Early Detection Of Breast Cancer Research Articles

Discovery of Plasma Lipids as Potential Biomarkers Distinguishing Breast Cancer Patients from Healthy Controls

The development of a sensitive and specific blood test for the early detection of breast cancer is crucial to improve screening and patient outcomes. Existing methods, such as mammography, have limitations, necessitating the exploration of alternative approaches, including circulating factors. Using 598 prospectively collected blood samples, a multivariate plasma-derived lipid biomarker signature was developed that can distinguish healthy control individuals from those with breast cancer. Liquid chromatography with high-resolution and tandem mass spectrometry (LC-MS/MS) was employed to identify lipids for both extracellular vesicle-derived and plasma-derived signatures. For each dataset, we identified a signature of 20 lipids using a robust, statistically rigorous feature selection algorithm based on random forest feature importance applied to cross-validated training samples. Using an ensemble of machine learning models, the plasma 20-lipid signature generated an area under the curve (AUC) of 0.95, sensitivity of 0.91, and specificity of 0.79. The results from this study indicate that lipids extracted from plasma can be used as target analytes in the development of assays to detect the presence of early-stage breast cancer.

Referral and disease patterns of patients treated at the Nicosia General Hospital Breast Centre

Abstract Background Cyprus has one of the lowest mortality rates from breast cancer in Europe. The reasons behind this have not been investigated but it is speculated that the ease of access to Breast Centers in combination with an increased public awareness play a role. The aim of this study was to investigate referral and disease presentation patterns of patients treated at the Nicosia General Hospital (NGH) Breast Centre and define opportunities for effective public health interventions. Methods A retrospective study of all patients (585) with in situ or invasive breast cancer treated at the NGH Breast Centre between February 2017 and November 2023 was conducted. Data on referral sources, symptoms, and cancer stage at the time of diagnosis were analyzed using Stata 18. Results Most patients were referrals from the national population screening program (42.2%), followed by self-referrals (40.6%), and General Practitioner (GP)/or other specialist referrals (17.2%). Half of the patients (50.3%) were asymptomatic at diagnosis. In addition, 8% of the patients diagnosed with invasive breast cancer were Stage I, 65% Stage II, and 27% Stage III. Conclusions Our study provides important insights forming the basis of public health activities for the early detection of breast cancer based on the fact that 20% of newly diagnosed breast cancer cases in Cyprus receive treatment in NGH Breast Center. Although adherence to the national population screening program does not meet desired expectations, a significant proportion of breast cancers were detected via this program highlighting its effectiveness in early detection of asymptomatic cancers. A large proportion of patients were self-referred underlining the high degree of breast cancer awareness in Cyprus but also the media-induced anxiety which leads to early investigations. Key messages • Screening program and breast cancer awareness campaigns are effective in detecting breast cancers early. • These activities should be sustained to further reduce breast cancer mortality rate.

Automated Breast Ultrasound With Remote Reading for Primary Breast Cancer Screening: A Prospective Study Involving 46 Community Health Centers in China.

