Early Detection Of Breast Cancer Research Articles

AI-Enhanced Visual-Spectral Synergy for Fast and Ultrasensitive Biodetection of Breast Cancer-Related miRNAs.

In biomedical testing, artificial intelligence (AI)-enhanced analysis has gradually been applied to the diagnosis of certain diseases. This research employs AI algorithms to refine the precision of integrative detection, encompassing both visual results and fluorescence spectra from lateral flow assays (LFAs), which signal the presence of cancer-linked miRNAs. Specifically, the color shift of gold nanoparticles (GNPs) is paired with the red fluorescence from nitrogen vacancy color centers (NV-centers) in fluorescent nanodiamonds (FNDs) and is integrated into LFA strips. While GNPs amplify the fluorescence of FNDs, in turn, FNDs enhance the color intensity of GNPs. This reciprocal intensification of fluorescence and color can be synergistically augmented with AI algorithms, thereby improving the detection sensitivity for early diagnosis. Supported by the detection platform based on this strategy, the fastest detection results with a limit of detection (LOD) at the fM level and the R2 value of ∼0.9916 for miRNA can be obtained within 5 min. Meanwhile, by labeling the capture probes for miRNA-21 and miRNA-96 (both of which are early indicators of breast cancer) on separate T-lines, simultaneous detection of them can be achieved. The miRNA detection methods employed in this study may potentially be applied in the future for the early detection of breast cancer.

Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach

Breast cancer remains a major public health challenge worldwide. The identification of accurate biomarkers is critical for the early detection and effective treatment of breast cancer. This study utilizes an integrative machine learning approach to analyze breast cancer gene expression data for superior biomarker and drug target discovery. Gene expression datasets, obtained from the GEO database, were merged post-preprocessing. From the merged dataset, differential expression analysis between breast cancer and normal samples revealed 164 differentially expressed genes. Meanwhile, a separate gene expression dataset revealed 350 differentially expressed genes. Additionally, the BGWO_SA_Ens algorithm, integrating binary grey wolf optimization and simulated annealing with an ensemble classifier, was employed on gene expression datasets to identify predictive genes including TOP2A, AKR1C3, EZH2, MMP1, EDNRB, S100B, and SPP1. From over 10,000 genes, BGWO_SA_Ens identified 1404 in the merged dataset (F1 score: 0.981, PR-AUC: 0.998, ROC-AUC: 0.995) and 1710 in the GSE45827 dataset (F1 score: 0.965, PR-AUC: 0.986, ROC-AUC: 0.972). The intersection of DEGs and BGWO_SA_Ens selected genes revealed 35 superior genes that were consistently significant across methods. Enrichment analyses uncovered the involvement of these superior genes in key pathways such as AMPK, Adipocytokine, and PPAR signaling. Protein-protein interaction network analysis highlighted subnetworks and central nodes. Finally, a drug-gene interaction investigation revealed connections between superior genes and anticancer drugs. Collectively, the machine learning workflow identified a robust gene signature for breast cancer, illuminated their biological roles, interactions and therapeutic associations, and underscored the potential of computational approaches in biomarker discovery and precision oncology.

A novel approach for breast cancer detection using optimized ensemble learning framework and XAI

Breast cancer (BC) is a common and highly lethal ailment. It stands as the second leading contributor to cancer-related deaths in women worldwide. The timely identification of this condition is of utmost importance in mitigating mortality rates. This research paper presents a novel framework for the precise identification of BC, utilising a combination of image and numerical data features with explainable Artificial Intelligence (XAI). The utilisation of the U-NET transfer learning model is employed for image-based prediction. Additionally, an ensemble model is constructed by integrating characteristics from a customised convolutional neural network (CNN) model with an ensemble comprising random forest (RF) and support vector machine (SVM). The experiments aim to evaluate the influence of original features compared to convoluted features. A comparative analysis is carried out to assess the efficacy of various classifiers in accurately detecting BC, utilising the Wisconsin dataset. The model under consideration exhibits promising capabilities in enhancing BC diagnosis, with a remarkable accuracy rate of 99.99%. The present study contributes to the advancement of BC diagnosis by introducing a novel strategy based on machine learning and discussing the interpretation of the variables using XAI. The primary objective of this approach is to get a notable level of precision, hence facilitating the early and reliable identification of BC. Ultimately, the implementation of this approach is expected to enhance patient outcomes.

