavioral nudges. These requirements created the foundation for the app prototype. The 5 breast cancer survivors and 3 ACT co-designers evaluated the app prototype for 1 week, using an Android smartphone. They rated the app as usable (mean 79.29, SD 19.83) on the System Usability Scale (a priori mean cutoff score=68) and acceptable (mean 24.28, SD 2.77) on the Acceptability E-scale (a priori mean cutoff score=24). Through the UCD process, we created an ACT app prototype with 5 breast cancer survivors, 3 ACT experts, and 2 UCD designers. The next step in our research is to continue the assessment and refining of the prototype with additional breast cancer survivors. Future work will pilot-test the app to examine the feasibility of a large-scale, randomized control trial. Studies will enroll increasingly diverse breast cancer survivors to broaden the generalizability of findings.

Where should the cancer control interventions target: A geospatial hotspot analysis for major cancer mortality 2018-2022 in the U.S.

Identifying changes in geographical disparities of cancer mortality reveals locations where cancer prevention and control efforts should be focused/targeted. We use recent cancer surveillance data to demonstrate the geographical disparity of major cancer mortality rates in the US and its shift compared to previous data. This cross-sectional study used the 2018-2022 county-level mortality rates of colorectal, lung, breast and prostate cancer from the CDC mortality data. Counties with suppressed death counts were imputed by spatial regression models. Getis-Ord Gi* statistics were used to evaluate spatial clustering of county mortality. Identified hotspot counties were visualized and compared with literature for hotspot pattern change. A total of 3108 US mainland counties were included. Cancer mortality rates were significantly higher in 244 counties for colorectal, 456 for lung, 147 for breast and 180 for prostate. Hotspot areas were central Appalachia (for colorectal and lung), Lower Mississippi Delta (colorectal, breast and prostate), Midwest (colorectal and lung), north Michigan/Wisconsin (lung and prostate), north Florida (lung), and the West (prostate). West central Appalachia and Lower Mississippi Delta continue to be hotspots for major cancer types, while previously identified eastern North Carolina/Virginia hotspots shrunk, east Oklahoma and north Florida emerged as the new hotspot for lung cancer, and several hotspots emerged in the West for prostate cancer. This study updated the analyses for geospatial disparity in major cancers' mortality since 2018-illustrating recent changes in the disparity pattern and pinpointing areas that cancer prevention and control efforts should target.

Role of inducible nitric oxide (iNOS) and nitrosative stress in regulating sex differences in secondary lymphedema.

Secondary lymphedema is a common complication following surgical treatment of solid tumors. Although more prevalent in women due to higher breast cancer rates, men also develop lymphedema, often with more severe manifestations. Despite these differences in clinical presentation, the cellular mechanisms underlying sex differences are poorly understood. Previous studies have shown that inducible nitric oxide synthase (iNOS) expression by inflammatory cells is an important regulator of lymphatic pumping and leakiness in lymphedema and that lymphatic endothelial cells are highly sensitive to nitrosative stress. Based on this rationale, we used a mouse tail model of lymphedema to study the role of nitric oxide in sex-related differences in disease severity. Consistent with clinical findings, we found that male mice have significantly worse tail edema and higher rates of tail necrosis compared with female mice following tail skin/lymphatic excision (p = 0.001). Our findings correlated with increased tissue infiltration of iNOS + inflammatory cells, increased iNOS protein expression, and increased nitrosative stress in male mouse lymphedematous skin tissues (p < 0.05). Importantly, transgenic male mice lacking the iNOS gene (iNOS-KO) displayed markedly reduced swelling, inflammation, and tissue necrosis rates, whereas no differences were observed between wild-type and iNOS-KO female mice. Overall, our results indicate that iNOS-mediated nitric oxide production contributes to sex-based differences in secondary lymphedema severity, emphasizing the need to consider sex as a biological variable in lymphedema research.

Surveillance, Epidemiology, and End Results Data Show Increasing Rates of Distant-Stage Breast Cancer at Presentation in U.S. Women.