Background: China has faced barriers to implementation of a population-based mammographic screening program. Breast ultrasound provides an alternative screening modality to mammography in low-resource settings. Objective: To evaluate the performance of ABUS with remote reading as the primary screening modality for breast cancer. Methods: This prospective study enrolled asymptomatic women 35-69 years old from 46 community health centers across 18 provinces representing all six regions of China from January 2021 to December 2021. Participants underwent screening ABUS as the sole breast cancer screening modality, with images acquired by a technologist at the community health center. The 3D volumetric data were transferred via cloud-based software to a single remote reading center, where examinations were interpreted independently in batches by two subspecialized breast radiologists using BI-RADS; a third radiologist at the remote reader center resolved discrepancies. Diagnostic reports were returned to the community centers, and patients sought follow-up care at local hospitals. The reference standard incorporated a combination of histopathology and 24-month follow-up. Outcomes measures included cancer detection rate, abnormal interpretation rate (AIR), sensitivity, specificity, biopsy rate, and PPV. Results: The final analysis included 5978 enrolled participants (median age, 46 years [IQR 40-52 years]) who underwent screening ABUS at the community health centers with subsequent remote reading. A total of 24 ABUS-detected cancers and two interval cancers were diagnosed. The cancer detection rate was 4.0 per 1000 women (95% CI: 2.7-5.9), and the AIR was 11.9% (95% CI: 11.1-12.7). A total of 95.8% (23/24) of ABUS-detected cancers were invasive. The 23 invasive cancers had a median diameter of 10.0 mm; 73.9% (17/23) were node-negative. Sensitivity was 92.3% (95% CI: 75.9-97.9), and specificity was 88.4% (95% CI: 87.6-89.2). The biopsy rate was 1.7% (95% CI: 1.4-2.0), and the PPV of biopsy was 24.0% (95% CI: 16.7-33.2). Conclusion: ABUS screening with remote reading met benchmark performance for cancer detection in comparison to mammography, with infrequent interval cancers. Clinical Impact: ABUS with remote reading holds promise in enhancing access to breast cancer screening and early detection in low-resource settings or underserved regions where mammographic screening is not established.

Breast Cancer Detection System Using Machine Learning

AI is transforming the medical field, especially in early detection and categorization of breast cancer. Deep learning models like CNNI-BCC are displaying impressive capabilities in examining MRI images to precisely recognize different types of breast cancer. This advancement in technology has significant implications for enhancing patient outcomes. By utilizing AI, healthcare professionals can now receive more accurate and prompt diagnoses. CNNI- BCC's exceptional accuracy in analyzing MRI images allows for earlier detection of breast cancer, which is crucial for successful treatment. This technology enables doctors to identify potential abnormalities that may have been missed by traditional methods, leading to more effective interventions.

How social class shapes breast cancer risk perspectives and prevention practices of Australian midlife women: a qualitative study using the concept of ‘breast cancer candidacy’

BackgroundThe increasing incidence of breast cancer and disease burden is a significant public health concern. While 30% of breast cancers could be prevented through addressing modifiable risk factors, misconceptions among women about breast cancer risks hamper primary prevention. In the absence of primary prevention, secondary prevention such as mammography increases the early detection of breast cancer and improves health outcomes. However, current population-level screening rates indicate secondary prevention is suboptimal. More effective public health efforts to improve breast cancer prevention are required. Given breast cancer is socially patterned, this work explores how social class impacts women’s breast cancer prevention practices. This study uses the concepts of lay epidemiology and candidacy as a mechanism to understand women’s breast cancer risk perspectives. It engages Bourdieu’s relational social class theory to unpack how women’s social, cultural, and structured life contexts shape these perspectives and their considerations regarding primary and secondary prevention.MethodsIn this qualitative study 43 Australian midlife women (aged 45–64 years), were interviewed to explore their understandings of breast cancer risks, how they perceived their own risk, and how this shaped their prevention behaviours. A theory-informed thematic analysis applying Bourdieu’s concepts of habitus, capital, and fields to understand how women’s social class positions shapes risk perspectives and prevention practices was conducted.ResultsThis social class analysis showed differences in how women engage in breast cancer discourse, consider risks, and participate in breast cancer prevention. Middle-class women prioritise health promoting practices and were more likely than working-class and affluent women to attend mammography screening. Working-class women experience structural factors, like low income, stress and difficult life circumstances, which hamper primary prevention practices and for some screening is not considered or prioritised, and their decisions not to screen are less active. Affluent women often do not consider themselves at-risk due to their healthier ‘lifestyles. ’They suggest that this, and their knowledge of screening benefits and harms allows them to make informed decisions not to screen.ConclusionsWomen interpret and understand breast cancer risks differently and enact prevention practices within the parameters afforded by their social class positions. These findings are useful to inform improved public health approaches regarding both modifiable breast cancer risks and increasing mammography screening. To improve equity in breast cancer prevention efforts, such approaches must respond to limitations based on social class and address structural factors that impact prevention practices.