Analysis of Breast Cancer Screening Results and Influencing Factors of Breast Cancer in Guangdong Province from 2017 to 2021.

Breast cancer screening plays an important role in the early detection, diagnosis and treatment of breast cancer. The aim of this study was to evaluate the screening results and explore the influencing factors of breast cancer detection rate in Guangdong. This cross-sectional study was conducted among 2,024,960 women aged 35-64 in Guangdong Province during 2017-2021. The data about breast cancer screening information were collected from the Guangdong maternal and child health information system. Descriptive statistical analysis was used to explain demographic characteristics and results of breast cancer screening. The generalized linear regression model was applied to analyze the related influencing factors of breast cancer detection rate. The estimated detection rate of breast cancer in Guangdong Province is 70.32/105, with an early diagnosis rate of 82.06%. After adjusting covariates, those women with older age (45-55 [OR (95% CI) 2.174 (1.872, 2.526)], 55-65 [OR (95% CI) 2.162 (1.760, 2.657)]), education for high school ([OR (95% CI) 1.491 (1.254, 1.773)]) and older age at first birth ([OR (95% CI) 1.632 (1.445, 1.844)]) were more likely to have higher detection rate of breast cancer. No history of surgery or biopsy ([OR (95% CI) 0.527 (0.387, 0.718)]), no history of breast cancer check ([OR (95% CI) 0.873 (0.774, 0.985)]) and no family history of breast cancer ([OR (95% CI) 0.255 (0.151, 0.432)]) women were more likely to screen negative for breast cancer (P < 0.05). The detection rate of breast cancer in screening showed an increasing trend year by year in Guangdong Province. Older age, education for high school and older age at first birth were risk factors for breast cancer detection rate, while no surgery or biopsy history, no family history of breast cancer and no history of breast cancer check were protective factors.

Barriers and facilitators for breast cancer early diagnosis in an indigenous community in Mexico: voices of otomí women

BackgroundLiterature on barriers and facilitators for early detection of Breast Cancer (BC) among indigenous women is very scarce. This study aimed to identify barriers and facilitators for BC early diagnosis as perceived by women of the otomí ethnic group in Mexico.MethodsWe performed an exploratory qualitative study. Data was collected in 2021 through three focus group interviews with 19 otomí women. The interview transcripts were analyzed using the constant comparison method and guided by a conceptual framework that integrates the Social Ecological Model (SEM), the Health Belief Model and the Institute of Medicine’s Healthcare Quality Framework.ResultsBarriers and facilitators were identified at several levels of the SEM. Among the main barriers reported by the study participants were: beliefs about illness, cancer stigma, cultural gender norms, access barriers to medical care, and mistreatment and discrimination by health care personnel. Our participants perceived as facilitators: information provided by doctors, social support, perceived severity of the disease and perceived benefits of seeking care for breast symptoms.ConclusionsHealthcare policies need to be responsive to the particular barriers faced by indigenous women in order to improve their participation in early detection and early help-seeking of care for breast symptoms. Measures to prevent and eradicate all forms of discrimination in healthcare are required to improve the quality of healthcare provided and the trust of the indigenous population in healthcare practitioners.

