An overview of mammographic density and its association with breast cancer

Spatial N-glycomics of the normal breast microenvironment reveals fucosylated and high-mannose N-glycan signatures related to BI-RADS density and ancestry

Higher breast cancer mortality rates continue to disproportionally affect black women (BW) compared to white women (WW). This disparity is largely due to differences in tumor aggressiveness that can be related to distinct ancestry-associated breast tumor microenvironments (TMEs). Yet, characterization of the normal microenvironment (NME) in breast tissue and how they associate with breast cancer risk factors remains unknown. N-glycans, a glucose metabolism-linked post-translational modification, has not been characterized in normal breast tissue. We hypothesized that normal female breast tissue with distinct Breast Imaging and Reporting Data Systems (BI-RADS) categories have unique microenvironments based on N-glycan signatures that varies with genetic ancestries. Profiles of N-glycans were characterized in normal breast tissue from BW (n = 20) and WW (n = 20) at risk for breast cancer using matrix assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI). A total of 176 N-glycans (32 core-fucosylated and 144 noncore-fucosylated) were identified in the NME. We found that certain core-fucosylated, outer-arm fucosylated and high-mannose N-glycan structures had specific intensity patterns and histological distributions in the breast NME dependent on BI-RADS densities and ancestry. Normal breast tissue from BW, and not WW, with heterogeneously dense breast densities followed high-mannose patterns as seen in invasive ductal and lobular carcinomas. Lastly, lifestyles factors (e.g. age, menopausal status, Gail score, BMI, BI-RADS) differentially associated with fucosylated and high-mannose N-glycans based on ancestry. This study aims to decipher the molecular signatures in the breast NME from distinct ancestries towards improving the overall disparities in breast cancer burden.

3D Cell Migration Chip (3DCM-Chip): A New Tool toward the Modeling of 3D Cellular Complex Systems

3D hydrogel-based cell cultures provide models for studying cell behavior and can efficiently replicate the physiologic environment. Hydrogels can be tailored to mimic mechanical and biochemical properties of specific tissues and allow to produce gel-in-gel models. In this system, microspheres encapsulating cells are embedded in an outer hydrogel matrix, where cells are able to migrate. To enhance the efficiency of such studies, a lab-on-a-chip named 3D cell migration-chip (3DCM-chip) is designed, which offers substantial advantages over traditional methods. 3DCM-chip facilitates the analysis of biochemical and physical stimuli effects on cell migration/invasion in different cell types, including stem, normal, and tumor cells. 3DCM-chip provides a smart platform for developing more complex cell co-cultures systems. Herein the impact of human fibroblasts on MDA-MB 231 breast cancer cells' invasiveness is investigated. Moreover, how the presence of different cellular lines, including mesenchymal stem cells, normal human dermal fibroblasts, and human umbilical vein endothelial cells, affects the invasive behavior of cancer cells is investigated using 3DCM-chip. Therefore, predictive tumoroid models with a more complex network of interactions between cells and microenvironment are here produced. 3DCM-chip moves closer to the creation of in vitro systems that can potentially replicate key aspects of the physiological tumor microenvironment.

Tamoxifen Dose De-Escalation: An Effective Strategy for Reducing Adverse Effects?

Tamoxifen, a cornerstone in the adjuvant treatment of estrogen receptor-positive breast cancer, significantly reduces breast cancer recurrence and breast cancer mortality; however, its standard adjuvant dose of 20 mg daily presents challenges due to a broad spectrum of adverse effects, contributing to high discontinuation rates. Dose reductions of tamoxifen might be an option to reduce treatment-related toxicity, but large randomized controlled trials investigating the tolerability and, more importantly, efficacy of low-dose tamoxifen in the adjuvant setting are lacking. We conducted an extensive literature search to explore evidence on the tolerability and clinical efficacy of reduced doses of tamoxifen. In this review, we discuss two important topics regarding low-dose tamoxifen: (1) the incidence of adverse effects and quality of life among women using low-dose tamoxifen; and (2) the clinical efficacy of low-dose tamoxifen examined in the preventive setting and evaluated through the measurement of several efficacy derivatives. Moreover, practical tools for tamoxifen dose reductions in the adjuvant setting are provided and further research to establish optimal dosing strategies for individual patients are discussed.

Risk of Breast Cancer After Diabetes in Pregnancy: A Population-based Cohort Study

OBJECTIVES

Diabetes is associated with an increased risk of several cancers, including postmenopausal breast cancer. The evidence for higher breast cancer risk after diabetes in pregnancy is conflicting. We compared the incidence of breast and other cancers between pregnant women with and without diabetes.

METHODS

This work was a propensity-matched, retrospective cohort study using population-based health-care databases from Ontario, Canada. Those deliveries with gestational diabetes mellitus (GDM) and pregestational diabetes mellitus (pregestational DM) were identified and matched to deliveries without diabetes mellitus (non-DM). Deliveries from each diabetes cohort were matched 1:2 on age, parity, year of delivery, and propensity score to non-DM deliveries. Matched subjects were followed from delivery for incidence of breast cancer as a primary outcome, and other site-specific cancers as secondary outcomes. We performed Cox proportional hazards regression to compare rates of breast cancer between matched groups.

RESULTS

Over a median of 8 (interquartile range 4 to 13) years of follow-up, compared with non-DM deliveries, the incidence of breast cancer was significantly lower for GDM but similar for pregestational DM deliveries (hazard ratio [HR] 0.90, 95% confidence interval [CI] 0.82 to 0.98; and HR 0.92, 95% CI 0.80 to 1.07, respectively). GDM was associated with a significantly higher incidence of pancreatic and hepatocellular cancer, and pregestational DM was associated with a higher incidence of thyroid, hepatocellular, and endometrial cancers.

CONCLUSIONS

Diabetes in pregnancy does not have a higher short-term risk of subsequent breast cancer, but there may be a higher incidence of other cancers.

Diagnostic performance of contrast-enhanced mammography for suspicious findings in dense breasts: A systematic review and meta-analysis

PURPOSE

Contrast-enhanced spectral imaging (CEM) is a new mammography technique, but its diagnostic value in dense breasts is still inconclusive. We did a systematic review and meta-analysis of studies evaluating the diagnostic performance of CEM for suspicious findings in dense breasts.

MATERIALS AND METHODS

The PubMed, Embase, and Cochrane Library databases were searched systematically until August 6, 2023. Prospective and retrospective studies were included to evaluate the diagnostic performance of CEM for suspicious findings in dense breasts. The QUADAS-2 tool was used to evaluate the quality and risk of bias of the included studies. STATA V.16.0 and Review Manager V.5.3 were used to meta-analyze the included studies.

RESULTS

A total of 10 studies (827 patients, 958 lesions) were included. These 10 studies reported the diagnostic performance of CEM for the workup of suspicious lesions in patients with dense breasts. The summary sensitivity and summary specificity were 0.95 (95% CI, 0.92-0.97) and 0.81 (95% CI, 0.70-0.89), respectively. Enhanced lesions, circumscribed margins, and malignancy were statistically correlated. The relative malignancy OR value of the enhanced lesions was 28.11 (95% CI, 6.84-115.48). The relative malignancy OR value of circumscribed margins was 0.17 (95% CI, 0.07-0.45).

CONCLUSION

CEM has high diagnostic performance in the workup of suspicious findings in dense breasts, and when lesions are enhanced and have irregular margins, they are often malignant.

Surgical and Radiology Trainees' Proficiency in Reading Mammograms: the Importance of Education for Cancer Localisation

Medical imaging with mammography plays a very important role in screening and diagnosis of breast cancer, Australia's most common female cancer. The visualisation of cancers on mammograms often forms a diagnosis and guidance for radiologists and breast surgeons, and education platforms that provide real cases in a simulated testing environment have been shown to improve observer performance for radiologists. This study reports on the performance of surgical and radiology trainees in locating breast cancers. An enriched test set of 20 mammography cases (6 cancer and 14 cancer free) was created, and 18 surgical trainees and 32 radiology trainees reviewed the cases via the Breast Screen Reader Assessment Strategy (BREAST) platform and marked any lesions identifiable. Further analysis of performance with high- and low-density cases was undertaken, and standard metrics including sensitivity and specificity. Radiology trainees performed significantly better than surgical trainees in terms of specificity (0.72 vs. 0.35; P < 0.01). No significant differences were observed between the surgical and radiology trainees in sensitivity or lesion sensitivity. Mixed results were obtained with participants regarding breast density, with higher density cases generally having lower performance. The higher specificity of the radiology trainees compared to the surgical trainees likely represents less exposure to negative mammography cases. The use of high-fidelity simulated self-test environments like BREAST is able to benchmark, understand and build strategies for improving cancer education in a safe environment, including identifying challenging scenarios like breast density for enhanced training.