A New Computer-Aided Diagnosis System for Breast Cancer Detection from Thermograms Using Metaheuristic Algorithms and Explainable AI

Advances in the early detection of breast cancer and treatment improvements have significantly increased survival rates. Traditional screening methods, including mammography, MRI, ultrasound, and biopsies, while effective, often come with high costs and risks. Recently, thermal imaging has gained attention due to its minimal risks compared to mammography, although it is not widely adopted as a primary detection tool since it depends on identifying skin temperature changes and lesions. The advent of machine learning (ML) and deep learning (DL) has enhanced the effectiveness of breast cancer detection and diagnosis using this technology. In this study, a novel interpretable computer aided diagnosis (CAD) system for breast cancer detection is proposed, leveraging Explainable Artificial Intelligence (XAI) throughout its various phases. To achieve these goals, we proposed a new multi-objective optimization approach named the Hybrid Particle Swarm Optimization algorithm (HPSO) and Hybrid Spider Monkey Optimization algorithm (HSMO). These algorithms simultaneously combined the continuous and binary representations of PSO and SMO to effectively manage trade-offs between accuracy, feature selection, and hyperparameter tuning. We evaluated several CAD models and investigated the impact of handcrafted methods such as Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), Gabor Filters, and Edge Detection. We further shed light on the effect of feature selection and optimization on feature attribution and model decision-making processes using the SHapley Additive exPlanations (SHAP) framework, with a particular emphasis on cancer classification using the DMR-IR dataset. The results of our experiments demonstrate in all trials that the performance of the model is improved. With HSMO, our models achieved an accuracy of 98.27% and F1-score of 98.15% while selecting only 25.78% of the HOG features. This approach not only boosts the performance of CAD models but also ensures comprehensive interpretability. This method emerges as a promising and transparent tool for early breast cancer diagnosis.

Abbreviated Breast MRI as a Supplement to Mammography in Family Risk History of Breast Cancer within the Croatian National Breast Screening Program.

To evaluate the diagnostic performance of abbreviated breast MRI compared with mammography in women with a family history of breast cancer included in the Croatian National Breast Screening Program. 178 women with a family history of breast cancer aged 50 to 69 underwent abbreviated breast MRI and mammography. Radiological findings for each method were categorized according to the BI-RADS classification. The gold standard for assessing the diagnostic accuracy of breast MRI and mammography, in terms of suspicious BI-RADS 4 and BI-RADS 5 findings, was the histopathological diagnosis. Performance measures, including cancer detection rates, specificity, sensitivity, and positive and negative predictive values, were calculated for both imaging methods. Twelve new cases of breast cancer were detected, with seven (58.3%) identified only by abbreviated breast MRI, four (33.3%) detected by both mammography and breast MRI, and one (8.3%) diagnosed only by mammography. Diagnostic accuracy parameters for abbreviated breast MRI were 91.67% sensitivity, 94.58% specificity, 55.0% positive predictive value (PPV), and 99.37% negative predictive value (NPV), while for mammography, the corresponding values were 41.67%, 96.39%, 45.46%, and 95.81%, respectively. Abbreviated breast MRI is a useful supplement to screening mammography in women with a family history of breast cancer. Considering the results of the conducted research, it is recommended to assess whether women with a family history of breast cancer have an increased risk and subsequently provide annual abbreviated breast MRI in addition to mammography for early detection of breast cancer.