UGGNet: Bridging U-Net and VGG for Advanced Breast Cancer Diagnosis

In the field of medical imaging, breast ultrasound has emerged as a crucial diagnostic tool for early detection of breast cancer. However, the accuracy of diagnosing the location of the affected area and the extent of the disease depends on the experience of the physician. In this paper, we propose a novel model called UGGNet, combining the power of the U-Net and VGG architectures to enhance the performance of breast ultrasound image analysis. The U-Net component of the model helps accurately segment the lesions, while the VGG component utilizes deep convolutional layers to extract features. The fusion of these two architectures in UGGNet aims to optimize both segmentation and feature representation, providing a comprehensive solution for accurate diagnosis in breast ultrasound images. Experimental results have demonstrated that the UGGNet model achieves a notable accuracy of 78.2\% on the "Breast Ultrasound Images Dataset."

A Multi-group similarity-decoding-based method for deep model ensembling applied in the microcalcification classification on digital mammograms

Classifying mammograms scored at Breast Imaging-Reporting and Data System (BI-RADS ) 4 is crucial for the early detection of breast cancer. But it is particularly challenging because microcalcifications (MCs) vary greatly in shape and distribution. Traditional machine learning and deep models are either time-consuming or poorly generalized. Recently, deep ensemble learning, which can improve generalization, has been developed rapidly. However, most ensembling strategies are based on weighted average and often sensitive to biased learners. To achieve data-adaption, they employ complex techniques to implement dynamic weight calculation. In this paper, we proposed a novel multi-group similarity-decoding-based method (MG-SDM) which enables a more robust and easier ensembling of different deep models. Firstly, the multi-group (MG) strategy is designed to transform model ensembling into a process of feature-level fusion, which could alleviate the impact of a single model on overall performance. Specifically, individual features of different models are grouped based on the position, and feature fusion is performed within each group. Secondly, the similarity-decoding-based method (SDM) is developed to fuse features. All individual features are vectorized through linear transformation. Then their similarities with a learnable latent embedding, which is introduced to denote most task-related features, are calculated using inner product. Finally, the similarity values among features within same group are normalized to be weights for a subsequent weighted summation operation. Extensive experiments on the clinical dataset demonstrate the superiority of the proposed over other state-of-the-art methods.

Iterative Tuning of Tree-Ensemble-Based Models' parameters Using Bayesian Optimization for Breast Cancer Prediction

The study presents a method for iterative parameter tuning of tree ensemble-based models using Bayesian hyperparameter tuning for states prediction, using breast cancer as an example. The proposed method utilizes three different datasets, including the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the Surveillance, Epidemiology, and End Results (SEER) breast cancer dataset, and the Breast Cancer Coimbra dataset (BCCD), and implements tree ensemble-based models, specifically AdaBoost, Gentle-Boost, LogitBoost, Bag, and RUSBoost, for breast cancer prediction. Bayesian optimization was used to tune the hyperparameters of the models iteratively, and the performance of the models was evaluated using several metrics, including accuracy, precision, recall, and f1-score. Our results show that the proposed method significantly improves the performance of tree ensemble-based models, resulting in higher accuracy, precision, recall, and f1-score. Compared to other state-of-the-art models, the proposed method is more efficient. It achieved perfect scores of 100% for Accuracy, Precision, Recall, and F1-Score on the WDBC dataset. On the SEER BC dataset, the method achieved an accuracy of 95.9%, a precision of 97.6%, a recall of 94.2%, and an F1-Score of 95.9%. For the BCCD dataset, the method achieved an accuracy of 94.7%, a precision of 90%, a recall of 100%, and an F1-Score of 94.7%. The outcomes of this study have important implications for medical professionals, as early detection of breast cancer can significantly increase the chances of survival. Overall, this study provides a valuable contribution to the field of breast cancer prediction using machine learning.

Navigating the genetic landscape of breast cancer in South Africa amidst a developing healthcare system.