Experimental and Computational Analysis of High-Intensity Focused Ultrasound Thermal Ablation in Breast Cancer Cells: Monolayers vs. Spheroids

High-intensity focused ultrasound (HIFU) is a non-invasive therapeutic modality that uses precise acoustic energy to ablate cancerous tissues through coagulative necrosis. In this context, we investigate the efficacy of HIFU ablation in two distinct cellular configurations, namely 2D monolayers and 3D spheroids of epithelial breast cancer cell lines (MDA-MB 231 and MCF7). The primary objective is to compare the response of these two in vitro models to HIFU while measuring their ablation percentages and temperature elevation levels. HIFU was systematically applied to the cell cultures, varying ultrasound intensity and duty cycle during different sonication sessions. The results indicate that the degree of ablation is highly influenced by the duty cycle, with higher duty cycles resulting in greater ablation percentages, while sonication duration has a minimal impact. Numerical simulations validate experimental observations, highlighting a significant disparity in the response of 2D monolayers and 3D spheroids to HIFU treatment. Specifically, tumor spheroids require lower temperature elevations for effective ablation, and their ablation percentage significantly increases with elevated duty cycles. This study contributes to a comprehensive understanding of acoustic energy conversion within the biological system during HIFU treatment for 2D versus 3D ablation targets, holding potential implications for refining and personalizing breast cancer therapeutic strategies.

Mechanosensitive TRPV4 channel guides maturation and organization of the bilayered mammary epithelium

Biophysical cues from the cell microenvironment are detected by mechanosensitive components at the cell surface. Such machineries convert physical information into biochemical signaling cascades within cells, subsequently leading to various cellular responses in a stimulus-dependent manner. At the surface of extracellular environment and cell cytoplasm exist several ion channel families that are activated by mechanical signals to direct intracellular events. One of such channel is formed by transient receptor potential cation channel subfamily V member, TRPV4 that is known to act as a mechanosensor in wide variaty of tissues and control ion-influx in a spatio-temporal way. Here we report that TRPV4 is prominently expressed in the stem/progenitor cell populations of the mammary epithelium and seems important for the lineage-specific differentiation, consequently affecting mechanical features of the mature mammary epithelium. This was evident by the lack of several markers for mature myoepithelial and luminal epithelial cells in TRPV4-depleted cell lines. Interestingly, TRPV4 expression is controlled in a tension-dependent manner and it also impacts differentation process dependently on the stiffness of the microenvironment. Furthermore, such cells in a 3D compartment were disabled to maintain normal mammosphere structures and displayed abnormal lumen formation, size of the structures and disrupted cellular junctions. Mechanosensitive TRPV4 channel therefore act as critical player in the homeostasis of normal mammary epithelium through sensing the physical environment and guiding accordingly differentiation and structural organization of the bilayered mammary epithelium.

Polygenic risk score-based prediction of breast cancer risk in Taiwanese women with dense breast using a retrospective cohort study

Mammographic screening has contributed to a significant reduction in breast cancer mortality. Several studies have highlighted the correlation between breast density, as detected through mammography, and a higher likelihood of developing breast cancer. A polygenic risk score (PRS) is a numerical score that is calculated based on an individual's genetic information. This study aims to explore the potential roles of PRS as candidate markers for breast cancer development and investigate the genetic profiles associated with clinical characteristics in Asian females with dense breasts. This is a retrospective cohort study integrated breast cancer screening, population genotyping, and cancer registry database. The PRSs of the study cohort were estimated using genotyping data of 77 single nucleotide polymorphisms based on the PGS000001 Catalog. A subgroup analysis was conducted for females without breast symptoms. Breast cancer patients constituted a higher proportion of individuals in PRS Q4 (37.8% vs. 24.8% in controls). Among dense breast patients with no symptoms, the high PRS group (Q4) consistently showed a significantly elevated breast cancer risk compared to the low PRS group (Q1-Q3) in both univariate (OR = 2.25, 95% CI 1.43-3.50, P < 0.001) and multivariate analyses (OR: 2.23; 95% CI 1.41-3.48, P < 0.001). The study was extended to predict breast cancer risk using common low-penetrance risk variants in a PRS model, which could be integrated into personalized screening strategies for Taiwanese females with dense breasts without prominent symptoms.

Breast Cancer Incidence Among Asian American Women in New York City: Disparities in Screening and Presentation

BACKGROUND

Asian American (AsAm) women have some of the lowest rates of up-to-date breast cancer screening, and lack of disaggregated racial/ethnic data can mask disparities. We evaluated presentation patterns among AsAms at two hospitals with distinct communities: New York Presbyterian-Queens (NYPQ), in Flushing, Queens and Weill Cornell Medical Center (WCM), on the Upper East Side (UES) neighborhood of Manhattan.

PATIENTS AND METHODS

Patients with newly diagnosed breast cancer between January 2019 and December 2022 were identified using a prospective database and clinical data collected. Patients were categorized as self-reported Asian versus Non-Asian. The Asian group was disaggregated as Chinese-Asian versus Other-Asian. Physician workforce data were obtained from public records.

RESULTS

A total of 3546 patients (1162 NYPQ, 2384 WCM) were included. More NYPQ patients identified as Asian compared with WCM (49 vs. 14%, p < 0.001). Asian patients were mostly East Asian Chinese (NYPQ 61%, WCM 53%). More Chinese patients at NYPQ reported Chinese as their preferred language (81 vs. 33%, p < 0.001). Greatest differences of screen-detected disease frequency were seen between NYPQ and WCM Chinese patients (75 vs. 59%, p < 0.001). Eighty percent of NYPQ Chinese patients presented with stage 0/I disease versus 69% at WCM (p = 0.007), a difference not observed between Other-Asian patients (75% NYPQ, 68% WCM, p = 0.095). 3% of UES physicians versus 16% in Flushing reported speaking Chinese.

CONCLUSIONS

Chinese patients residing in a neighborhood with more Chinese-speaking physicians more frequently presented with screen-detected, early-stage breast cancer. Stage distribution differences were not apparent among the aggregated pool of Other-Asian patients, suggesting cancer disparities may be masked when ethnic groups are studied in aggregate.

Constructing the Optimal Classification Model for Benign and Malignant Breast Tumors Based on Multifeature Analysis from Multimodal Images

The purpose of this study was to fuse conventional radiomic and deep features from digital breast tomosynthesis craniocaudal projection (DBT-CC) and ultrasound (US) images to establish a multimodal benign-malignant classification model and evaluate its clinical value. Data were obtained from a total of 487 patients at three centers, each of whom underwent DBT-CC and US examinations. A total of 322 patients from dataset 1 were used to construct the model, while 165 patients from datasets 2 and 3 formed the prospective testing cohort. Two radiologists with 10-20 years of work experience and three sonographers with 12-20 years of work experience semiautomatically segmented the lesions using ITK-SNAP software while considering the surrounding tissue. For the experiments, we extracted conventional radiomic and deep features from tumors from DBT-CCs and US images using PyRadiomics and Inception-v3. Additionally, we extracted conventional radiomic features from four peritumoral layers around the tumors via DBT-CC and US images. Features were fused separately from the intratumoral and peritumoral regions. For the models, we tested the SVM, KNN, decision tree, RF, XGBoost, and LightGBM classifiers. Early fusion and late fusion (ensemble and stacking) strategies were employed for feature fusion. Using the SVM classifier, stacking fusion of deep features and three peritumoral radiomic features from tumors in DBT-CC and US images achieved the optimal performance, with an accuracy and AUC of 0.953 and 0.959 [CI: 0.886-0.996], a sensitivity and specificity of 0.952 [CI: 0.888-0.992] and 0.955 [0.868-0.985], and a precision of 0.976. The experimental results indicate that the fusion model of deep features and peritumoral radiomic features from tumors in DBT-CC and US images shows promise in differentiating benign and malignant breast tumors.

Association between Family History of Breast Cancer and Breast Density in Saudi Premenopausal Women Participating in Mammography Screening

BACKGROUND

Mammographic density and family history of breast cancer (FHBC) are well-established independent factors affecting breast cancer risk; however, the association between these two risk factors in premenopausal-screened women remains unclear. The aim of this study is to investigate the relationship between mammographic density and FHBC among Saudi premenopausal-screened women.

METHODS

A total of 446 eligible participants were included in the study. Mammographic density was assessed qualitatively using the Breast Imaging Reporting and Data System (BIRADS 4th edition). Logistic regression models were built to investigate the relationship between mammographic density and FHBC.

RESULTS

Women with a family history of breast cancer demonstrated an 87% greater chance of having dense tissue than women without a family history of breast cancer (95% CI: 1.14-3.08; p = 0.01). Having a positive family history for breast cancer in mothers was significantly associated with dense tissue (adjusted odds ratio (OR): 5.6; 95% CI: 1.3-24.1; p = 0.02).

CONCLUSION

Dense breast tissue in Saudi premenopausal women undergoing screening may be linked to FHBC. If this conclusion is replicated in larger studies, then breast cancer risk prediction models must carefully consider these breast cancer risk factors.

BRCA1 and BRCA2 germline mutations in Chinese Hakka breast cancer patients

OBJECTIVE

To investigate the prevalence of BRCA1/2 gene variants and evaluate the clinical and pathological characteristics associated with these variants in Chinese Hakka breast cancer patients.

METHODS

A total of 409 breast cancer patients were analyzed based on next-generation sequencing results, with 337 categorized as non-carriers and 72 as carriers of BRCA1/2 variants. Data on the patients' BRCA1/2 gene mutation status, clinical and pathological characteristics, as well as menstrual and reproductive information, were collected, analyzed, compared, and tabulated. Logistic regression analysis was performed to explore the relationship between clinical characteristics and pathogenic variants.