Machine Learning Empowered Breast Cancer Diagnosis: Insights from Coimbra Dataset

Aim: The aim of this work is to succinctly communicate the key aspects of a research study on breast cancer. This includes highlighting the global impact and prevalence of breast cancer, emphasizing the challenges of early diagnosis, discussing the potential of technological advancements, and showcasing the role of machine learning algorithms in the context of liver cancer diagnosis. Background: Cancer, notably breast cancer, represents a global health challenge, claiming a significant toll with 12.5% of new cancer cases annually. The prevalence of breast cancer among women worldwide is alarming, resulting in 2.26 million incidents and the unfortunate loss of 685,000 lives. Objective: This article focuses on the critical aspect of early breast cancer diagnosis, acknowledging its heightened difficulty in developing nations compared to developed counterparts. The potential for advancements in technology to serve as a beacon of hope lies in early identification and timely treatment, offering salvation to numerous women and significantly elevating survival chances. Method: In this intricate landscape, machine learning algorithms, particularly in diagnosing liver cancer at its nascent stages, emerge as instrumental tools. The study employs the latest Coimbra dataset, encompassing nine key attributes and a binary classification attribute, with values 1 and 2 signifying benign and malignant cases, respectively. Result: Supervised machine learning algorithms, including Bayes net, multilayer perceptron, IBK, random committee, and random tree, are meticulously applied. Certain models exhibit superior accuracy, precision, recall, and performance, positioning them as promising cornerstones for breast cancer analysis. Conclusion: This structured abstract highlights the urgent need for effective screening and prevention strategies, emphasizing the potential of advanced technology and machine learning algorithms to play a pivotal role in the early detection and analysis of breast cancer, offering hope for improved outcomes and survival rates.

Enhancing awareness of breast self-examination and pap smears for early detection of breast and cervical cancer

Reproductive health problems, especially breast cancer and cervical cancer are the most common cancers diagnosed in women in Indonesia. Currently, several testing methods to detect breast and cervical cancer early, such as breast self-examination (BSE) and pap smears, have been used to reduce cancer risk factors. However, these two methods are still not widely used. Outreach activities have been proven to increase knowledge about the importance of BSE and pap smears in women. This community service program aims to increase knowledge about the causes and symptoms of breast and cervical cancer, as well as early detection using BSE and pap smears. This service activity plan consisted of preparation, implementation, and evaluation stages. The method used consists of delivering interactive material, discussion, and practice. This activity was attended by 38 participants, consisting of women from PKK cadres in the Andir area, Ciroyom District, the majority of whom were over 35 years old. Based on the results of the post-test, this community service activity was successful in increasing participants' knowledge, attitudes, and behavior towards breast and cervical cancer as well as the early detection methods of BSE and pap smears.

Towards Early Breast Cancer Detection: A Deep Learning Approach

Towards Early Breast Cancer Detection: A Deep Learning Approach

Conscious Techniques Affected the Adolescent’s Ability In Early Breast Cancer Detection

Breast cancer is a malignant tumor and grows in the breast tissue resulting in death. Breast cancer prevention by means of breast self-examination. This study aimed to determine the effect of video media on conscious techniques with a combination of demonstrations on the practical ability of adolescents in early detection of breast cancer. The method used a quasi-experimental design, non-equivalent control group design with a population of 279 and a sample of 40 respondents with inclusion criteria of young women, aged 16 years as students of SMA Negeri 1 Tarik Sidoarjo. The data collection measured skills in 2 groups of respondents. The experimental intervention group used videos and demonstrations, while the intervention control group used lectures and leaflets. The intervention was done in 1 day, the posttest was conducted 7 days after the intervention. The results showed that the average value of the pretest posttest of the two groups increased. The results of the statistical test paired T-Test obtained a significant value of p-value = (0.000 < 0.05) meant that there was an effect of providing video media with conscious techniques with a combination of demonstrations on the practical ability of adolescents in early detection of breast cancer. Educators should develop programs to improve skills for early detection of breast cancer, in collaboration with health workers through health promotion so that all students are aware of health.