Breast cancer is a significant global health issue as it represents the leading cause of death in women worldwide. In 2021, the World Health Organization established the Global Breast Cancer Initiative framework with the aim to reduce the breast cancer mortality rate by the year 2040. In countries with developing healthcare systems, such as South Africa, the implementation of first-world technologies has been slow. We provide an overview of the strides taken to improve the cost-effectiveness of genetic service delivery for breast cancer patients in South Africa - from advances in the technology utilized for BRCA founder genotyping to variant screening in moderate-to high-penetrance genes. We furthermore reflect on research undertaken to improve accessibility by means of population-directed point-of-care genetic testing that is ideal for use in a primary healthcare setting. We also report on a pilot study utilizing exome sequencing at the intersection between research and service delivery. Finally, we discuss and conclude on the controversies, research gaps, and future prospects based on the most recent developments in first-world countries that are implementable in developing countries to improve early detection of breast cancer and overall disease management.

The Relationship Between Knowledge Of Women Childbearing Age And Early Detection Of Breast Cancer

According to 2018 World Health Organization data, the most common cancer incident in Indonesia is breast cancer, namely 58,256 cases or 16.7% of the total 348,809 cancer cases. Cancer is the second leading cause of death in the world at 13% after cardiovascular disease. It is estimated that by 2030 the incidence of cancer could reach 26 million people and 17 million of them will die from cancer, especially in poor and developing countries, the incidence will be faster. (Sari et al., 2020) The method used in this research is an analytical survey with a cross-sectional approach where data relating to independent variables or risks and dependent variables or impact variables, will be collected at the same time (Notoatmodjo, 2012). The data collection method in research uses primary data, where data is taken directly from the source, observed and recorded for the first time. Data testing was carried out using the chi-square statistical test with a confidence level of 95% and a significance level of 5%, meaning that the risk of making a wrong decision to reject the correct hypothesis is as much as 5%. knowledge of women of childbearing age regarding early detection of breast cancer (Awareness) in the UPTD Working Area of ​​the Semulajadi Health Center in 2023. The results of the research showed that from 82 respondents, it was found that 38 people (46.3%) had poor knowledge and were not aware. Those who did so were 44 people (53.7%). KEYWORDS: breast cancer, awareness, knowledge, childbearing age

Health Education and Simulation of Breast Self-Examination Implementation for Young Women at Konawe Islands State High School

This lecturer's dedication aims to increase health awareness of adolescent girls at Konawe Islands State High School through education and breast self-examination simulations. Lecturers are involved in the development of educational programs involving interactive presentations, group discussions, and simulated demonstrations of breast self-examination. During the activity, lecturers provided in-depth knowledge about the importance of early detection of breast cancer and provided practical information on how to conduct self-examination correctly. The simulation was performed using simple breast models and visual aids. The results of this dedication show an increase in the knowledge and skills of young women in conducting breast self-examinations, as well as motivating them to maintain breast health regularly. With this approach, lecturer service contributes positively to breast cancer prevention efforts and improves the quality of life of adolescent girls in the educational environment.

Empowering breast cancer diagnosis and radiology practice: advances in artificial intelligence for contrast-enhanced mammography.

Artificial intelligence (AI) applications in breast imaging span a wide range of tasks including decision support, risk assessment, patient management, quality assessment, treatment response assessment and image enhancement. However, their integration into the clinical workflow has been slow due to the lack of a consensus on data quality, benchmarked robust implementation, and consensus-based guidelines to ensure standardization and generalization. Contrast-enhanced mammography (CEM) has improved sensitivity and specificity compared to current standards of breast cancer diagnostic imaging i.e., mammography (MG) and/or conventional ultrasound (US), with comparable accuracy to MRI (current diagnostic imaging benchmark), but at a much lower cost and higher throughput. This makes CEM an excellent tool for widespread breast lesion characterization for all women, including underserved and minority women. Underlining the critical need for early detection and accurate diagnosis of breast cancer, this review examines the limitations of conventional approaches and reveals how AI can help overcome them. The Methodical approaches, such as image processing, feature extraction, quantitative analysis, lesion classification, lesion segmentation, integration with clinical data, early detection, and screening support have been carefully analysed in recent studies addressing breast cancer detection and diagnosis. Recent guidelines described by Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to establish a robust framework for rigorous evaluation and surveying has inspired the current review criteria.

