Detection Of Breast Cancer Research Articles

Multi-omic analysis identifies metabolic biomarkers for the early detection of breast cancer and therapeutic response prediction

Reliable blood-based tests for identifying early-stage breast cancer remain elusive. Employing single-cell transcriptomic sequencing analysis, we illustrate a close correlation between nucleotide metabolism in the breast cancer and activation of regulatory Tcells (Tregs) in the tumor microenvironment, which shows distinctions between subtypes of patients with triple-negative breast cancer (TNBC) and non-TNBC, and is likely to impact cancer prognosis through the A2AR-Treg pathway. Combining machine learning with absolute quantitative metabolomics, we have established an effective approach to the early detection of breast cancer, utilizing a four-metabolite panel including inosine and uridine. This metabolomics study, involving 1111 participants, demonstrates high accuracy across the training, test, and independent validation cohorts. Inosine and uridine prove predictive of the response to neoadjuvant chemotherapy (NAC) in patients with TNBC. This study deepens our understanding of nucleotide metabolism in breast cancer development and introduces a promising non-invasive method for early breast cancer detection and predicting NAC response in patients with TNBC.

Addressing Global Gaps in Mammography Screening for Improved Breast Cancer Detection: A Review of the Literature.

Breast cancer is the second most common cancer globally, with 2.3 million new cases annually, constituting 11.6% of all cancer cases. It is also the fourth leading cause of cancer deaths, claiming 670,000 lives a year. This high incidence of breast cancer morbidity worldwide has increased the urgent need for standardized and adequate screening methods, including clinical breast examination, self-breast examination, and mammography screening tests for non-symptomatic individuals. Mammography is considered the gold standard for breast cancer screening, with early randomized control trials showing significant reductions in mortality rates in women aged 50 and over (International Agency for Research on Cancer and American College of Radiology). Despite this, discrepancies in mammography practices across different healthcare settings regarding adherence to international standards raise concerns. A comprehensive review of the vast literature looking at the practices and norms of mammography screening worldwide highlighted several domains that present limitations to screening. These include epidemiological data deficits, lack of educational training offered to radiographers and varied image quality indices, exposure technique, method of breast compression, dose calculation, reference levels, screening frequency intervals, and diverse distribution of resources, particularly in developing countries. These factors shed light on the substantial discrepancies in the implementation and efficacy of screening programs, underscoring the necessity for future research endeavors to collaborate in creating coherent, standardized, evidence-based guidelines. Addressing these issues can enhance the feasibility, sensitivity, and accessibility of screening programs, resulting in favorable impacts on the early diagnosis and survival of breast cancer on a global scale.

Optimizing breast cancer diagnosis: combining hybrid architectures through Apache Spark

Early detection and diagnosis of breast cancer are critical for saving lives. This paper addresses two major challenges associated with this task: the vast amount of data processing involved and the need for early detection of breast cancer. To tackle these issues, we developed thirty hybrid architectures by combining five deep learning techniques (Xception, Inception-V3, ResNet50, VGG16, VGG19) as feature extractors and six classifiers (random forest, logistic regression, naive Bayes, gradient-boosted tree, decision tree, and support vector machine) implemented on the Spark framework. We evaluated the performance of these architectures using four classification criteria. The results, analyzed using Scott Knott's statistical test, demonstrated the effectiveness of merging deep learning feature extraction techniques with traditional classifiers for classifying breast cancer into malignant and benign tumors. Notably, the hybrid architecture using logistic regression as the classifier and ResNet50 for feature extraction (RESLR) emerged as the top performer. It achieved impressive accuracy scores of 98.20%, 96.59%, 96.64%, and 94.84% across the Break-His dataset at different magnifications (40X, 100X, 200X, and 400X) respectively. Additionally, RESLR achieved an accuracy of 97.05% on the ICIAR dataset and a remarkable accuracy of 95.31% on the FNAC dataset.