Advanced deep learning strategies for breast cancer image analysis

One of the leading causes of cancer-related deaths in women is breast cancer (BC). Patients' chances of survival are improved when this ailment is diagnosed in a timely manner and suitable treatments are prescribed. Thus, the chance of survival increases with the early detection of BC. Deep learning neural networks have garnered significant attention in recent years for use in BC screening, identification, and classification. Even though some encouraging results have surfaced, more improvement and confirmation are necessary. In this regard, the creation and comparison of many deep learning approaches for the early identification and classification of BC from mammography pictures is the main focus of our research. In this study, Innovative deep learning methods are developed by freezing the first 40 layers an dropping the 43 layers of MobileNetV2, InceptionV3 and DenseNet121 pretrained models, this approach was reinforced by using Data augmentation techniques. Our suggested methodologies are tested through simulations on MIAS dataset and SA private dataset. The three modified models achieved very high classification accuracies, specifically the modified DenseNet121 model, which reached 99,1% on MIAS dataset and 98,8% on SA dataset.

Economic impact of screening on postdiagnosis work productivity in Japanese women with breast cancer: a life-table modeling approach.

In Japan, biennial mammography screening has been recommended for the early detection of breast cancer (BC) in women aged 40years or above since 2004 by the Ministry of Health, Labor and Welfare. The purpose of this study is to estimate the economic impact of BC screening on work productivity, using a new measure called the productivity-adjusted life-year (PALY). We used a dynamic life table modeling approach to estimate the work productivity of female patients aged 40-64years diagnosed with BC in 2019 over the year of diagnosis and the subsequent 5years. Changes in life-years, PALYs, and gross domestic product (GDP) were assessed by changing the screening detection rate from the current (34.2%) to an ideal (100%) percentage. Each input for modeling was obtained from the most recent public database available. BC patients were estimated to lose 1903 in life-years, 3596 in PALYs, and US$281 million in GDP at the current screening detection rate compared with the ideal detection rate. On the other hand, the following gains are expected when the current screening detection rate was increased to 40-80%; life-years gain; 168-1325, PALYs gain; 317-2503, GDP gain: US$25-196 million. This study has used modeling to show that detecting BC without screening is associated with a lower work productivity and an economic and life-years loss. Encouraging BC screening may be beneficial to maintaining work productivity after diagnosis.

A comparative performance assessment of artificial intelligence based classifiers and optimized feature reduction technique for breast cancer diagnosis

Breast cancer (BC) is a catastrophic global health concern that causes numerous fatalities worldwide. Early detection of breast cancer may mitigate death rates; however, the prevailing diagnostic procedure for the malignancy necessitates numerous multifaceted laboratory tests that must be performed by medical professionals. In this article machine learning, a branch of Artificial Intelligence (AI), has been employed to improve cancer diagnosis, prognoses and survival rates while reducing the vulnerability of humans. Support Vector Machine (SVM), K-nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF) and Grey Wolf Optimizer (GWO) are implemented to prognosticate breast cancer. Comprehensive insights into the efficacy of these approaches for breast cancer prognosis are provided by the performance assessment that is accomplished using the confusion matrix, Receiver Operating Characteristic (ROC) curves and parallel coordinate plots. Both UCI (University of California Irvine) and SEER (Surveillance, Epidemiology and End Results) datasets have been utilized to confirm the investigation's findings and ensure their generalizability across diverse data sources. The results conclusively demonstrate that SVM is the cohort's most accurate classifier. With a stupendous accuracy rate of 99.1 %, the GWO-SVM compares favorably to all other algorithms. Furthermore, feature reduction approaches such as Minimum Redundancy Maximum Relevance (mRMR), ReliefF and Principal Component Analysis (PCA) are utilized. ReliefF has demonstrated exceptional effectiveness with a maximum accuracy of 98.2 %.