Mass segmentation in automated breast ultrasound using an enhanced attentive UNet

Breast cancer is the most diagnosed cancer among women. Early detection of breast cancer through screening high risk population significantly reduces the mortality rate. To this end, 3-D Automated Breast Ultrasound (ABUS) is a favorable modality due to its complementary information in comparison with other modalities. Different characteristics of breast masses and the large number of ABUS slices result in time consuming and exhaustive evaluations by radiologists. This study aims to develop a fully-automatic segmentation system that acts as a second interpreter to facilitate the interpretation process. Specifically, a bidirectional convolutional long short-term memory (Bi-ConvLSTM) network is adopted for tumor detection. Afterwards, a 2-D attentive UNet takes an ABUS slice and its approximate saliency map to precisely segment breast masses. The attention blocks of the proposed network use this map to direct the network’s attention to regions with high saliency values. We proposed two enhanced versions of the base attention module with superior performance and less complexity. To increase the robustness against the mass size variations, we stacked several side outputs from different stages of the network and control their contribution to final results by a Convolutional Block Attention Module (CBAM). The effectiveness of the proposed modifications is verified with step-by-step evaluations, and our optimal model achieves a Dice similarity index of 85.82% on a dataset of 60 volumes.

Multi-wavelength interference phase imaging for automatic breast cancer detection and delineation using diffuse reflection imaging

Millions of women globally are impacted by the major health problem of breast cancer (BC). Early detection of BC is critical for successful treatment and improved survival rates. In this study, we provide a progressive approach for BC detection using multi-wavelength interference (MWI) phase imaging based on diffuse reflection hyperspectral (HS) imaging. The proposed findings are based on the measurement of the interference pattern between the blue (446.6 nm) and red (632 nm) wavelengths. We consider implementing a comprehensive image processing and categorization method based on the use of Fast Fourier (FF) transform analysis pertaining to a change in the refractive index between tumor and normal tissue. We observed that cancer growth affects tissue organization dramatically, as seen by persistently increased refractive index variance in tumors compared normal areas. Both malignant and normal tissue had different depth data collected from it that was analyzed. To enhance the categorization of ex-vivo BC tissue, we developed and validated a training classifier algorithm specifically designed for categorizing HS cube data. Following the application of signal normalization with the FF transform algorithm, our methodology achieved a high level of performance with a specificity (Spec) of 94% and a sensitivity (Sen) of 90.9% for the 632 nm acquired image categorization, based on preliminary findings from breast specimens under investigation. Notably, we successfully leveraged unstained tissue samples to create 3D phase-resolved images that effectively highlight the distinctions in diffuse reflectance features between cancerous and healthy tissue. Preliminary data revealed that our imaging method might be able to assist specialists in safely excising malignant areas and assessing the tumor bed following resection automatically at different depths. This preliminary investigation might result in an effective "in-vivo" disease description utilizing optical technology using a typical RGB camera with wavelength-specific operation with our quantitative phase MWI imaging methodology.

Intelligent Model for the Early Detection of Breast Cancer Using Fine Needle Aspiration of Breast Mass.

Breast cancer is a type of cancer that forms in the cells of the breasts. It can occur in both men and women, but it is far more common in women with an increase likelihood of development caused by some risk factors like age, family history, gene mutations, hormonal factors, and exposure to estrogen. There are several essential methods for early diagnostic and detection, like regular breast self-examinations, clinical breast examination mammograms, etc. Breast cancer detection faces several challenges that span various stages from screening to diagnosis. It is in the light of these, that the drive to overcome these challenges faced by patients and medical experts to get timely diagnosis or detection of breast cancer that motivated this research. The work aims at developing an intelligent-based system for the early detection of breast cancer driven by machine learning algorithm using features obtained from a digitized image of the Fine Needle Aspiration (FNA) of breast mass. The Machine learning algorithm used was the Support Vector Machine (SVM) and was apply to the Breast Cancer dataset. Additionally, feature selection and a neural network model was applied to improve the performance of the system. This work demonstrates that SVM algorithm can be used to enhance the early detection of breast cancer. Compared with the 92% accuracy of mammography, this machine learning model achieved a higher accuracy of around 98.18% for breast cancer detection and diagnosis based on the dataset that was used.