RESULTS

Among the patients, 72 were identified as carriers of pathogenic or likely pathogenic variants in BRCA1/2, while 337 had likely benign or benign mutations. The BRCA1 c.2635G > T (p. Glu879*) variant was detected at a high frequency, accounting for 12.5% (4/32) of the BRCA1 mutations, while the c.5164_5165del (p.Ser1722Tyrfs*4) variant was common among the BRCA2 mutations, accounting for 17.5% (7/40). It was observed that a higher proportion of BRCA1 carriers had the triple-negative breast cancer subtype, whereas more BRCA2 carriers exhibited estrogen receptor (ER) + and progesterone receptor (PR) + subtypes. Multivariate logistic regression analysis revealed that a family history of cancer (OR = 2.36, 95% CI = 1.00-5.54), bilateral cancer (OR = 4.78, 95% CI 1.61-14.20), human epidermal growth factor receptor 2 (HER2)- (OR = 8.23, 95% CI 3.25-20.84), and Ki67 ≥ 15% (OR = 3.88, 95% CI 1.41-10.65) were associated with BRCA1/2 mutations, with the age at diagnosis, age at menarche, and premenopausal status serving as covariates.

CONCLUSIONS

The most common pathogenic variant of the BRCA1 and BRCA2 in breast cancer patients was c.2635G > T and c.5164_5165del, respectively. Additionally, a family history of cancer, bilateral cancer, HER2-, and Ki67 ≥ 15% were identified as independent predictors of BRCA1/2 pathogenic variants.

Epidemiology of Breast, Corpus Uteri, and Ovarian Cancers in Lebanon With Emphasis on Breast Cancer Incidence Trends and Risk Factors Compared to Regional and Global Rates

OBJECTIVES

This study explores the incidence and trends of breast (Bca), corpus uteri (CUca), and ovarian (Oca) cancer in Lebanon, a Middle Eastern country. It compares the Bca rates to regional and global ones and discusses Bca risk factors in Lebanon.

INTRODUCTION

Globally, Bca is the premier cause of cancer morbidity and mortality in women.

METHODS

Data on female Bca, CUca, and Oca published by the Lebanese national cancer registry were obtained (ie, for the years of 2005 to 2016). The age-standardized incidence rates (ASIRw) and age-specific rates per 100,000 female population were computed.

RESULTS

From 2005 to 2016, Bca, Oca, and CUca ranked first, sixth, and seventh, respectively, for cancer incidence among women in Lebanon. Bca alone accounted for 39.4% of all new female cancer cases. The ASIRw increased significantly for Bca and CUca (APC: 3.60 and 3.73, P < .05) but not for Oca (APC: 1.27, P > .05). The Bca ASIRw (per 100,000) increased significantly from 71.0 in 2005 to 115.6 in 2013 (P < .05), then decreased steadily but non-significantly to reach 96.8 in 2016 (P > .05). Lebanon's Bca ASIRw is comparable to developed countries. This may reflect altered sociological and reproductive patterns as the country transitions from regional to global trends. The five-year age-specific rates analysis revealed that Bca rates rose steeply from 35-39 to 50-54, dropped slightly between 55 and 64, then rose till 75+. The five-year age-specific rates between 35 and 54 among Lebanese women were amongst the highest worldwide from 2008 to 2012, even higher than the rates in Belgium, which had the highest ASIRw of Bca worldwide in 2020.

CONCLUSION

Lebanon's Bca ASIRw is among the highest globally. It's important to investigate the contributing factors and develop a national Bca control strategy. This study supports the national recommendation in initiating Bca screening at age 40 for women.

Practical Challenges of DBT-Guided VABB: Harms and Benefits, from Literature to Clinical Experience

Vacuum-assisted breast biopsy (VABB) guided by digital breast tomosynthesis (DBT) represents one of the best instruments to obtain a histological diagnosis of suspicious lesions with no ultrasound correlation or those which are visible only on DBT. After a review of the literature, we retrospectively analyzed the DBT-guided VABBs performed from 2019 to 2022 at our department. Descriptive statistics, Pearson's correlation and χ2 test were used to compare distributions of age, breast density (BD) and early performance measures including histopathology. We used kappa statistics to evaluate the agreement between histological assessment and diagnosis. Finally, we compared our experience to the literature to provide indications for clinical practice. We included 85 women aged 41-84 years old. We identified 37 breast cancers (BC), 26 stage 0 and 11 stage IA. 67.5% of BC was diagnosed in women with high BD. The agreement between VABB and surgery was 0.92 (k value, 95% CI: 0.76-1.08). We found a statistically significant inverse correlation between age and BD. The post-procedural clip was correctly positioned in 88.2%. The post-procedural hematoma rate was 14.1%. No infection or hemorrhage were recorded. When executed correctly, DBT-guided VABB represents a safe and minimally invasive technique with high histopathological concordance, for detecting nonpalpable lesions without ultrasound correlation.

Automated 3D Ultrasound as an Adjunct to Screening Mammography Programs in Dense Breast: Literature Review and Metanalysis

Purpose: The purpose of this meta-analysis is to investigate the effectiveness of supplementing screening mammography with three-dimensional automated breast ultrasonography (3D ABUS) in improving breast cancer detection rates in asymptomatic women with dense breasts. Materials and Methods: We conducted a thorough review of scientific publications comparing 3D ABUS and mammography. Articles for inclusion were sourced from peer-reviewed journal databases, namely MEDLINE (PubMed) and Scopus, based on an initial screening of their titles and abstracts. To ensure a sufficient sample size for meaningful analysis, only studies evaluating a minimum of 20 patients were retained. Eligibility for evaluation was further limited to articles written in English. Additionally, selected studies were required to have participants aged 18 or above at the time of the study. We analyzed 25 studies published between 2000 and 2021, which included a total of 31,549 women with dense breasts. Among these women, 229 underwent mammography alone, while 347 underwent mammography in combination with 3D ABUS. The average age of the women was 50.86 years (±10 years standard deviation), with a range of 40-56 years. In our efforts to address and reduce bias, we applied a range of statistical analyses. These included assessing study variation through heterogeneity assessment, accounting for potential study variability using a random-effects model, exploring sources of bias via meta-regression analysis, and checking for publication bias through funnel plots and the Egger test. These methods ensured the reliability of our study findings. Results: According to the 25 studies included in this metanalysis, out of the total number of women, 27,495 were diagnosed with breast cancer. Of these, 211 were diagnosed through mammography alone, while an additional 329 women were diagnosed through the combination of full-field digital mammography (FFDSM) and 3D ABUS. This represents an increase of 51.5%. The rate of cancers detected per 1000 women screened was 23.25‱ (95% confidence interval [CI]: 21.20, 25.60; p < 0.001) with mammography alone. In contrast, the addition of 3D ABUS to mammography increased the number of tumors detected to 20.95‱ (95% confidence interval [CI]: 18.50, 23; p < 0.001) per 1000 women screened. Discussion: Even though variability in study results, lack of long-term outcomes, and selection bias may be present, this systematic review and meta-analysis confirms that supplementing mammography with 3D ABUS increases the accuracy of breast cancer detection in women with ACR3 to ACR4 breasts. Our findings suggest that the combination of mammography and 3D ABUS should be considered for screening women with dense breasts. Conclusions: Our research confirms that adding 3D automated breast ultrasound to mammography-only screening in patients with dense breasts (ACR3 and ACR4) significantly (p < 0.05) increases the cancer detection rate.

The Level of Awareness Among Healthcare Practitioners Regarding the Relationship Between Breast Density and Breast Cancer

Background Breast cancer is the most prevalent cancer in women, accounting for around 23% of all cancer-related deaths across 140 nations. The awareness about breast density (BD) has a significant impact on early diagnosis of breast cancer. Aim and objective This study aims to assess the awareness of healthcare providers about BD in King Abdullah Medical City. Methods This is an analytical cross-sectional questionnaire-based study among the healthcare practitioners of KAMC in Makkah, Saudi Arabia. Questions measured knowledge about BD and a pass mark indicated participant awareness. The collected data were analyzed using SPSS, and a chi-square test used for bivariate analysis. Results Out of 124 participants, 41% were well aware. Physicians (37% of the sample) were significantly more aware than allied healthcare practitioners and nurses (awareness: 59.6%, 33.3%, 30.4% respectively, (p = 0.03)). Regarding specialty, radiologists and surgeons had the top level of awareness (62% and 64%, respectively) as compared to oncologists (47.1%) and other specialties (29.7%), (p= 0.016). Those above 40 years of age were more aware than those below 40 years (awareness: 62.1% and 34%, respectively, (p=0.007)). Non-significant factors included: gender, years of experience, screened versus non-screened, and receiving information before about BD (p > 0.05). Conclusion The results of this population-based study indicate the existence of moderate deficits in the general knowledge about BD and its relation to breast cancer. This might lead to a late diagnosis. The results showed no dramatic differences in the awareness among healthcare providers.