Background The incidence of distant-stage (metastatic) breast cancer at initial presentation has increased significantly in U.S. women under 40 years of age, but no clear trend in older women has been reported. Purpose To investigate trends in distant-stage breast cancer incidence using the latest available Surveillance, Epidemiology, and End Results (SEER) data, including the COVID-19 pandemic years 2020 and 2021. Materials and Methods In this retrospective study of 71 million to 80 million women annually, SEER 22 stage-specific breast cancer incidence rates from January 2004 to December 2021 were analyzed using Joinpoint software to evaluate trends in distant-stage disease in U.S. women overall, by age group, and by race and ethnicity. Results Distant-stage breast cancer incidence rates at presentation increased significantly for U.S. women overall, with an annual percentage change (APC) of 1.16% (95% CI: 0.92, 1.42; P < .001) over 2004-2021. Distant-stage disease increased consistently for women aged 20-39 years (APC, 2.91%; P < .001) and for women aged 75 years or older (APC, 1.44%; P < .001). For women aged 40-74 years, the distant-stage breast cancer incidence rate increased with an APC of 2.10% over 2004-2010 (P = .002) and an APC of 2.73% over 2018-2021 (P = 0.01). The interruption of care in 2020 due to COVID-19 affected distant-stage breast cancer detection, especially for older women from racial and ethnic minority groups. The incidence of distant-stage breast cancer increased over 2004-2021 for Asian women (APC, 2.90%; P < .001), Black women (0.86%; P = .008), and Hispanic women (1.56%; P < .001) and over 2004-2019 for Native American women (3.86%; P = .04). White women showed an increase over 2004-2012 (1.68%; P = .01) but no evidence of a trend over 2012-2021. Distant-stage cancer incidence rates were 55% higher in Black women than White women. Conclusion Distant-stage breast cancer incidence rates at presentation have increased significantly in U.S. women overall; in age groups 20-39 years, 40-74 years, and 75 years or older; and in Asian, Black, Hispanic, and Native American women. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Kim and Moy in this issue.

Breast Cancer Detection Using Image Processing and Machine Learning

As the outlines picturize, one driving reason for death in women across the entire world is breast cancer. It is an often-occurring disease in women, affecting approximately 2.1 million women annually. Studies indicate it generally affects women more in developed regions, although rates are increasing globally. While prevention may not be a feasible option, improving the outcomes and survival rates of breast cancer is a viable goal. Breast cancer mortality can be considerably decreased by more efficient treatments, which are made possible by early discovery of the disease. Many researchers and scientists are working on methods to facilitate early detection of breast cancer. Using the K-Nearest Neighbors (KNN) algorithm is one such technique. KNN is a straightforward machine learning technique that works well for regression and classification. In order to categorize an input according to the majority class of its neighbors, it first finds the k-nearest data points to the input. Using features taken from medical imaging, KNN can be utilized to determine a tumor's malignancy or benignity in the context of breast cancer detection. This algorithm is a useful tool for creating precise and dependable diagnostic systems since it can adjust and get better with additional data.

Beyond Tuskegee: A contemporary qualitative assessment of barriers to research participation among Black women.

Health care inequities have partially contributed to the existing racial gaps in health. Despite having lower incidence rates of breast cancer, Black women have a 41% higher mortality rate than White women. Black individuals remain underrepresented in research. Diversity in research is paramount to the improvement of clinical care practices and subgroup-specific guidelines. Black women from various community venues across geographic regions of the United States were invited via email, online fliers, social media platforms, and word of mouth to participate in focus groups. Six online focus groups of six to 10 Black women aged 25-65 years (N=38) with and without a history of cancer were conducted with an in-depth semistructured discussion guide. Most participants were college educated (32 of 38; 84.2%), aged 50 years or older (31 of 38; 81.6%), and had an annual income of $50,000 or more (26 of 38; 68.4%). Several barriers to research participation were identified. They included a lack of empathy and respect in health care settings, apprehension regarding the sharing of personal information, mistrust of medical research, and logistical/technical barriers. Alternatively, building individual and community trust and communicating the value of conducting research beneficial to the Black community were viewed as facilitators to research participation. Successful engagement of Black women in research requires the acknowledgment and consideration of the numerous barriers that affect their ability to participate. Black women are more inclined to participate in research when the research team is knowledgeable, has experience within their communities, and engages trusted community partners. Additionally, the research must be meaningful and impactful to future generations of Black women.