The role of clinical breast examination and fine needle aspiration cytology in early detection of breast cancer: A cross-sectional study nested in a cohort in a low-resource setting.

Breast cancer is prevalent worldwide, with disparities in screening, diagnosis, treatment outcomes, and survival. In Africa, the majority of women are diagnosed at advanced stages, affecting treatment outcomes. Screening is one of the best strategies to reduce mortality rates caused by this cancer. Yet in a resource-constrained setting, there is limited access to screening and early detection services, which are available only at a few referral hospitals. We aimed to evaluate the prevalence and screening results of breast cancer using clinical breast examination coupled with fine needle aspiration cytology in a resource-constraint setting. A combined cross-sectional and cohort study. Women at risk of developing breast cancer in the Kilimanjaro region of Tanzania were invited, through public announcements, to their primary healthcare facilities. A questionnaire was used to assess the participants' characteristics. The women received a clinical breast examination, and detectable lesions were subjected to a confirmatory fine needle aspiration cytology or an excisional biopsy. Preliminary data from this ongoing breast cancer control program were extracted and analyzed for this study. A total of 3577 women were screened for breast cancer; their mean age was 47 ± 7.53 years. About a third of them (1145, 32%) were practicing self-breast examination at least once a month. Of 200 (5.6%) with abnormal clinical breast examination, 18 (9%) were confirmed to be breast cancer, making the prevalence to be 0.5%. The vast majority of participants with breast cancer (13, 72.2%) had early disease stages, and infiltrating ductal carcinoma, no special type, was the most common (15, 83.3%) histopathology subtype. Hormonal receptor status determination results indicated that 11 (61.1%), 7 (38.9%), and 5 (27.8%) of the tumors overexpressed estrogen receptor, progesterone receptor, and human epidermal receptor-2, respectively. Our study demonstrates 5.6% of Tanzanian women have abnormal clinical breast examination findings, with 9% having breast cancer. Nearly three-quarters (72.2%) of breast cancer screened for early disease were detected in the early disease stages. This finding suggests that organized screening with clinical breast examination coupled with fine needle aspiration cytology, which is a simple and cost-effective screening method, has the potential to improve early detection and outcomes for breast cancer patients in a resource-constraint setting.

Knowledge of Non-Invasive Biomarkers of Breast Cancer, Risk Factors, and BSE Practices Among Nursing Undergraduates in Farasan Island, KSA.

Mammograms are sometimes met with issues of overdiagnosis and underdiagnosis; therefore, they are less reliable in identifying cancer in women with dense breasts. As a result, it is critical to be aware of other sensitive screening techniques for the early diagnosis of breast cancer. The ultimate objective of this study was to assess the knowledge of nursing undergraduates regarding non-invasive biomarkers, such as volatile organic compounds in breath, nipple aspirate fluid, sweat, urine, and tears, for the early detection of breast cancer to help improve patient care, determine the risk factors, and encourage practice of breast self-examination. Cross-sectional research was done in the Department of Nursing at Farasan campus using a self-structured questionnaire as the study tool. A total of 260 students willingly participated. The study tool had evaluation questions focused on the non-invasive biomarkers of breast cancer, risk factors, and breast self-examination practices to collect data. The data were subjected to descriptive and inferential statistics. The statistical significance was calculated at P < .05. Data analyses were done using Microsoft Excel (2013). A significant knowledge gap existed among the study participants about the non-invasive biomarkers of breast cancer. A lesser percentage of students (25%) stated that they do breast self-examination on a monthly basis. The most common reasons for not doing the breast self-examination were "not knowing how to do the breast self-examination" (77.3%), fear of a positive diagnosis (53.9%), thinking that they are not at risk as all were in their teens and hence not required (44.7%), and lack of time (48.7%). Age and frequency of breast self-examination were significantly associated (P < .05) as those few students (22.7%) who were doing breast self-examination practices every 2-4 months belonged to a higher study year. Furthermore, knowledge regarding incidence rates and health care expenditure by the government on breast cancer was also significantly low (P < .05). Outcomes would help prioritize actions to help future nurses better understand breast cancer, allowing them to extend patient care in the best way possible.