DM-PA-CNTFET Biosensor for Breast Cancer Detection: Analytical Model

In this paper, an analytical model for a novel design dielectric modulated plasma-assisted carbon nanotube field-effect transistor (DM-PA-CNTFET) biosensor is proposed for breast cancer detection. This work is based on a PA-CNTFET in which CNT is used as a channel of FET, and various other device engineering techniques such as dual metal gate-all-around structure and dielectric stack of SiO2 and HfO2 have been used. A comparative analysis of DS-GAAE-CNTFET was performed using a silicon gate all-around FET (Silicon-GAA-FET)-based biosensor. Early detection of breast cancer is made possible by immobilizing MDA-MB-231 and HS578t into the dual-sided nanocavity, which alters the electrical properties of the proposed CNTFET-based biosensor. The DS-GAAE-CNTFET sensor demonstrates a drain ON current sensitivity of 236.9 nA and a threshold voltage sensitivity of 285.58 mV for HS578t cancer cells. Malignant MDA-MB-231 breast cells exhibit a higher drain ON current sensitivity of 343.35 nA and a corresponding threshold voltage sensitivity of 293.23 mV. The exceptional sensitivity and structural resilience of the DS-GAAE-CNTFET biosensor establish it as a promising candidate for early breast cancer detection.

Factors associated with deferral or non-performance of an organized breast cancer screening program during the COVID-19 pandemic in France.

Delays in detection and treatment of breast cancer can lead to increased mortality. To date, participation in organized breast cancer screenings (OBCS) has been suboptimal worldwide. The objective of this study was to investigate the factors associated with deferral or non-performance of mammography during the COVID-19 pandemic for women who had previously participated in OBCS. A retrospective observational study was conducted on a cohort of 6282 women from the Aube Department of France, who were invited to an OBCS in 2020. Participants were divided into women who promptly underwent screening after receiving an invitation (between 22 and 25 months elapsed since the last mammogram), women who underwent late screening (≥26 months since the last mammogram), and those who were never screened. Data were collected from a self-reported questionnaire. Comparative and multivariable analyses modeling the probability of each type of attendance were performed using these data. In total, 2301 women (aged 50-74 years) returned a valid questionnaire. Compared to women who promptly underwent mammography, non- and late-screening participants were younger, had less frequent gynecological follow-up and a less frequent history of colorectal cancer screening. Women with higher education status and those residing in socially disadvantaged areas were more likely to attend late. The absence of regular gynecological follow-up and the absence of colorectal cancer screening were significant factors associated with deferral of or non-attendance at OBCS.

Detection of Contralateral Malignancies on Breast MRI.

Women with unilateral breast cancer are at increased risk for having simultaneous cancer of the contralateral breast. Overall, earlier detection of contralateral breast cancer prevents the burden of additional surgery or chemotherapy rounds and is associated with higher overall survival. However, MRI screening for the contralateral breast is seldom done following an initial unilateral breast cancer diagnosis. The purpose of this study is to retrospectively evaluate patients with known, biopsy-proven malignancy who went on to obtain a breast MRI and were later found to have cancer of the contralateral breast. Methods: This was a retrospective study that reviewed the charts of women aged over 18 years who were determined to have synchronous bilateral breast cancer from January 2017 to January 2022 at the University of Florida, Gainesville, FL. The study extracted data from this institution's cancer registry database, which provided information on patients with breast cancer diagnoses. The study conducted a review of mammography (MAM) and MRI imaging reports to ascertain the presence or absence of contralateral breast cancer identified by each respective imaging modality. Surgical pathology reports from the biopsy of the contralateral breast were reviewed to obtain information on the histological type of cancer and TNM (tumor, node, metastasis) staging. Of the 17 cases in which MAM missed contralateral cancer, follow-up MRI detected contralateral malignancy in 12 cases (70.59%) and subsequently changed management, resulting in additional imaging, biopsy, and eventual diagnosis and treatment of contralateral breast cancer. Examining the number of contralateral breast cancers detected by patients who had undergone MAM followed by MRI and those who had only undergone MAM, the study found that the detection rate of contralateral breast cancer from MAM was 45.45% (15/33). The tumor stages of the missed cancers were all T1 or Tis stage with one T1mi, and there was no nodal involvement. Conclusion: In addition to its utility in staging breast cancers, MRI also has the superior ability to detect otherwise undetected contralateral breast malignancy. This retrospective study found that MRI imaging led to a considerable increase in the detection of contralateral cancer. The study found that these undetected contralateral breast cancers by MAM were often of lower staging with no nodal involvement, highlighting the opportunity for MRI to assist in early cancer detection while the patient's prognosis is still good. Its high cost should be balanced with staging and occult malignancy detection utility in future practice.

72229 - Ultralow dose of superparamagnetic iron oxide nanoparticles in combination with Magseed for sentinel node detection in nonpalpable breast cancer

72229 - Ultralow dose of superparamagnetic iron oxide nanoparticles in combination with Magseed for sentinel node detection in nonpalpable breast cancer

Mammography and Breast Ultrasonography Services in Ghana, Availability, and Geographic Access.