Integrating clinical research in an operative screening and diagnostic breast imaging department: First experience, results and perspectives using microwave imaging

Rationale and objectives

Clinical research is crucial for evaluating new medical procedures and devices. It is important for healthcare units and hospitals to minimize the disruptions caused by conducting clinical studies; however, complex clinical pathways require dedicated recruitment and study designs.This work presents the effective introduction of novel microwave breast imaging (MBI), via MammoWave apparatus, into the clinical routine of an operative screening and diagnostic breast imaging department for conducting a multicentric clinical study.

Materials and methods

Microwave breast imaging, using MammoWave apparatus, was performed on volunteers coming from different clinical pathways. Clinical data, comprising demographics and conventional radiologic reports (used as reference standard), was collected; a satisfaction questionnaire was filled by every volunteer. Microwave images were analyzed by an automatic clinical decision support system, which quantified their corresponding features to discriminate between breasts with no relevant radiological findings (NF) and breasts with described findings (WF).

Results

Conventional breast imaging (DBT, US, MRI) and MBI were performed and adapted to assure best clinical practices and optimum pathways. 180 volunteers, both symptomatic and asymptomatic, were enrolled in the study. After microwave images' quality assessment, 48 NF (15 dense) and 169 WF (88 dense) breasts were used for the prospective study; 48 (18 dense) breasts suffered from a histology-confirmed carcinoma. An overall sensitivity of 85.8 % in breasts lesions' detection was achieved by the microwave imaging apparatus.

Conclusion

An optimum recruitment strategy was implemented to assess MBI. Future trials may show the clinical usefulness of microwave imaging, which may play an important role in breast screening.

Recent progress on the effect of extracellular matrix on occurrence and progression of breast cancer

Breast cancer (BC) metastasis is an enormous challenge targeting BC therapy. The extracellular matrix (ECM), the principal component of the BC metastasis niche, is the pivotal driver of breast tumor development, whose biochemical and biophysical characteristics have attracted widespread attention. Here, we review the biological effects of ECM constituents and the influence of ECM stiffness on BC metastasis and drug resistance. We provide an overview of the relative signal transduction mechanisms, existing metastasis models, and targeted drug strategies centered around ECM stiffness. It will shed light on exploring more underlying targets and developing specific drugs aimed at ECM utilizing biomimetic platforms, which are promising for breast cancer treatment.

Breast Imaging Physics in Mammography (Part I)

Breast cancer is the most frequently diagnosed neoplasm in women in Italy. There are several risk factors, but thanks to screening and increased awareness, most breast cancers are diagnosed at an early stage when surgical treatment can most often be conservative and the adopted therapy is more effective. Regular screening is essential but advanced technology is needed to achieve quality diagnoses. Mammography is the gold standard for early detection of breast cancer. It is a specialized technique for detecting breast cancer and, thus, distinguishing normal tissue from cancerous breast tissue. Mammography techniques are based on physical principles: through the proper use of X-rays, the structures of different tissues can be observed. This first part of the paper attempts to explain the physical principles used in mammography. In particular, we will see how a mammogram is composed and what physical principles are used to obtain diagnostic images.

Propagation-based phase-contrast imaging of the breast: image quality and the effect of X-ray energy and radiation dose

OBJECTIVES

Propagation-based phase-contrast computed tomography (PB-CT) is a new imaging technique that exploits refractive and absorption properties of X-rays to enhance soft tissue contrast and improve image quality. This study compares image quality of PB-CT and absorption-based CT (AB-CT) for breast imaging while exploring X-ray energy and radiation dose.

METHODS

Thirty-nine mastectomy samples were scanned at energy levels of 28-34keV using a flat panel detector at radiation dose levels of 4mGy and 2mGy. Image quality was assessed using signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), spatial resolution (res) and visibility (vis). Statistical analysis was performed to compare PB-CT images against their corresponding AB-CT images scanned at 32keV and 4mGy.

RESULTS

The PB-CT images at 4mGy, across nearly all energy levels, demonstrated superior image quality than AB-CT images at the same dose. At some energy levels, the 2mGy PB-CT images also showed better image quality in terms of CNR/Res and vis compared to the 4mGy AB-CT images. At both investigated doses, SNR and SNR/res were found to have a statistically significant difference across all energy levels. The difference in vis was statistically significant at some energy levels.

CONCLUSION

This study demonstrates superior image quality of PB-CT over AB-CT, with X-ray energy playing a crucial role in determining image quality parameters.

ADVANCES IN KNOWLEDGE

Our findings reveal that standard dose PB-CT outperforms standard dose AB-CT across all image quality metrics. Additionally, we demonstrate that low dose PB-CT can produce superior images compared to standard dose AB-CT in terms of CNR/Res and vis.

A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows screening, follow up, and diagnosis for breast tumor with high sensitivity. Accurate tumor segmentation from DCE-MRI can provide crucial information of tumor location and shape, which significantly influences the downstream clinical decisions. In this paper, we aim to develop an artificial intelligence (AI) assistant to automatically segment breast tumors by capturing dynamic changes in multi-phase DCE-MRI with a spatial-temporal framework. The main advantages of our AI assistant include (1) robustness, i.e., our model can handle MR data with different phase numbers and imaging intervals, as demonstrated on a large-scale dataset from seven medical centers, and (2) efficiency, i.e., our AI assistant significantly reduces the time required for manual annotation by a factor of 20, while maintaining accuracy comparable to that of physicians. More importantly, as the fundamental step to build an AI-assisted breast cancer diagnosis system, our AI assistant will promote the application of AI in more clinical diagnostic practices regarding breast cancer.

[Breast density assessment and organised breast cancer screening]

INTRODUCTION

The objective of this study was to evaluate the intra- and inter-rater agreement of radiologists regarding the evaluation of breast density.

METHODOLOGY

Breast density assessments of 120 cases were performed by four radiologists in the city of Ouagadougou according to the fifth edition of the American College of Radiology BI-RADS atlas. Cohen's weighted kappa coefficients and Fleiss kappa coefficients were used to estimate agreement between observers and with a panel of three experts radiologists. A new evaluation of the 120 cases was performed by all raters one month after the initial evaluation.

RESULTS

Inter-rater kappa coefficients ranged from 0.55 to 0.74. The Fleiss kappa coefficient was 0.58, 0.43, 0.41, and 0.43 for categories A, B, C, and D respectively. In terms of classification into "sparse breasts" and "dense breasts", the kappa coefficients ranged from 0.47 to 0.67. Taking the results of the expert panel as a reference, the proportion of false positives in the diagnosis "dense breasts" ranged from 18.6% to 26.8%. Intraobserver agreement was good.

CONCLUSION

Our study showed moderate to good intra- and inter-raters agreements. Upgrading and harmonisation of practices will be used to empower radiologists to participate in organised breast cancer screening in Burkina Faso.

Breast cancer risk coordinators: Artificial intelligence-based density measurement and Mullerian-inhibiting substance

BACKGROUND

Due to its increasing prevalence, breast cancer has become a serious public health problem. In addition to the models used to identify individuals at risk, the search for fast and accurate tools has continued for years.

AIMS

In our study, we aimed to examine the correlation of mammographic density measurement and serum Mullerian-inhibiting substance (MIS) levels with an effective model such as Gail.

METHODS

Of the women whose serum MIS levels were measured in the last 1 year, 214 participants who applied for routine breast examination were included in the study. The age range was between 40 and 60. Exclusion criteria were determined as pathological mammographic findings, active breast symptom, and thoracic radiotherapy history. Mammographic density measurement (PD) was performed with the artificial intelligence-based Deep-LIBRA software. The relationship of these two parameters with the lifetime risk of developing breast cancer was examined.

RESULTS

The correlation between PD and GRP was remarkable (p < 0.01 cc:0.35). A positive correlation was observed between serum MIS levels and increased breast cancer, but it was not possible to prove this statistically (p = 0.056). It was thought that this situation was caused by perimenopausal patients. Because when the menopause group was excluded, the correlation between MIS levels and GRP decreased (p = 0.12 cc:0.17).

CONCLUSIONS

PD measurement can be considered as a promising method for the determination of individuals at risk for breast cancer in a large group of patients, but we think that serum MIS levels are not suitable for risk assessment in perimenopausal patients.

The association between mammographic density and breast cancer molecular subtypes: a systematic review and meta-analysis

AIM

To conduct a systematic review and meta-analysis to evaluate the whether high mammographic density (MD) is differentially associated with all subtypes of breast cancer.

MATERIALS AND METHODS

The PubMed, Cochrane Library, and Embase databases were searched systematically in October 2022 to include all studies that investigated the association between MD and breast cancer subtype. Aggregate data of 17,193 breast cancer cases from 23 studies were selected, including five cohort/case-control and 18 case-only studies. The relative risk (RR) of MD were combined using random/fixed effects models for case-control studies, and for case-only studies, relative risk ratios (RRRs) were a combination of luminal A, luminal B, and HER2-positive versus triple-negative tumours.

RESULTS

Women in the highest density category in case-control/cohort studies had a 2.24-fold (95% confidence interval [CI] 1.53, 3.28), 1.81-fold (95% CI 1.15, 2.85), 1.44-fold (95% CI 1.14, 1.81), and 1.59-fold (95% CI 0.89, 2.85) higher risk of triple-negative, HER-2 (human epidermal growth factor receptor 2) positive, luminal A, and luminal B breast cancer compared to women in the lowest density category. RRRs for breast tumours being luminal A, luminal B, and HER-2 positive versus triple-negative in case-only studies were 1.62 (95% CI 1.14, 2.31), 1.81 (95% CI 1.22, 2.71) and 2.58 (95% CI 1.63, 4.08), respectively, for BIRADS 4 versus BIRADS 1.