Time trends of cancer incidence in young adults (20-49 years) in Italy. A population - based study, 2008-2017.

To evaluate short-term (2008-2017) cancer incidence trends in Italy for individuals aged 20-49 years by sex and cancer type. Observational study from population-based data collected by 20 Italian Cancer Registries, covering 33% of the Italian population. The age-standardized incidence rates (ASRs), overall and stratified by area, sex, cancer site or type, and major age groups (i.e., 20-39, 40-49), were computed. In 2008-2017, cancer incidence rates were almost two times higher in Italian women aged 20-49 than in age-corresponding men (202.2 vs 112.4 per 100,000) on account of elevated rates of breast and thyroid cancers. Contrasting trends emerged according to cancer sites/types. ASRs for female breast cancer increased steadily from 2008 (82.4) to 2014 (86.2) and remained unchanged thereafter (i.e., 86.5 in 2017). During the study period, there was an increase for testicular cancer, skin melanoma in both sexes, and thyroid cancer until 2013 (followed by a slight decrease from 2014 to 2017). Conversely, ASRs consistently declined for colorectal cancer and were substantially stable or slightly decreasing for cervix uteri (from 8.1 to 7.7), ovary (from 7.5 to 6.9) and non-Hodgkin lymphoma (from 8.3 to 7.6 in men and from 5.9 to 5.5 in women). Study findings do not support a unique temporal pattern for the incidence of early-onset cancer in Italy until 2017, as reported in other countries. Increases in incidence documented in both sexes for some tumor sites was counterbalanced by a decrease in other sites. The importance of supporting prevention strategies from the youngest of ages must be emphasized, and the role of anticipated screening should be carefully addressed.

Cell type specific drug repurposing for breast cancer by integrating single-cell RNA sequencing and molecular docking simulation

Abstract. Breast cancer is the most frequently diagnosed cancer and the second leading cause of cancer-related deaths among women. Despite the widespread use of chemotherapy and radiotherapy, there is an urgent need for novel treatments. This study focuses on drug repurposing for breast cancer using single-cell RNA sequencing (scRNA-seq) data. By analyzing differentially expressed genes across various breast cancer cell types, we identified potential protein targets. We then used Drug2cell to generate top cancer-related drug candidates, followed by molecular featurization of proteins and drugs. Drug-target interactions were analyzed through molecular docking, and the top drug candidates for each cell type were ranked. Our data analysis annotated ten cell types from breast cancer scRNA-seq data. Using the top ten differentially expressed genes, we identified over a hundred protein targets and conducted molecular docking analysis. Fourteen drugs, including doxorubicin and everolimus, were repurposed with supporting clinical evidence for their effectiveness in breast cancer treatment. Overall, this drug-repurposing strategy, combined with scRNA-seq data, identified several cell-type-specific drugs, enhancing therapeutic target discovery and improving treatment success rates for breast cancer.

Assessing the expression of Nectin-4 in solid tumors by immunohistochemistry - what do we know?

Antibody-Drug Conjugates (ADCs) represent a promising class of anti-cancer substances that combine the specificity of monoclonal antibodies with the potency of cytotoxic drugs, hence they enable a new approach of targeted therapy. The use of the ADC Entfortumab Vedotin (EV), which targets the viral receptor Nectin-4, showed an impressive clinical response in metastatic urothelial carcinoma. In this review, we present what is known about the expression of Nectin-4 in various tumor entities, focusing on immunohistochemistry as a diagnostic venue to detect positive expression, as this inexpensive technique is readily available in pathology laboratories. Various studies demonstrated expression of Nectin-4 in many solid tumor entities with the highest expression rates in urothelial carcinomas and breast cancer. To date, the relevance of the subcellular compartment of immunoreactivity (membranous vs. cytoplasmic) is still unclear in respect of its predictive value for EV therapy, which ought to be clarified in further studies.

"If diagnosed early, you will be stressed and die…" drivers for breast cancer screening services uptake among women in Dar es Salaam.