Single-cell transcriptome analysis reveals immune microenvironment changes and insights into the transition from DCIS to IDC with associated prognostic genes

BackgroundDuctal carcinoma in situ (DCIS) of the breast is an early stage of breast cancer, and preventing its progression to invasive ductal carcinoma (IDC) is crucial for the early detection and treatment of breast cancer. Although single-cell transcriptome analysis technology has been widely used in breast cancer research, the biological mechanisms underlying the transition from DCIS to IDC remain poorly understood.ResultsWe identified eight cell types through cell annotation, finding significant differences in T cell proportions between DCIS and IDC. Using this as a basis, we performed pseudotime analysis on T cell subpopulations, revealing that differentially expressed genes primarily regulate immune cell migration and modulation. By intersecting WGCNA results of T cells highly correlated with the subtypes and the differentially expressed genes, we identified six key genes: FGFBP2, GNLY, KLRD1, TYROBP, PRF1, and NKG7. Excluding PRF1, the other five genes were significantly associated with overall survival in breast cancer, highlighting their potential as prognostic biomarkers.ConclusionsWe identified immune cells that may play a role in the progression from DCIS to IDC and uncovered five key genes that can serve as prognostic markers for breast cancer. These findings provide insights into the mechanisms underlying the transition from DCIS to IDC, offering valuable perspectives for future research. Additionally, our results contribute to a better understanding of the biological processes involved in breast cancer progression.

Social determinants of breast cancer screening: a multilevel analysis of proximal and distal factors related to the practice of mammography.

Mammography is crucial for early breast cancer detection. In Latin America, Argentina faces a significant breast cancer burden, with varying mammography rates. The social factors influencing mammography practices remain unclear. This study aimed to identify the proximal and distal social determinants of this practice among Argentinean women using a multilevel approach. This nationwide cross-sectional study included 4,924 women aged 50-70 participating in the 2018 National Risk Factor Survey of Argentina. Two-level logistic models were used to estimate measures of association (ORs) between timely mammography practice (within the last 2 years) and selected covariates (sociodemographics, proximal environment, and distal-level variables). The intraclass correlation coefficient (ICC) and proportional change in variance (PCV) were calculated. 62.8% of women underwent timely mammography. Age (OR=0.96; 95%CI 0.94-0.97), health insurance (OR=2.22; 95%CI 1.87-2.63), education (OR=2.1; 95%CI 1.74-2.64), and income (OR=1.56; 95%CI 1.23-1.97) were associated with mammography practice. Women in non-marital (OR=0.61; 95%CI 0.52-0.72) or larger households (OR=0.61; 95%CI 0.51-0.63) were less likely to have timely mammograph; living in a larger city was positively associated (OR=1.28; 95%CI 1.12-1.46). Women in provinces with higher physician density (OR=1.06; 95%CI 1.01-1.11) and lower maternal mortality ratio (OR=0.9; 95%CI 0.87-0.96) had higher chances of timely mammography. The ICC and PCV suggested that the explored healthcare indicators largely explained the macro-contextual effect. Multilevel factors influenced mammography practices in Argentina. The results highlight disparities linked to sociodemographic characteristics and healthcare resources. Efforts to address social inequalities in breast cancer screening must consider multilevel determinants, including in healthcare settings.

B-352 Effects of COVID-19 on Breast Cancer Positivity: A Retrospective and Predictive Study