CONCLUSION

The evidence indicates MD is a potent risk factor for the majority of breast cancer subtypes to different degrees. Increased MD is more strongly linked to HER-2-positive cancers compared to other breast cancer subtypes. The application of MD as a subtype-specific risk marker may facilitate the creation of personalised risk prediction models and screening procedures.

Extensive review on breast cancer its etiology, progression, prognostic markers, and treatment

As the most frequent and vulnerable malignancy among women, breast cancer universally manifests a formidable healthcare challenge. From a biological and molecular perspective, it is a heterogenous disease and is stratified based on the etiological factors driving breast carcinogenesis. Notably, genetic predispositions and epigenetic impacts often constitute the heterogeneity of this disease. Typically, breast cancer is classified intrinsically into histological subtypes in clinical landscapes. These stratifications empower physicians to tailor precise treatments among the spectrum of breast cancer therapeutics. In this pursuit, numerous prognostic algorithms are extensively characterized, drastically changing how breast cancer is portrayed. Therefore, it is a basic requisite to comprehend the multidisciplinary rationales of breast cancer to assist the evolution of novel therapeutic strategies. This review aims at highlighting the molecular and genetic grounds of cancer additionally with therapeutic and phytotherapeutic context. Substantially, it also renders researchers with an insight into the breast cancer cell lines as a model paradigm for breast cancer research interventions.

Integrated molecular modeling and dynamics approaches revealed potential natural inhibitors of NF-κB transcription factor as breast cancer therapeutics

Breast cancer is a silent killer malady among women and a serious economic burden in health care management. A case of breast cancer is diagnosed among women every 19 s, and every 74 s, a woman dies of breast cancer somewhere in the world. Despite the pop-up of progressive research, advanced treatment approaches, and preventive measures, breast cancer remains amplifying ailment. The nuclear factor kappa B (NF-κB) is a key transcription factor that links inflammation with cancer and is demonstrated as being involved in the tumorigenesis of breast cancer. The NF-κB transcription factor family in mammals consists of five proteins; c-Rel, RelA(p65), RelB, NF-κB1(p50), and NF-κB2(p52). The antitumor effect of NF-κB has also been explored in breast cancer, however, the actual treatment for breast cancer is yet to be discovered. This study is attributed to the identification of novel drug targets against breast cancer by targeting c-Rel, RelA(p65), RelB, NF-κB1(p50), and NF-κB2(p52) proteins. To identify the putative active compounds, a structure-based 3D pharmacophore model to the protein active site cavity was generated followed by virtual screening, molecular docking, and molecular dynamics (MD) simulation. Initially, a library of 45000 compounds were docked against the target protein and five compounds namely Z56811101, Z653426226, Z1097341967, Z92743432, and Z464101066 were selected for further analysis. The relative binding affinity of Z56811101, Z653426226, Z1097341967, Z92743432, and Z464101066 with NF-κB1 (p50), NF-κB2 (p52), RelA (p65), RelB, and c-Rel proteins were -6.8, -8, -7.0, -6.9, and -7.2 kcal/mol, respectively which remained stable throughout the simulations of 200 ns. Furthermore, all of these compounds depict maximum drug-like properties. Therefore, the proposed compounds can be a potential candidate for patients with breast cancer, but, experimental validation is needed to ensure their safety.Communicated by Ramaswamy H. Sarma.

Outdoor air pollution and histologic composition of normal breast tissue

BACKGROUND

Biologic pathways underlying the association between outdoor air pollution and breast cancer risk are poorly understood. Breast tissue composition may reflect cumulative exposure to breast cancer risk factors and has been associated with breast cancer risk among patients with benign breast disease. Herein, we evaluated whether fine particulate matter (PM2.5) was associated with the histologic composition of normal breast tissue.

METHODS

Machine-learning algorithms were applied to digitized hematoxylin and eosin-stained biopsies of normal breast tissue to quantify the epithelium, stroma, adipose and total tissue area from 3,977 individuals aged 18-75 years from a primarily Midwestern United States population who donated breast tissue samples to the Susan G. Komen Tissue Bank (2009-2019). Annual levels of PM2.5 were assigned to each woman's residential address based on year of tissue donation. We applied predictive k-means to assign participants to clusters with similar PM2.5 chemical composition and used linear regression to examine the cross-sectional associations between a 5-μg/m3 increase in PM2.5 and square root-transformed proportions of epithelium, stroma, adipose, and epithelium-to-stroma proportion [ESP], overall and by PM2.5 cluster.

RESULTS

Higher residential PM2.5 was associated with lower proportion of breast stromal tissue [β = -0.93, 95% confidence interval: (-1.52, -0.33)], but was not related to the proportion of epithelium [β = -0.11 (-0.34, 0.11)]. Although PM2.5 was not associated with ESP overall [β = 0.24 (-0.16, 0.64)], the association significantly differed by PM2.5 chemical composition (p-interaction = 0.04), with a positive association evident only among an urban, Midwestern cluster with higher concentrations of nitrate (NO3-) and ammonium (NH4+) [β = 0.49 (0.03, 0.95)].

CONCLUSIONS

Our findings are consistent with a possible role of PM2.5 in breast cancer etiology and suggest that changes in breast tissue composition may be a potential pathway by which outdoor air pollution impacts breast cancer risk. This study further underscores the importance of considering heterogeneity in PM2.5 composition and its impact on breast carcinogenesis.

Breast cancer risk prediction combining a convolutional neural network-based mammographic evaluation with clinical factors

PURPOSE

Deep learning techniques, including convolutional neural networks (CNN), have the potential to improve breast cancer risk prediction compared to traditional risk models. We assessed whether combining a CNN-based mammographic evaluation with clinical factors in the Breast Cancer Surveillance Consortium (BCSC) model improved risk prediction.

METHODS

We conducted a retrospective cohort study among 23,467 women, age 35-74, undergoing screening mammography (2014-2018). We extracted electronic health record (EHR) data on risk factors. We identified 121 women who subsequently developed invasive breast cancer at least 1 year after the baseline mammogram. Mammograms were analyzed with a pixel-wise mammographic evaluation using CNN architecture. We used logistic regression models with breast cancer incidence as the outcome and predictors including clinical factors only (BCSC model) or combined with CNN risk score (hybrid model). We compared model prediction performance via area under the receiver operating characteristics curves (AUCs).

RESULTS

Mean age was 55.9 years (SD, 9.5) with 9.3% non-Hispanic Black and 36% Hispanic. Our hybrid model did not significantly improve risk prediction compared to the BCSC model (AUC of 0.654 vs 0.624, respectively, p = 0.063). In subgroup analyses, the hybrid model outperformed the BCSC model among non-Hispanic Blacks (AUC 0.845 vs. 0.589; p = 0.026) and Hispanics (AUC 0.650 vs 0.595; p = 0.049).

CONCLUSION

We aimed to develop an efficient breast cancer risk assessment method using CNN risk score and clinical factors from the EHR. With future validation in a larger cohort, our CNN model combined with clinical factors may help predict breast cancer risk in a cohort of racially/ethnically diverse women undergoing screening.

Developing a Supplementary Diagnostic Tool for Breast Cancer Risk Estimation Using Ensemble Transfer Learning

Breast cancer is the most prevalent cancer worldwide. Thus, it is necessary to improve the efficiency of the medical workflow of the disease. Therefore, this study aims to develop a supplementary diagnostic tool for radiologists using ensemble transfer learning and digital mammograms. The digital mammograms and their associated information were collected from the department of radiology and pathology at Hospital Universiti Sains Malaysia. Thirteen pre-trained networks were selected and tested in this study. ResNet101V2 and ResNet152 had the highest mean PR-AUC, MobileNetV3Small and ResNet152 had the highest mean precision, ResNet101 had the highest mean F1 score, and ResNet152 and ResNet152V2 had the highest mean Youden J index. Subsequently, three ensemble models were developed using the top three pre-trained networks whose ranking was based on PR-AUC values, precision, and F1 scores. The final ensemble model, which consisted of Resnet101, Resnet152, and ResNet50V2, had a mean precision value, F1 score, and Youden J index of 0.82, 0.68, and 0.12, respectively. Additionally, the final model demonstrated balanced performance across mammographic density. In conclusion, this study demonstrates the good performance of ensemble transfer learning and digital mammograms in breast cancer risk estimation. This model can be utilised as a supplementary diagnostic tool for radiologists, thus reducing their workloads and further improving the medical workflow in the screening and diagnosis of breast cancer.

Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completion

Automated tumor detection in Digital Breast Tomosynthesis (DBT) is a difficult task due to natural tumor rarity, breast tissue variability, and high resolution. Given the scarcity of abnormal images and the abundance of normal images for this problem, an anomaly detection/localization approach could be well-suited. However, most anomaly localization research in machine learning focuses on non-medical datasets, and we find that these methods fall short when adapted to medical imaging datasets. The problem is alleviated when we solve the task from the image completion perspective, in which the presence of anomalies can be indicated by a discrepancy between the original appearance and its auto-completion conditioned on the surroundings. However, there are often many valid normal completions given the same surroundings, especially in the DBT dataset, making this evaluation criterion less precise. To address such an issue, we consider pluralistic image completion by exploring the distribution of possible completions instead of generating fixed predictions. This is achieved through our novel application of spatial dropout on the completion network during inference time only, which requires no additional training cost and is effective at generating diverse completions. We further propose minimum completion distance (MCD), a new metric for detecting anomalies, thanks to these stochastic completions. We provide theoretical as well as empirical support for the superiority over existing methods of using the proposed method for anomaly localization. On the DBT dataset, our model outperforms other state-of-the-art methods by at least 10% AUROC for pixel-level detection.

Background enhancement in contrast-enhanced spectral mammography (CESM): are there qualitative and quantitative differences between imaging systems?

OBJECTIVE

To evaluate the impact of the digital mammography imaging system on overall background enhancement on recombined contrast-enhanced spectral mammography (CESM) images, the overall background enhancement of two different mammography systems was compared.

METHODS

In a retrospective single-center study, CESM images of n = 129 female patients who underwent CESM between 2016 and 2019 were analyzed independently by two radiologists. Two mammography machines of different manufacturers were compared qualitatively using a Likert-scale from 1 (minimal) to 4 (marked overall background enhancement) and quantitatively by placing a region of interest and measuring the intensity enhancement. Lesion conspicuity was analyzed using a Likert-scale from 1 (lesion not reliably distinguishable) to 5 (excellent lesion conspicuity). A multivariate regression was performed to test for potential biases on the quantitative results.

RESULTS

Significant differences in qualitative background enhancement measurements between machines A and B were observed for both readers (p = 0.003 and p < 0.001). The quantitative evaluation showed significant differences in background enhancement with an average difference of 75.69 (99%-CI [74.37, 77.02]; p < 0.001). Lesion conspicuity was better for machine A for the first and second reader respectively (p = 0.009 and p < 0.001). The factor machine was the only influencing factor (p < 0.001). The factors contrast agent, breast density, age, and menstrual cycle could be excluded as potential biases.

CONCLUSION

Mammography machines seem to significantly influence overall background enhancement qualitatively and quantitatively; thus, an impact on diagnostic accuracy appears possible.

KEY POINTS

• Overall background enhancement on CESM differs between different vendors qualitatively and quantitatively. • Our retrospective single-center study showed consistent results of the qualitative and quantitative data analysis of overall background enhancement. • Lesion conspicuity is higher in cases of lower background enhancement on CESM.

The Landmark Series: The Breast Cancer Burden of the Asian American Population and the Need for Disaggregated Data

The Asian American Pacific Islander (AAPI) population is a heterogeneous group of people from geographically and ethnically distinct regions of the world. Traditionally, these patients have been reported as one large aggregate in the breast cancer literature under the race category of "Asian." A detailed examination of this group shows compelling evidence that breast cancer manifests differently among Asian ethnic subgroups, resulting in overlooked health disparities when these races are grouped together. The AAPI community is the fastest growing ethnic group in the United States, and their incidence of breast cancer is increasing at rates greater than among their non-Asian counterparts. When these patients are disaggregated by race, they show wide variations in breast cancer screening, presentation, treatment, and outcomes. This population often faces additional unique challenges in the health care system due to cultural, social, health literacy, and language barriers, which can contribute to further disparity. Our landmark series aims to showcase the breadth of the breast cancer burden in the AAPI population and highlight the need for disaggregated ethnic data.

Associations of alcohol consumption with breast tissue composition

BACKGROUND

We investigated the associations of alcohol with percentage of epithelium, stroma, fibroglandular tissue (epithelium + stroma), and fat in benign breast biopsy samples.

METHODS

We included 857 cancer-free women with biopsy-confirmed benign breast disease within the Nurses' Health Study (NHS) and NHSII cohorts. Percentage of each tissue was measured on whole slide images using a deep-learning algorithm and then log-transformed. Alcohol consumption (recent and cumulative average) was assessed with semi-quantitative food frequency questionnaires. Regression estimates were adjusted for known breast cancer risk factors. All tests were 2-sided.

RESULTS

Alcohol was inversely associated with % of stroma and fibroglandular tissue (recent ≥ 22 g/day vs. none: stroma: β = - 0.08, 95% Confidence Interval [CI] - 0.13; - 0.03; fibroglandular: β = - 0.08, 95% CI - 0.13; - 0.04; cumulative ≥ 22 g/day vs. none: stroma: β = - 0.08, 95% CI - 0.13; - 0.02; fibroglandular: β = - 0.09, 95% CI - 0.14; - 0.04) and positively associated with fat % (recent ≥ 22 g/day vs. none: β = 0.30, 95% CI 0.03; 0.57; cumulative ≥ 22 g/day vs. none: β = 0.32, 95% CI 0.04; 0.61). In stratified analysis, alcohol consumption was not associated with tissue measures in premenopausal women. In postmenopausal women, cumulative alcohol use was inversely associated with % of stroma and fibroglandular tissue and positively associated with fat % (≥ 22 g/day vs. none: stroma: β = - 0.16, 95% CI - 0.28; - 0.07; fibroglandular: β = - 0.18, 95% CI - 0.28; - 0.07; fat: β = 0.61, 95% CI 0.01; 1.22), with similar results for recent alcohol use.

CONCLUSION

Our findings suggest that alcohol consumption is associated with smaller % of stroma and fibroglandular tissue and a greater % of fat in postmenopausal women. Future studies are warranted to confirm our findings and to elucidate the underlying biological mechanisms.

Development of a breast cancer screening protocol to use automated breast ultrasound in a local setting

Introduction

The sensitivity of mammography screening is lower in women with dense breast. Increasing the efficacy of breast cancer screening have received special attention recently. The automated breast ultrasound (ABUS) shows promising results to complement mammography. Our aim was to expand the existing breast cancer screening protocol with ABUS within a Hungarian pilot project.

Methods

First, we developed a protocol for the screening process focusing on integrating ABUS to the current practice. Consensus among clinical experts was achieved considering information from the literature and the actual opportunities of the hospital. Then we developed a protocol for evaluation that ensures systematic data collection and monitoring of screening with mammography and ABUS. We identified indicators based on international standards and adapted them to local setting. We considered their feasibility from the data source and timeframe perspective. The protocol was developed in a partnership of researchers, clinicians and hospital managers.

Results

The process of screening activity was described in a detailed flowchart. Human and technological resource requirements and communication activities were defined. We listed 23 monitoring indicators to evaluate the screening program and checked the feasibility to calculate these indicators based on local data collection and other sources. Partnership between researchers experienced in planning and evaluating screening programs, interested clinicians, and hospital managers resulted in a locally implementable, evidence-based screening protocol.

Discussion

The experience and knowledge gained on the implementation of the ABUS technology could generate real-world data to support the decision on using the technology at national level.

Perceptions of Breast Cancer Risks Among Women Receiving Mammograph Screening

Importance

Breast density is an independent risk factor for breast cancer. Despite the proliferation of mandated written notifications about breast density following mammography, there is little understanding of how women perceive the relative breast cancer risk associated with breast density.

Objective

To assess women's perception of breast density compared with other breast cancer risks and explore their understanding of risk reduction.

Design, Setting, and Participants

This mixed-methods qualitative study used telephone surveys and semistructured interviews to investigate perceptions about breast cancer risk among a nationally representative, population-based sample of women. Eligible study participants were aged 40 to 76 years, reported having recently undergone mammography, had no history of prior breast cancer, and had heard of breast density. Survey participants who had been informed of their personal breast density were invited for a qualitative interview. Survey administration spanned July 1, 2019, to April 30, 2020, with 2306 women completing the survey. Qualitative interviews were conducted from February 1 to May 30, 2020.

Main Outcomes and Measures

Respondents compared the breast cancer risk associated with breast density with 5 other risk factors. Participants qualitatively described what they thought contributed to breast cancer risk and ways to reduce risk.

Results

Of the 2306 women who completed the survey, 1858 (166 [9%] Asian, 503 [27%] Black, 268 [14%] Hispanic, 792 [43%] White, and 128 [7%] other race or ethnicity; 358 [19%] aged 40-49 years, 906 [49%] aged 50-64 years, and 594 [32%] aged ≥65 years) completed the revised risk perception questions and were included in the analysis. Half of respondents thought breast density to be a greater risk than not having children (957 [52%]), having more than 1 alcoholic drink per day (975 [53%]), or having a prior breast biopsy (867 [48%]). Most respondents felt breast density was a lesser risk than having a first-degree relative with breast cancer (1706 [93%]) or being overweight or obese (1188 [65%]). Of the 61 women who were interviewed, 6 (10%) described breast density as contributing to breast cancer risk, and 43 (70%) emphasized family history as a breast cancer risk factor. Of the interviewed women, 17 (28%) stated they did not know whether it was possible to reduce their breast cancer risk.

Conclusions and Relevance

In this qualitative study of women of breast cancer screening age, family history was perceived as the primary breast cancer risk factor. Most interviewees did not identify breast density as a risk factor and did not feel confident about actions to mitigate breast cancer risk. Comprehensive education about breast cancer risks and prevention strategies is needed.