Worldwide, there has been an increase in the breast cancer mortality rate, with disproportionately high rates in low and middle-income countries. Addressing breast cancer starts with early detection through screening services. In Tanzania, despite being among countries with high rates of breast cancer, screening services uptake has remained low. This study aimed to explore the drivers for breast cancer screening among women accessing health care services at a specialized cancer treatment hospital in Dar es Salaam, Tanzania. We adopted an exploratory case study employing qualitative techniques to analyze the drivers for breast cancer screening among women in Dar es Salaam. We interviewed four groups of respondents: women with breast cancer attending cancer treatment clinics, young women and old women without breast cancer attending cancer screening units, and older women who do not show up for breast cancer screening. From analysis of the in-depth interviews and focus group discussions we found that the drivers for breast cancer screening operate at different levels; individual as a centre of making the decision to be screened, family as an attribute to decide uptake of screening, the society drivers, the healthcare providers related drivers and health facility related drivers. These attributes were found to influence women's decisions to screen, and the possibility of uptake of breast cancer screening was dependent on family and social motivation. In most instances, women were driven to utilize breast cancer screening when the services were readily available at their neighbouring health facilities. The findings from this study have enlightened that people's decision about utilizing breast cancer screening services is based not only on perceptions of their risk but also on fellow community members who have survived the disease, the experiences of breast cancer screening services from their networks and the performance of healthcare institutions in delivery of such services. The use of breast cancer survivors' support groups to promote breast cancer screening services is advocated from the findings of our study.

Taxane/anthracycline combinations reduced incidence of breast cancer recurrence in young women across molecular subtypes: a real-world evidence of Taiwan from 2011 to 2019.

Adolescent and young adult (AYA) patients with breast cancer generally have poor prognoses and a higher risk of secondary cancers compared to those at the same cancer stage. Notably, AYA patients in Asia exhibit a higher incidence rate of breast cancer, with Luminal A as the predominant molecular subtype, which contrasts with the trends observed in Western countries. This study aims to compare the efficacy of Taxane/Anthracycline combination-based regimens (TACB) versus Anthracycline-based regimens (AB) in AYA patients with stage I-II breast cancer, focusing on different molecular subtypes. This study utilized data from the Taiwan National Health Insurance Research Database (NHIRD) and the Taiwan Cancer Registry (TCR) from 2011 to 2019. The study cohort included patients aged 15 to 39years who were diagnosed with stage I-II breast cancer and received either TACB or AB regimens. Propensity score matching and Cox proportional hazards regression models were used to calculate the hazard ratios (HR) for recurrence. The results showed that TACB regimens significantly reduced the risk of recurrence compared to AB regimens across all patients (aHR 0.73, 95% CI 0.55-0.97). Specifically, for low/middle-recurrence risk groups, the aHR was 0.68 (95% CI 0.49-0.96), and for high-recurrence risk groups, it was 0.43 (95% CI 0.21-0.87). The analysis further indicated no significant differences in recurrence risk between AYA and non-AYA patients using TACB regimens. The TACB regimens showed a more favorable prognosis than AB regimens across all molecular subtypes. Furthermore, TACB regimens not only outperformed AB treatments but also closed the gap in prognostic outcomes between AYA and non-AYA patients. We believe the findings of this study are highly reliable and can provide valuable guidance for physicians in choosing the most appropriate treatment strategies for AYA patients with stage I-II breast cancer.

Systematic review and meta-analysis: The incidence rate of breast cancer among ovarian cancer patients with BRCA mutation

Systematic review and meta-analysis: The incidence rate of breast cancer among ovarian cancer patients with BRCA mutation

Zn2+ enhanced the anti-breast cancer activity of fucoidan based nanoparticles by activating the cGAS-STING pathway