Abstract Background The COVID-19 pandemic has had a significant impact on the healthcare system, resulting in overall reduction in access to healthcare services, including the suspension of screening programs and postponement of surgeries and medical treatments. The aim of this study was to evaluate the effects of the pandemic on breast cancer positivity from cases received at a laboratory in São Paulo, Brazil. Methods A retrospective analysis of a database from a pathology laboratory in São Paulo, Brazil, was performed during the 2017-2023 period and classified as following: pre-pandemic (January 2017-February 2020), pandemic (March 2020 - April-2022), and post-pandemic (May 2022-December 2023). Cases were classified as negative or positive for malignancies. Positive anatomopathological examinations (breast biopsies and surgical specimens) considered primary, metastatic mesenchymal or endothelial malignancies. The Mann-Kendall test was used to evaluate the trend, and the PACF (Partial Autocorrelation Function) correlogram was used to analyze historical memory for definition of the autoregressive model. The augmented Dickey-Fuller hypothesis test was applied to analyze whether the series is stationary or not, and the first differences test was applied. The ARIMA model was used for the estimates (1,1,1). Statistics were performed using the gretl software and pymannkendall library in the Python language. Results A total of 74,973 samples were evaluated during the 2017-2023 period. In the three-year pre-pandemic period, 32,644 tests were recorded, with an average of 29.34% positive cases. From the analysis of the pre-pandemic time series, there was a positive trend towards an increase in cases (p < 0.0001, z=6.1360, Tau=0.6955). In a non-pandemic scenario, from March 2020 to February 2021, the number of new tests expected would be 11,535; however, in the pandemic period, only 8,904 were observed, a 22.81% reduction on expected cases. According to the historical average positivity rate in the pre-pandemic period, the expected and observed total were, respectively, 3,384 and 2,566 positive cases for malignancy. Conclusions This study reveals a significant decrease of approximately 22.81% in new breast anatomopathological diagnoses during the COVID-19 pandemic in a laboratory in São Paulo, Brazil. For comparison purposes, we identified a 24.17% of missed opportunities on diagnoses of new malignancy-positive cases, compared to the pre-pandemic period. These findings highlight the need for strategies to mitigate the negative effects of the pandemic on the early detection and treatment of breast cancer.

Artificial Intelligence Algorithm for Subclinical Breast Cancer Detection

Early breast cancer detection is associated with lower morbidity and mortality. To examine whether a commercial artificial intelligence (AI) algorithm for breast cancer detection could estimate the development of future cancer. This retrospective cohort study of 116 495 women aged 50 to 69 years with no prior history of breast cancer before they underwent at least 3 consecutive biennial screening examinations used scores from an AI algorithm (INSIGHT MMG, version 1.1.7.2; Lunit Inc; used September 28, 2022, to April 5, 2023) for breast cancer detection and screening data from multiple, consecutive rounds of mammography performed from September 13, 2004, to December 21, 2018, at 9 breast centers in Norway. The statistical analyses were performed from September 2023 to August 2024. Artificial intelligence algorithm score indicating suspicion for the presence of breast cancer. The algorithm provided a continuous cancer detection score for each examination ranging from 0 to 100, with increasing values indicating a higher likelihood of cancer being present on the current mammogram. Maximum AI algorithm score for cancer detection and absolute difference in score among breasts of women developing screening-detected cancer, women with interval cancer, and women who screened negative. The mean (SD) age at the first study round was 58.5 (4.5) years for 1265 women with screening-detected cancer in the third round, 57.4 (4.6) years for 342 women with interval cancer after 3 negative screening rounds, and 56.4 (4.9) years for 116 495 women without breast cancer all 3 screening rounds. The mean (SD) absolute differences in AI scores among breasts of women developing screening-detected cancer were 21.3 (28.1) at the first study round, 30.7 (32.5) at the second study round, and 79.0 (28.9) at the third study round. The mean (SD) differences prior to interval cancer were 19.7 (27.0) at the first study round, 21.0 (27.7) at the second study round, and 34.0 (33.6) at the third study round. The mean (SD) differences among women who did not develop breast cancer were 9.9 (17.5) at the first study round, 9.6 (17.4) at the second study round, and 9.3 (17.3) at the third study round. Areas under the receiver operating characteristic curve for the absolute difference were 0.63 (95% CI, 0.61-0.65) at the first study round, 0.72 (95% CI, 0.71-0.74) at the second study round, and 0.96 (95% CI, 0.95-0.96) at the third study round for screening-detected cancer and 0.64 (95% CI, 0.61-0.67) at the first study round, 0.65 (95% CI, 0.62-0.68) at the second study round, and 0.77 (95% CI, 0.74-0.79) at the third study round for interval cancers. In this retrospective cohort study of women undergoing screening mammography, mean absolute AI scores were higher for breasts developing vs not developing cancer 4 to 6 years before their eventual detection. These findings suggest that commercial AI algorithms developed for breast cancer detection may identify women at high risk of a future breast cancer, offering a pathway for personalized screening approaches that can lead to earlier cancer diagnosis.