Therapeutic effects of Crataegus monogyna inhibitors against breast cancer

Breast cancer is a silent killer disorder among women and a serious economic burden in healthcare management. Every 19 s, a woman is diagnosed with breast cancer, and every 74 s, a woman worldwide passes away from the disease. Despite the increase in progressive research, advanced treatment approaches, and preventive measures, breast cancer rates continue to increase. This study provides a combination of data mining, network pharmacology, and docking analysis that surely could revolutionize cancer treatment by exploiting prestigious phytochemicals. Crataegus monogyna is a small, rounded deciduous tree with glossy, deeply lobed leaves and flat sprays of cream flowers, followed by dark red berries in autumn. Various studies demonstrated that C. monogyna is therapeutically effective against breast cancer. However, the particular molecular mechanism is still unknown. This study is credited for locating bioactive substances, metabolic pathways, and target genes for breast cancer treatment. According to the current investigation, which examined compound-target genes-pathway networks, it was found that the bioactive compounds of C. monogyna may operate as a viable solution against breast cancer by altering the target genes implicated in the disease pathogenesis. The expression level of target genes was analyzed using GSE36295 microarray data. Docking analysis and molecular dynamic simulation studies further strengthened the current findings by validating the effective activity of the bioactive compounds against putative target genes. In summary, we propose that six key compounds, luteolin, apigenin, quercetin, kaempferol, ursolic acid, and oleanolic acid, contributed to the development of breast cancer by affecting the MMP9 and PPARG proteins. Integration of network pharmacology and bioinformatics revealed C. monogyna's multitarget pharmacological mechanisms against breast cancer. This study provides convincing evidence that C. monogyna might partially alleviate breast cancer and ultimately lays a foundation for further experimental research on the anti-breast cancer activity of C. monogyna.

Mammography biomarkers of cardiovascular and musculoskeletal health: A review

Breast density (BD) and breast arterial calcifications (BAC) can expand the role of mammography. In premenopause, BD is related to body fat composition: breast adipose tissue and total volume are potential indicators of fat storage in visceral depots, associated with higher risk of cardiovascular disease (CVD). Women with fatty breast have an increased likelihood of hypercholesterolemia. Women without cardiometabolic diseases with higher BD have a lower risk of diabetes mellitus, hypertension, chest pain, and peripheral vascular disease, while those with lower BD are at increased risk of cardiometabolic diseases. BAC, the expression of Monckeberg sclerosis, are associated with CVD risk. Their prevalence, 13 % overall, rises after menopause and is reduced in women aged over 65 receiving hormonal replacement therapy. Due to their distinct pathogenesis, BAC are associated with hypertension but not with other cardiovascular risk factors. Women with BAC have an increased risk of acute myocardial infarction, ischemic stroke, and CVD death; furthermore, moderate to severe BAC load is associated with coronary artery disease. The clinical use of BAC assessment is limited by their time-consuming manual/visual quantification, an issue possibly solved by artificial intelligence-based approaches addressing BAC complex topology as well as their large spectrum of extent and x-ray attenuations. A link between BD, BAC, and osteoporosis has been reported, but data are still inconclusive. Systematic, standardised reporting of BD and BAC should be encouraged.

Comparison of Diagnostic Performance in Mammography Assessment: Radiologist with Reference to Clinical Information Versus Standalone Artificial Intelligence Detection

We compared diagnostic performances between radiologists with reference to clinical information and standalone artificial intelligence (AI) detection of breast cancer on digital mammography. This study included 392 women (average age: 57.3 ± 12.1 years, range: 30−94 years) diagnosed with malignancy between January 2010 and June 2021 who underwent digital mammography prior to biopsy. Two radiologists assessed mammographic findings based on clinical symptoms and prior mammography. All mammographies were analyzed via AI. Breast cancer detection performance was compared between radiologists and AI based on how the lesion location was concordant between each analysis method (radiologists or AI) and pathological results. Kappa coefficient was used to measure the concordance between radiologists or AI analysis and pathology results. Binominal logistic regression analysis was performed to identify factors influencing the concordance between radiologists’ analysis and pathology results. Overall, the concordance was higher in radiologists’ diagnosis than on AI analysis (kappa coefficient: 0.819 vs. 0.698). Impact of prior mammography (odds ratio (OR): 8.55, p < 0.001), clinical symptom (OR: 5.49, p < 0.001), and fatty breast density (OR: 5.18, p = 0.008) were important factors contributing to the concordance of lesion location between radiologists’ diagnosis and pathology results.

Deep learning of longitudinal mammogram examinations for breast cancer risk prediction

Information in digital mammogram images has been shown to be associated with the risk of developing breast cancer. Longitudinal breast cancer screening mammogram examinations may carry spatiotemporal information that can enhance breast cancer risk prediction. No deep learning models have been designed to capture such spatiotemporal information over multiple examinations to predict the risk. In this study, we propose a novel deep learning structure, LRP-NET, to capture the spatiotemporal changes of breast tissue over multiple negative/benign screening mammogram examinations to predict near-term breast cancer risk in a case-control setting. Specifically, LRP-NET is designed based on clinical knowledge to capture the imaging changes of bilateral breast tissue over four sequential mammogram examinations. We evaluate our proposed model with two ablation studies and compare it to three models/settings, including 1) a "loose" model without explicitly capturing the spatiotemporal changes over longitudinal examinations, 2) LRP-NET but using a varying number (i.e., 1 and 3) of sequential examinations, and 3) a previous model that uses only a single mammogram examination. On a case-control cohort of 200 patients, each with four examinations, our experiments on a total of 3200 images show that the LRP-NET model outperforms the compared models/settings.

Over-the-Counter Breast Cancer Classification Using Machine Learning and Patient Registration Records

This study aims to determine the feasibility of machine learning (ML) and patient registration record to be utilised to develop an over-the-counter (OTC) screening model for breast cancer risk estimation. Data were retrospectively collected from women who came to the Hospital Universiti Sains Malaysia, Malaysia for breast-related problems. Eight ML models were used: k-nearest neighbour (kNN), elastic-net logistic regression, multivariate adaptive regression splines, artificial neural network, partial least square, random forest, support vector machine (SVM), and extreme gradient boosting. Features utilised for the development of the screening models were limited to information in the patient registration form. The final model was evaluated in terms of performance across a mammographic density. Additionally, the feature importance of the final model was assessed using the model agnostic approach. kNN had the highest Youden J index, precision, and PR-AUC, while SVM had the highest F2 score. The kNN model was selected as the final model. The model had a balanced performance in terms of sensitivity, specificity, and PR-AUC across the mammographic density groups. The most important feature was the age at examination. In conclusion, this study showed that ML and patient registration information are feasible to be used as the OTC screening model for breast cancer.

Autoantibodies as biomarkers for breast cancer diagnosis and prognosis

Breast cancer is the most common cancer in women worldwide and is a substantial public health problem. Screening for breast cancer mainly relies on mammography, which leads to false positives and missed diagnoses and is especially non-sensitive for patients with small tumors and dense breasts. The prognosis of breast cancer is mainly classified by tumor, node, and metastasis (TNM) staging, but this method does not consider the molecular characteristics of the tumor. As the product of the immune response to tumor-associated antigens, autoantibodies can be detected in peripheral blood and can be used as noninvasive, presymptomatic, and low-cost biomarkers. Therefore, autoantibodies can provide a possible supplementary method for breast cancer screening and prognosis classification. This article introduces the methods used to detect peripheral blood autoantibodies and the research progress in the screening and prognosis of breast cancer made in recent years to provide a potential direction for the examination and treatment of breast cancer.

A hybrid breast cancer classification algorithm based on meta-learning and artificial neural networks

The incidence of breast cancer in women has surpassed that of lung cancer as the world’s leading new cancer case. Regular screening and measures become an effective way to prevent breast cancer and also provide a good foundation for later treatment. Women should receive regular checkups in the hospital after reaching a certain age. The use of computer-aided technology can improve the accuracy and efficiency of physicians’ decision-making. Data pre-processing is required before data analysis, and 16 features are selected using a correlation-based feature selection method. In this paper, meta-learning and Artificial Neural Networks (ANN) are combined to create a hybrid algorithm. The proposed hybrid algorithm for predicting breast cancer was attempted to achieve 98.74% accuracy and 98.02% F1-score by creating a combination of various meta-learning models whose output was used as input features for creating ANN models. Therefore, the hybrid algorithm proposed in this paper can obtain better prediction results than a single model.