Breast cancer is the leading type of cancer diagnosed and the second leading cause of cancer-related death in Ghana. Mammography and ultrasound have proven benefits in the early detection of breast cancer. This study evaluates mammography, breast ultrasound, and radiology work force availability throughout Ghana. A survey was administered to all hospitals in Ghana from November 2020 to October 2021. Mammography, breast ultrasound services, and the number of radiologists were assessed. For mammography, the number performed per month, cost incurred by the patient, where images were read, and how long it took to receive reports were also assessed. Health Facilities Regulatory Authority records on diagnostic centers were obtained to identify additional in-country breast imaging services. Three hundred and twenty-eight of 346 hospitals participated in the survey (95%). Only 21 hospitals reported on-site mammography. One hospital reported performing >100 mammographies per month. The average cost to the patient ranged from 100 to 500 Cedis ($17-87 US dollars [USD]), although three hospitals performed mammography at no cost. An additional 10 mammography machines were identified at diagnostic centers throughout the country, with 41.3% of the female population living within 1 hour of mammography services. There were 135 hospital-based breast ultrasound services identified with 69.5% of the female population living within 1 hour of these services. There were an additional 190 ultrasound machines at diagnostic centers. There were 96 in-country radiologists identified. Although there is limited availability and utilization of mammography in Ghana, there is more readily available ultrasonography. A focus on increasing breast cancer early diagnostic capabilities with breast ultrasound should be prioritized in addition to further expansion of the radiology workforce.

CNN-based deep learning approach for classification of invasive ductal and metastasis types of breast carcinoma.

Breast cancer is one of the leading cancer causes among women worldwide. It can be classified as invasive ductal carcinoma (IDC) or metastatic cancer. Early detection of breast cancer is challenging due to the lack of early warning signs. Generally, a mammogram is recommended by specialists for screening. Existing approaches are not accurate enough for real-time diagnostic applications and thus require better and smarter cancer diagnostic approaches. This study aims to develop a customized machine-learning framework that will give more accurate predictions for IDC and metastasis cancer classification. This work proposes a convolutional neural network (CNN) model for classifying IDC and metastatic breast cancer. The study utilized a large-scale dataset of microscopic histopathological images to automatically perceive a hierarchical manner of learning and understanding. It is evident that using machine learning techniques significantly (15%-25%) boost the effectiveness of determining cancer vulnerability, malignancy, and demise. The results demonstrate an excellent performance ensuring an average of 95% accuracy in classifying metastatic cells against benign ones and 89% accuracy was obtained in terms of detecting IDC. The results suggest that the proposed model improves classification accuracy. Therefore, it could be applied effectively in classifying IDC and metastatic cancer in comparison to other state-of-the-art models.

The Chiraiya project: a retrospective analysis of breast cancer detection gaps addressed via mobile mammography in Jammu Province, India

BackgroundBreast cancer remains a pervasive threat to women worldwide, with increasing incidence rates necessitating effective screening strategies. Timely detection with mammography has emerged as the primary tool for mass screening. This retrospective study, which is part of the Chiraiya Project, aimed to evaluate breast lesion patients identified during opportunistic mammography screening camps in Jammu Province, India.MethodsA total of 1505 women aged 40 years and older were screened using a mobile mammographic unit over a five-year period, excluding 2020 and 2021 due to the COVID-19 pandemic. The inclusion criterion was women in the specified age group, while the exclusion criterion was women with open breast wounds, history of breast cancer or a history of breast surgery. The screening process involved comprehensive data collection using a detailed Proforma, followed by mammographic assessments conducted within strategically stationed mobile units. Radiological interpretations utilizing the BI-RADS system were performed, accompanied by meticulous documentation of patient demographics, habits, literacy, medical history, and breastfeeding practices. Participants were recruited through collaborations with NGOs, army camps, village panchayats, and urban cooperatives. Screening camps were scheduled periodically, with each camp accommodating 90 patients or fewer.ResultsAmong the 1505 patients, most were aged 45–50 years. The number of screenings increased yearly, peaking at 441 in 2022. The BI-RADS II was the most common finding (48.77%), indicating the presence of benign lesions, while the BI-RADS 0 (32.96%) required further evaluation. Higher-risk categories (BI-RADS III, IV, V) were less common, with BI-RADS V being the rarest. Follow-up adherence was highest in the BI-RADS III, IV, and V categories, with BI-RADS V achieving 100% follow-up. However, only 320 of 496 BI-RADS 0 patients were followed up, indicating a gap in continuity of care. The overall follow-up rate was 66.89%. Compared to urban areas, rural areas demonstrated greater screening uptake but lower follow-up rates, highlighting the need for tailored interventions to improve follow-up care access, especially in rural contexts.ConclusionThis study underscores the efficacy of a mobile mammographic unit in reaching marginalized populations. Adherence to screening protocols has emerged as a linchpin for early detection, improved prognosis, and holistic public health enhancement. Addressing misconceptions surrounding mammographic screenings, especially in rural settings, is crucial. These findings call for intensified efforts in advocacy and education to promote the benefits of breast cancer screening initiatives. Future interventions should prioritize improving access to follow-up care and addressing screening to enhance breast cancer management in Jammu Province.