Enhanced breast tumor localization with DRA antenna backscattering and GPR algorithm in microwave imaging

With the rapid rate of increasing breast cancer cases across the globe and restrictions of existing early stage detection techniques, microwave imaging based cancer and malignant cell diagnosing methodology is finding its way. Microwave imaging technology has higher accuracy of detection in the variation of tissue properties. In this article, a dielectric resonator based wide band antenna is designed and investigated for microwave imaging (MWI) of early breast cancer detection due to its improved accuracy. The designed antenna has compact size, broad frequency spectrum, high gain and broadside radiation characteristics. To attain the wider bandwidth from (6.5 GHz–12.5 GHz), two rectangular shaped dielectric slabs are used. Wide band microwave frequency band supports the high resolution images. A C-shaped defected ground structure is used to tune the impedance matching. Dielectric resonator based antenna has higher gain and reduces the squints in antenna impedance. Two-asymmetrical dielectric slabs contribute in the excitation of different resonating modes which in turn enhance the bandwidth. It also contributes to excite multiple resonance mode that in turn enhance the bandwidth. Antenna performance is first numerically and experimentally verified in free space. Then, simulation based performance is examined for the microwave imaging. Numerical phantom with equivalent tissue properties is designed with and without the tumor. Unhealthy cells have higher water percentage and larger dielectric constant as compared to healthy cells. It causes the different in the back scattered signal of antenna, which is analyzed to diagnose the tumor or cancer. Here, a maximum deviation of 16 dB is observed in the backscattered signal. Furthermore, surface of the breast tissue is scanned along the x-axis and y-axis at different angels. The collected signals are used to reconstruct the microwave image of the interior. Mean value data of backscattered signals is used to locate the existence of unhealthy cells and GPR algorithm is used to calculate the depth of a 4 mm tumor in breast tissue. Proposed structure has shown efficient performance for microwave imaging.

Exploring factors associated with uptake of breast cancer screening among a subset of women in a South African metropolitan area: research to inform public health interventions

Aim Breast cancer represents a significant global health concern and remains a leading cause of cancer-related mortality, particularly among women in developing countries. Despite the South African Department of Health recommending early breast cancer detection to improve overall survival rates and enhance patients’ quality of life, there is lack of awareness among women regarding the importance of regular breast cancer screening and available options. This study investigates the factors associated with breast cancer screening uptake among a subset of women attending public health services in a South African metropolitan area. Methods A cross-sectional survey of adult women attending primary health care services at a community health centre was conducted in February-March 2020. Data on sociodemographics, health beliefs, access to information, and breast cancer screening uptake were subjected to descriptive and logistic regression analyses. Results Out of 252 women, 48.0% were older than 30 years, 69.4% were unemployed, and 56.0% self-reported breast cancer screening non-uptake. After adjusting for other variables, marital status, employment status, home language, and perceived breast cancer screening benefits were significantly associated with breast cancer screening uptake. Married women were twice (AoR: 2.3; CI: 1.19-4.58) as likely to self-report breast cancer screening than their unmarried counterparts. Employed women were almost thrice (AoR: 2.6; CI: 1.41-4.83) as likely to self-report breast cancer screening uptake relative to unemployed women. Women whose home language was Sesotho were almost twice (AoR: 1.9; CI: 1.05-3.43) as likely to self-report breast cancer screening than women reporting other home languages. Self-reported uptake of breast cancer screening was also more likely with every unit increase in perceived benefit scores (AoR: 1.1; CI: 1.02-1.16). Conclusions A significant proportion of women were in the age range where breast cancer screening is typically recommended, with more than half self-reporting breast cancer screening non-uptake. Results highlight the necessity of addressing socioeconomic, cultural and individual perceptions to improve breast cancer screening rates and promote early detection.