Dual band polygonal slot antenna for early detection of breast cancer

Dual band polygonal slot antenna for early detection of breast cancer

Dysregulation of miR-122, miR-574 and miR-375 in Egyptian patients with breast cancer.

The early detection of breast cancer (BC) is receiving global attention, creating an urgent need for more sensitive and comprehensive strategies for preventive intervention, therapy assessment, and prognosis prediction. Aberrant expression of miRNAs has been observed in various malignancies and may be potential targets for therapy. Our study aims to examine the expression profiles of miR-375, miR-574-3p, and miR-122 in the sera of Egyptian women with BC, benign breast lesions, and a control group. We hope to determine if these miRNAs can serve as minimally invasive biomarkers for BC. This is a case-control study in which 77 patients with newly diagnosed BC, 20 patients with benign breast tumors, and 30 normal healthy subjects as controls were recruited from the outpatient clinic of the National Cancer Institute. The assessment of miRNAs was conducted using RT-PCR (Applied Biosystems). The expression level of miRNA-122 was significantly upregulated in the BC group, while the expression levels of miRNA-574 and miRNA-375 showed significant downregulation in BC patients. Serum miR-122 and miRNA-375 were able to distinguish breast cancer from the benign and control groups in ROC curve analysis, with AUCs of 0.786 and 0.796, respectively. Our results also showed that serum miR-122 and miR-574 are significant predictor variables in the multivariate analysis, after adjusting for age. Our findings suggest that miR-122 may act as an onco-microRNA, while miR-574 and miR-375 may have a main tumour suppressor role. The studied miRNAs may serve as minimally invasive biomarkers for cases of breast cancer and as promising potential therapeutic targets for breast cancer.

Challenges in the management of inconclusive breast findings on mammography in educated Nigerian women

Introduction: Breast cancer is one of the leading causes of morbidity and mortality among women in middle to low-income countries with most women presenting with late disease. Mammography is a major screening tool for the early detection of breast cancer with wide coverage in developed countries when compared to low-income countries. This study aims to evaluate the mammographic findings of women in southwest Nigeria, and also the follow-up rate of women with inconclusive findings on mammograms, Methodology: This is a descriptive study of findings in women who presented for mammography at the Radiology Department of LAUTECH Teaching Hospital, Ogbomoso. Mammograms with two standard views were performed after the completion of a structured questionnaire and informed consent. The data was entered and analyzed using SPSS version 26 with a p-value ≤ 0.5. Results: A total of 250 women participated in the study with an average age of 52.3±8.70 years and an age range of 36-86 years. Diagnostic mammography (55.2%) was more common than screening mammography and the commonest indication was breast lumps (25.2%). Heterogeneously dense breast density pattern (BIRADS C) which may obscure small masses is the most common (40.8%) while the most common final BIRADS category is 2, benign findings (51.6%), followed by BIRADS 0 (20%). Only 13 out of the 51 cases of BIRADS 0 had complementary imaging studies, and 38 (75%) failed to show up. Further, telephone interviews revealed that the most common reason for failure of follow up is poor counselling and ignorance. Conclusion: There is a need to increase the awareness of regular mammograms for screening purposes in this environment as observed in developed climes and also for women to understand the clinical implication of their breast density pattern to reduce anxiety and increase their cooperation for further imaging studies.