Accuracy of an Artificial Intelligence System for Interval Breast Cancer Detection at Screening Mammography.

Background Artificial intelligence (AI) systems can be used to identify interval breast cancers, although the localizations are not always accurate. Purpose To evaluate AI localizations of interval cancers (ICs) on screening mammograms by IC category and histopathologic characteristics. Materials and Methods A screening mammography data set (median patient age, 57 years [IQR, 52-64 years]) that had been assessed by two human readers from January 2011 to December 2018 was retrospectively analyzed using a commercial AI system. The AI outputs were lesion locations (heatmaps) and the highest per-lesion risk score (range, 0-100) assigned to each case. AI heatmaps were considered false positive (FP) if they occurred on normal screening mammograms or on IC screening mammograms (ie, in patients subsequently diagnosed with IC) but outside the cancer boundary. A panel of consultant radiology experts classified ICs as normal or benign (true negative [TN]), uncertain (minimal signs of malignancy [MS]), or suspicious (false negative [FN]). Several specificity and sensitivity thresholds were applied. Mann-Whitney U tests, Kruskal-Wallis tests, and χ2 tests were used to compare groups. Results A total of 2052 screening mammograms (514 ICs and 1548 normal mammograms) were included. The median AI risk score was 50 (IQR, 32-82) for TN ICs, 76 (IQR, 41-90) for ICs with MS, and 89 (IQR, 81-95) for FN ICs (P = .005). Higher median AI scores were observed for invasive tumors (62 [IQR, 39-88]) than for noninvasive tumors (33 [IQR, 20-55]; P < .01) and for high-grade (grade 2-3) tumors (62 [IQR, 40-87]) than for low-grade (grade 0-1) tumors (45 [IQR, 26-81]; P = .02). At the 96% specificity threshold, the AI algorithm flagged 121 of 514 (23.5%) ICs and correctly localized the IC in 93 of 121 (76.9%) cases, with 48 FP heatmaps on the mammograms for ICs (rate, 0.093 per case) and 74 FP heatmaps on normal mammograms (rate, 0.048 per case). The AI algorithm correctly localized a lower proportion of TN ICs (54 of 427; 12.6%) than ICs with MS (35 of 76; 46%) and FN ICs (four of eight; 50% [95% CI: 13, 88]; P < .001). The AI algorithm localized a higher proportion of node-positive than node-negative cancers (P = .03). However, no evidence of a difference by cancer type (P = .09), grade (P = .27), or hormone receptor status (P = .12) was found. At 89.8% specificity and 79% sensitivity thresholds, AI detection increased to 181 (35.2%) and 256 (49.8%) of the 514 ICs, respectively, with FP heatmaps on 158 (10.2%) and 307 (19.8%) of the 1548 normal mammograms. Conclusion Use of a standalone AI system improved early cancer detection by correctly identifying some cancers missed by two human readers, with no differences based on histopathologic features except for node-positive cancers. © RSNA, 2024 Supplemental material is available for this article.