Comprehensive analysis of prognostic significance of cadherin (CDH) gene family in breast cancer

Breast cancer is one of the leading deaths in all kinds of malignancies; therefore, it is important for early detection. At the primary tumor site, tumor cells could take on mesenchymal properties, termed the epithelial-to-mesenchymal transition (EMT). This process is partly regulated by members of the cadherin (CDH) family of genes, and it is an essential step in the formation of metastases. There has been a lot of study of the roles of some of the CDH family genes in cancer; however, a holistic approach examining the roles of distinct CDH family genes in the development of breast cancer remains largely unexplored. In the present study, we used a bioinformatics approach to examine expression profiles of CDH family genes using the Oncomine, Gene Expression Profiling Interactive Analysis 2 (GEPIA2), cBioPortal, MetaCore, and Tumor IMmune Estimation Resource (TIMER) platforms. We revealed that CDH1/2/4/11/12/13 messenger (m)RNA levels are overexpressed in breast cancer cells compared to normal cells and were correlated with poor prognoses in breast cancer patients’ distant metastasis-free survival. An enrichment analysis showed that high expressions of CDH1/2/4/11/12/13 were significantly correlated with cell adhesion, the extracellular matrix remodeling process, the EMT, WNT/beta-catenin, and interleukin-mediated immune responses. Collectively, CDH1/2/4/11/12/13 are thought to be potential biomarkers for breast cancer progression and metastasis.

Deep Learning Models for Automated Assessment of Breast Density Using Multiple Mammographic Image Types

Simple Summary

The DL model predictions in automated breast density assessment were independent of the imaging technologies, moderately or substantially agreed with the clinical reader density values, and had improved performance as compared to inclusion of commercial software values.

Abstract

Recently, convolutional neural network (CNN) models have been proposed to automate the assessment of breast density, breast cancer detection or risk stratification using single image modality. However, analysis of breast density using multiple mammographic types using clinical data has not been reported in the literature. In this study, we investigate pre-trained EfficientNetB0 deep learning (DL) models for automated assessment of breast density using multiple mammographic types with and without clinical information to improve reliability and versatility of reporting. 120,000 for-processing and for-presentation full-field digital mammograms (FFDM), digital breast tomosynthesis (DBT), and synthesized 2D images from 5032 women were retrospectively analyzed. Each participant underwent up to 3 screening examinations and completed a questionnaire at each screening encounter. Pre-trained EfficientNetB0 DL models with or without clinical history were optimized. The DL models were evaluated using BI-RADS (fatty, scattered fibroglandular densities, heterogeneously dense, or extremely dense) versus binary (non-dense or dense) density classification. Pre-trained EfficientNetB0 model performances were compared using inter-observer and commercial software (Volpara) variabilities. Results show that the average Fleiss’ Kappa score between-observers ranged from 0.31–0.50 and 0.55–0.69 for the BI-RADS and binary classifications, respectively, showing higher uncertainty among experts. Volpara-observer agreement was 0.33 and 0.54 for BI-RADS and binary classifications, respectively, showing fair to moderate agreement. However, our proposed pre-trained EfficientNetB0 DL models-observer agreement was 0.61–0.66 and 0.70–0.75 for BI-RADS and binary classifications, respectively, showing moderate to substantial agreement. Overall results show that the best breast density estimation was achieved using for-presentation FFDM and DBT images without added clinical information. Pre-trained EfficientNetB0 model can automatically assess breast density from any images modality type, with the best results obtained from for-presentation FFDM and DBT, which are the most common image archived in clinical practice.

Radiogenomics, Breast Cancer Diagnosis and Characterization: Current Status and Future Directions

Breast cancer (BC) is a heterogeneous disease, affecting millions of women every year. Early diagnosis is crucial to increasing survival. The clinical workup of BC diagnosis involves diagnostic imaging and bioptic characterization. In recent years, technical advances in image processing allowed for the application of advanced image analysis (radiomics) to clinical data. Furthermore, -omics technologies showed their potential in the characterization of BC. Combining information provided by radiomics with -omics data can be important to personalize diagnostic and therapeutic work up in a clinical context for the benefit of the patient. In this review, we analyzed the recent literature, highlighting innovative approaches to combine imaging and biochemical/biological data, with the aim of identifying recent advances in radiogenomics applied to BC. The results of radiogenomic studies are encouraging approaches in a clinical setting. Despite this, as radiogenomics is an emerging area, the optimal approach has to face technical limitations and needs to be applied to large cohorts including all the expression profiles currently available for BC subtypes (e.g., besides markers from transcriptomics, proteomics and miRNomics, also other non-coding RNA profiles).

Mouse Mammary Gland Whole Mount Density Assessment across Different Morphologies Using a Bifurcated Program for Image Processing

Mammographic density is associated with increased breast cancer risk. Conventional visual assessment of murine mouse models does not include quantified total density analysis. A bifurcated method was sufficient to obtain relative density scores on a broad range of two-dimensional whole mount images that contained both normal and abnormal findings. Image processing techniques, including a ridge operator and a gaussian denoising method, were used to isolate background away from mammary epithelium and use mean pixel intensity to represent mammary density on genetically engineered mouse models for breast cancer in mice 4 to 29 months of age. The bifurcated method allowed for application of an optimal image processing approach for the structural elements present in the whole mount images. Gaussian denoising was the optimal approach when more dense lobular growth and tertiary branching dominate and a ridge operator when epithelial growth was more sparse and secondary branching was the more dominant structural feature. The two processing approaches were combined in a single experimental flow program using an initial image density measurement as the decision point between the two approaches. Higher density was associated with lobular growth, tertiary branching, fibrotic stroma, and presence of cancer. The significance of the study is development of a readily accessible program for digital assessment of mammary gland whole mount density across a range of mammary gland morphologies.

Evaluation of an artificial intelligence support system for breast cancer screening in Chinese people based on mammogram

BACKGROUND

To evaluate the diagnostic performance of radiologists on breast cancer with or without artificial intelligence (AI) support.

METHODS

A retrospective study was performed. In total, 643 mammograms (average age: 54 years; female: 100%; cancer: 62.05%) were randomly allocated into two groups. Seventy-five percent of mammograms in each group were randomly selected for assessment by two independent radiologists, and the rest were read once. Half of the 71 radiologists could read mammograms with AI support, and the other half could not. Sensitivity, specificity, Youden's index, agreement rate, Kappa value, the area under the receiver operating characteristic curve (AUC) and the reading time of radiologists in each group were analyzed.

RESULTS

The average AUC was higher if the AI support system was used (unaided: 0.84; with AI support: 0.91; p < 0.01). The average sensitivity increased from 84.77% to 95.07% with AI support (p < 0.01), but the average specificity decreased (p = 0.07). Youden's index, agreement rate and Kappa value were larger in the group with AI support, and the average reading time was shorter (p < 0.01).

CONCLUSIONS

The AI support system might contribute to enhancing the diagnostic performance (e.g., higher sensitivity and AUC) of radiologists. In the future, the AI algorithm should be improved, and prospective studies should be conducted.

Deep learning applied to breast imaging classification and segmentation with human expert intervention

PURPOSE

Automatic classification and segmentation of tumors in breast ultrasound images enables better diagnosis and planning treatment strategies for breast cancer patients.

METHODS

We collected 953 breast ultrasound images from two open-source datasets and classified them with help of an expert radiologist according to BI-RADS criteria. The data was split into normal, benign and malignant classes. We then used machine learning to develop classification and segmentation algorithms.

RESULTS

We found 3.92% of the images across the open-source datasets had erroneous classifications. Post-radiologist intervention, three algorithms were developed based on the classification categories. Classification algorithms distinguished images with healthy breast tissue from those with abnormal tissue with 96% accuracy, and distinguished benign from malignant images with 85% accuracy. Both algorithms generated robust F1 and AUROC metrics. Finally, the masses within images were segmented with an 80.31% DICE score.

CONCLUSIONS

Our work illustrates the potential of deep learning algorithms to improve the accuracy of breast ultrasound assessments and to facilitate automated assessments.

CUX2/KDM5B/SOX17 Axis Affects the Occurrence and Development of Breast Cancer

OBJECTIVE

Abnormal expression of CUT-like homeobox 2 gene (CUX2) has been highlighted as potential clinical biomarkers in human cancers. Notably, the function of CUX2 has been less elucidated in breast cancer (BC). We focused on the role of the CUX2 in tumorigenesis and progression of BC with the involvement of the lysine demethylase 5B (KDM5B)/sex determining region Y-box 17 (SOX17) axis.

METHODS

CUX2, KDM5B, and SOX17 expression levels in BC tissues and cells were tested by reverse transcription quantitative PCR and Western blotting. Later, the effects of CUX2, KDM5B, and SOX17 on the malignant behaviors of MDA-MB-231 and MCF-7 cells were analyzed by CCK-8, colony formation, and Transwell assays in vitro. The interactions of CUX2, KDM5B, and SOX17 were validated by online website prediction, ChIP assay, and dual luciferase reporter gene assay. The subcutaneous tumorigenesis in nude mice was conducted to observe the roles of CUX2, KDM5B, and SOX17 in BC tumor growth in vivo.

RESULTS

CUX2 and KDM5B were highly expressed while SOX17 had low expression in BC. Inhibition of CUX2 suppressed BC cell malignant phenotypes. CUX2 promoted KDM5B expression through transcriptional activation, enabling its high expression in BC. KDM5B inhibited SOX17 expression through histone demethylation. Overexpression of KDM5B or downregulation of SOX17 reversed the inhibitory effect of CUX2 downregulation on the malignant behaviors of BC cells. Inhibition of CUX2 impeded BC cell growth in vivo through the KDM5B/SOX17 axis.

CONCLUSION

This study highlights that suppression of CUX2 inhibits KDM5B to repress tumorigenesis and progression of BC through overexpressing SOX17.