The incidence rate of breast cancer remains high, and lung metastasis often leads to treatment failure. Immunotherapy based on the cyclic guanosine monophosphate synthesis (cGAS) − interface gene stimulatory factor (STING) pathway is expected to bring benefits to the treatment of breast cancer. This study provided a delivery system based on fucoidan (Fu) and polyvinylpyrrolidone (PVP) for the potential molecule resveratrol (Res). The study confirmed that Fu and PVP were assembled into nanoparticles through hydrogen bonding and effectively loaded Res (Fu/PVP/Res). Interestingly, the adsorption of Zn2+ (Fu/PVP/Res Zn2+) was expected to provide impetus for the treatment of breast cancer. The results in vitro confirmed that Fu/PVP/Res Zn2+ significantly inhibited the proliferation of breast cancer 4T1 cells, induced the accumulation of mitochondrial reactive oxygen species (ROS) and the damage of mitochondrial membrane potential (MMP), and further induced the cytoplasmic release of mtDNA, which was the direct cause of the activation of cGAS-STING pathway. In vivo studies confirmed that Fu/PVP/Res Zn2+ treatment inhibited the growth of 4T1 transplanted tumors. Excitingly, Fu/PVP/Res Zn2+ treatment also prevented lung metastasis of in situ tumors. The in vivo mechanism confirmed that Fu/PVP/Res Zn2+ treatment activated the cGAS-STING pathway and induced abnormal expression of tumor apoptosis factors such as Bax/Bcl-2. In conclusion, our research provided potential for the treatment of breast cancer.

Breast cancer risk among women with schizophrenia and association with duration of antipsychotic use: population-based cohort study in South Korea.

Breast cancer is a major global health issue, especially among women. Previous research has indicated a possible association between psychiatric conditions, particularly schizophrenia, and an increased risk of breast cancer. However, the specific risk of breast cancer in women with schizophrenia, compared with those with other psychiatric disorders and the general population, remains controversial and needs further clarification. To estimate the risk of breast cancer among people with schizophrenia compared with people with other psychiatric disorders and people in the general population. We utilised medical claims data of women aged 18 to 80 years in the Korean National Health Information Database from 2007 to 2018. Individuals with schizophrenia were defined as women with ICD-10 codes F20 or F25 (n = 224 612). The control groups were defined as women with other psychiatric disorders (n = 224 612) and women in the general Korean population (n = 449 224). Cases and controls were matched by index date and age, in a 1:1:2 ratio. We estimated the hazard of breast cancer using the Cox proportional hazards model, adjusting for insurance premiums and medical comorbidities. Among the people with schizophrenia, we used the landmark method to estimate the association between duration of antipsychotic medication use and the incidence of breast cancer. In multivariable Cox regression models, the hazard rate of breast cancer was 1.26 times higher in the people with schizophrenia than in the general population (95% CI: 1.20-1.32). In comparison with the psychiatric patient group, the hazard ratio was 1.17 (95% CI: 1.11-1.28). Among women with schizophrenia, the hazard of breast cancer was greater among those who took antipsychotic medications for 1 year or more compared with those who took antipsychotics for less than 6 months. Women with schizophrenia have an elevated risk of breast cancer, and long-term use of antipsychotics is associated with an increased risk of breast cancer.

Discovering explainable biomarkers for breast cancer anti-PD1 response via network Shapley value analysis

Background and objectiveImmunotherapy holds promise in enhancing pathological complete response rates in breast cancer, albeit confined to a select cohort of patients. Consequently, pinpointing factors predictive of treatment responsiveness is of paramount importance. Gene expression and regulation, inherently operating within intricate networks, constitute fundamental molecular machinery for cellular processes and often serve as robust biomarkers. Nevertheless, contemporary feature selection approaches grapple with two key challenges: opacity in modeling and scarcity in accounting for gene-gene interactions MethodsTo address these limitations, we devise a novel feature selection methodology grounded in cooperative game theory, harmoniously integrating with sophisticated machine learning models. This approach identifies interconnected gene regulatory network biomarker modules with priori genetic linkage architecture. Specifically, we leverage Shapley values on network to quantify feature importance, while strategically constraining their integration based on network expansion principles and nodal adjacency, thereby fostering enhanced interpretability in feature selection. We apply our methods to a publicly available single-cell RNA sequencing dataset of breast cancer immunotherapy responses, using the identified feature gene set as biomarkers. Functional enrichment analysis with independent validations further illustrates their effective predictive performance ResultsWe demonstrate the sophistication and excellence of the proposed method in data with network structure. It unveiled a cohesive biomarker module encompassing 27 genes for immunotherapy response. Notably, this module proves adept at precisely predicting anti-PD1 therapeutic outcomes in breast cancer patients with classification accuracy of 0.905 and AUC value of 0.971, underscoring its unique capacity to illuminate gene functionalities ConclusionThe proposed method is effective for identifying network module biomarkers, and the detected anti-PD1 response biomarkers can enrich our understanding of the underlying physiological mechanisms of immunotherapy, which have a promising application for realizing precision medicine.