ARTEMIS: An independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions

IntroductionBreast cancer, a heterogeneous disease, is influenced by multiple genetic and epigenetic factors. The majority of prognostic models for breast cancer focus merely on the main effects of predictors, disregarding the crucial impacts of gene-gene interactions on prognosis. ObjectivesUsing DNA methylation data derived from nine independent breast cancer cohorts, we developed an independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions (ARTEMIS) with an innovative 3-D modeling strategy. ARTEMIS was evaluated for discrimination ability using area under the receiver operating characteristics curve (AUC), and calibration using expected and observed (E/O) ratio. Additionally, we conducted decision curve analysis to evaluate its clinical efficacy by net benefit (NB) and net reduction (NR). Furthermore, we conducted a systematic review to compare its performance with existing models. ResultsARTEMIS exhibited excellent risk stratification ability in identifying patients at high risk of mortality. Compared to those below the 25th percentile of ARTEMIS scores, patients with above the 90th percentile had significantly lower overall survival time (HR=15.43, 95 % CI: 9.57–24.88, P=3.06 × 10-29). ARTEMIS demonstrated satisfactory discrimination ability across four independent populations, with pooled AUC3-year = 0.844 (95 % CI: 0.805–0.883), AUC5-year = 0.816 (95 % CI: 0.775–0.857), and C-index = 0.803 (95 % CI: 0.776–0.830). Meanwhile, ARTEMIS had well calibration performance with pooled E/O ratio 1.060 (95 % CI: 1.038–1.083) and 1.090 (95 % CI: 1.057–1.122) for 3- and 5-year survival prediction, respectively. Additionally, ARTEMIS is a clinical instrument with acceptable cost-effectiveness for detecting breast cancer patients at high risk of mortality (Pt = 0.4: NB3-year = 0.019, NB5-year = 0.062; NR3-year = 69.21 %, NR5-year = 56.01 %). ARTEMIS has superior performance compared to existing models in terms of accuracy, extrapolation, and sample size, as indicated by the systematic review. ARTEMIS is implemented as an interactive online tool available at http://bigdata.njmu.edu.cn/ARTEMIS/. ConclusionARTEMIS is an efficient and practical tool for breast cancer prognostic prediction.

Enhancing Breast Cancer Detection: Leveraging Convolutional Neural Networks

Abstract: Breast cancer continues to be a critical health concern, necessitating early detection and accurate classification for effective treatment. This study presents a comparative analysis between a custom-designed Convolutional Neural Network (CNN) and the pre-trained DenseNet121 model for breast cancer detection and classification. We compiled a comprehensive dataset of breast cancer images and applied appropriate preprocessing techniques to optimize the input for the models. The dataset was divided into training, validation, and testing sets to evaluate the models' performance. The CNN model comprises multiple convolutional and pooling layers followed by fully connected layers for classification, while the DenseNet121 model is fine-tuned specifically for breast cancer detection. The models were evaluated based on metrics such as accuracy, precision, recall, F1 score, and AUC-ROC. The DenseNet121 model outperformed the custom CNN model, achieving higher accuracy and reliability. For wider accessibility, we integrated the superior DenseNet121 model into a user-friendly web-based interface using Python Flask, enabling real-time breast cancer predictions. Ethical considerations were paramount, ensuring data privacy, security, and transparency in all model predictions. This study highlights the effectiveness of the DenseNet121 model and contributes to improved breast cancer diagnosis and patient care.

Relationship between Volpara Density Grade and Compressed Breast Thickness in Japanese Patients with Breast Cancer.

High breast density found using mammographs (MGs) reduces positivity rates and is considered a risk factor for breast cancer. Research on the relationship between Volpara density grade (VDG) and compressed breast thickness (CBT) in the Japanese population is still lacking. Moreover, little attention has been paid to pseudo-dense breasts with CBT < 30 mm among high-density breasts. We investigated VDG, CBT, and apparent high breast density in patients with breast cancer. Women who underwent MG and breast cancer surgery at our institution were included. VDG and CBT were measured. VDG was divided into a non-dense group (NDG) and a dense group (DG). This study included 419 patients. VDG was negatively correlated with CBT. The DG included younger patients with lower body mass index (BMI) and thinner CBT. In the DG, patients with CBT < 30 mm had lower BMI and higher VDG; however, no significant difference was noted in the positivity rate of the two groups. Younger women tend to have higher breast density, resulting in thinner CBT, which may pose challenges in detecting breast cancer on MGs. However, there was no significant difference in the breast cancer detection rate between CBT < 30 mm and CBT ≥ 30 mm.

Hybrid Adaptive Threshold Segmentation for predicting Breast Cancer

Breast cancer is a leading cause of death for women in many parts of the world. The disease is often misdiagnosed until it has progressed beyond the point of effective treatment. Therefore, early detection of the disease would aid in reducing mortality and other associated risks. Microarray gene expression data is difficult to identify and interpret, making it challenging to evaluate and choose the most relevant set of genes for use as breast cancer markers. Our research utilized Matlab 2018a and the Breast Cancer Wisconsin Diagnostic dataset to develop a mixed machine-learning model for fast breast cancer prediction. Here, we apply various machine learning techniques including random forest classification, logistic regression, support vector machine and Naive Bayes, as well as to a dataset to make predictions about their development and eventual size. The success percentage for the eXtreme Gradient Boosting classifier compared to various machine learning approaches is 99.78%. This new approach to prediction has the potential to revolutionize the detection, analysis and prognosis of breast cancer.