Obesity and lack of breastfeeding: a perfect storm to augment risk of breast cancer?

Triple-negative breast cancer (TNBC) is one of the most aggressive subtypes of breast cancer with higher rates of recurrence and distant metastasis, as well as decreased 5-year survival rates. Racial disparities are evident in the incidence and mortality rates of triple negative breast cancer particularly increased in young African American women. Concurrently, young African American women have multiple risk factors for TNBC including higher rates of premenopausal abdominal obesity (higher waist-hip ratio) and lower rates of breastfeeding with higher parity, implicating these factors as potentially contributors to poor outcomes. By understanding the mechanisms of how premenopausal obesity and lack of breastfeeding may be associated with increased risk of triple negative breast cancer, we can determine the best strategies for intervention and awareness to improve outcomes in TNBC.

The role of ethnic enclaves and neighborhood socioeconomic status in invasive breast cancer incidence rates among Asian American, Native Hawaiian, and Pacific Islander females in California.

Few studies have examined whether the incidence rates of invasive breast cancer among Asian American, Native Hawaiian, and Pacific Islander (AANHPI) populations differ by the neighborhood social environment. Thus, we examined associations of ethnic enclave and neighborhood socioeconomic status (nSES) with breast cancer incidence rates among AANHPI females in California. A total of 14,738 AANHPI females diagnosed with invasive breast cancer in 2008-2012 were identified from the California Cancer Registry. AANHPI ethnic enclaves (culturally distinct neighborhoods) and nSES were assessed at the census tract level using 2007-2011 American Community Survey data. Breast cancer age-adjusted incidence rates and incidence rate ratios (IRRs) were estimated for AANHPI ethnic enclave, nSES, and their joint effects. Subgroup analyses were conducted by stage of disease. The incidence rate of breast cancer among AANHPI females living in lowest ethnic enclave neighborhoods (quintile (Q)1) were 1.21 times (95% Confidence Interval (CI) 1.11, 1.32) that of AANHPI females living highest ethnic enclave neighborhoods (Q5). In addition, AANHPI females living in highest vs. lowest SES neighborhoods had higher incidence rates of breast cancer (Q5 vs. Q1 IRR = 1.30, 95% CI 1.22 to 1.40). The incidence rate of breast cancer among AANHPI females living in low ethnic enclave + high SES neighborhoods was 1.32 times (95% CI 1.25, 1.39) that of AANHPI females living in high ethnic enclave + low SES neighborhoods. Similar patterns of associations were observed for localized and advanced stage disease. For AANHPI females in California, incidence rates of breast cancer differed by nSES, ethnic enclave, when considered independently and jointly. Future studies should examine whether the impact of these neighborhood-level factors on breast cancer incidence rates differ across specific AANHPI ethnic groups and investigate the pathways through which they contribute to breast cancer incidence.

Quantum classical hybrid convolutional neural networks for breast cancer diagnosis.

The World Health Organization states that early diagnosis is essential to increasing the cure rate for breast cancer, which poses a danger to women's health worldwide. However, the efficacy and cost limitations of conventional diagnostic techniques increase the possibility of misdiagnosis. In this work, we present a quantum hybrid classical convolutional neural network (QCCNN) based breast cancer diagnosis approach with the goal of utilizing quantum computing's high-dimensional data processing power and parallelism to increase diagnosis efficiency and accuracy. When working with large-scale and complicated datasets, classical convolutional neural network (CNN) and other machine learning techniques generally demand a large amount of computational resources and time. Their restricted capacity for generalization makes it challenging to maintain consistent performance across multiple data sets. To address these issues, this paper adds a quantum convolutional layer to the classical convolutional neural network to take advantage of quantum computing to improve learning efficiency and processing speed. Simulation experiments on three breast cancer datasets, GBSG, SEER and WDBC, validate the robustness and generalization of QCCNN and significantly outperform CNN and logistic regression models in classification accuracy. This study not only provides a novel method for breast cancer diagnosis but also achieves a breakthrough in breast cancer diagnosis and promotes the development of medical diagnostic technology.