Pandemia da COVID-19 e seu reflexo no rastreamento do câncer de mama no Brasil

Introduction: breast cancer has been the leading cause of death among women in Brazil and worldwide. During the years of social isolation due to COVID-19, health services, including breast cancer screening, were suspended in an attempt to contain the spread of the virus, interfering with the early detection and treatment of breast cancer, effective means of reducing mortality. Objective: to identify how the COVID-19 pandemic has interfered with the therapeutic itinerary for breast cancer in Brazilian regions. Method: this is an observational, ecological study, carried out through secondary analysis of data available on the website of the Department of Informatics of the Unified Health System (DATASUS). Results: information was collected from all screening bilateral mammography exams in women aged 50 to 69 years, positive cases, and therapeutic modalities from 2018 to 2022. The data showed that the pandemic impacted Brazilian regions differently, requiring different strategies and investments to ensure access to health services and minimize the impacts of the pandemic. Conclusion: although the survey did not show statistically significant results, the southeast region stood out in the registration of cases, but shows a drop-in case registered between 2021 and 2022. On the other hand, the northern region had the lowest number of cases in all years.

A Systematic Review of Women's Knowledge and Practices Regarding Breast Self-Examination

Globally, breast cancer is the primary cause of death. Non-pharmacological therapy involving knowledge and practice of breast self-examination (BSE) is a straightforward, incredibly affordable, non-invasive initial diagnostic technique for early breast cancer diagnosis. The objective of this study is to examine women's knowledge and practices about breast self-examination. The method is a systematic review using PRISMA. Data sourced from searches in PubMed, ScienceDirect, and Google Scholar were conducted to identify relevant published studies. The data was restricted to English-language articles on women's knowledge and practice of breast self-examination published during the previous five years. The results show 15 articles that explain the knowledge and practice of breast self-examination carried out by women. The majority of women have heard of BSE but do not fail to carry out BSE regularly every month. The majority of evaluated studies demonstrated a notable correlation between participants' knowledge of breast cancer and their age, marital status, and level of education. The conclusion is that women's knowledge and practice of breast self-examination are still lacking. Hence, it is imperative to develop techniques that might enhance the awareness and adoption of breast self-examination. Although breast self-examination is generally part of a prevention and monitoring strategy, it does not replace medical or pharmacological care. The contribution of this study is to provide information that breast self-examination has several important contributions in the context of breast health and early detection of breast cancer.

Predicting Breast Self-Examination Practices among Catholic Nuns in Tanzania's Lake Zone: A Health Belief Model Approach

Background: Early breast cancer detection is crucial for improving breast cancer outcomes. Breast Self-Examination (BSE) is a valuable tool that can empower Catholic nuns to take charge of their health, particularly in constrained resource countries like Tanzania. This study aims to use the Health Belief Model (HBM) to predict SBE practices among Catholic nuns in Tanzania's Lake Zone. Methods: This study employed a cross-sectional design whereby a total of 385 catholic nuns were enrolled to participate in the study. The Health Beliefs Model (HBM) guided the study of the interrelated variables related to self-breast examination. Data was collected using a self-administered questionnaire and analyzed using Stata version 18.0. Bivariate and multivariate logistic regression was used to test for association at a 5% significant level. Results: The prevalence of non-performance self-breast examination was 64.2% (95% CI, 59.1%-69.0%). The multivariate logistic regression revealed that nuns who are in the non-health field were more likely not to perform self-breast cancer examination with an AOR of 1.67 (95% CI, 1.02-2.73, p=0.041). Likewise, nuns with high barrier were more likely not to perform self-breast cancer examination with an AOR of 1.88 (95% CI, 1.17-3.02, p=0.009), also, an AOR of 2.25 (95% CI, 1.39-3.65, p=0.001) for nuns with low self-efficacy. Conclusion: The study revealed that the HBM constructs can predict the SBE behavior of catholic nuns. In addition, educational health interventions are highly recommended to enhance SBE practice among this special group.

Application of AI in detection of breast cancer with laboratory results monitoring

Breast cancer is one of the most common types of cancer among women worldwide, therefore an early and precise process of diagnostics plays an important role in improving the prognosis and outcome of treatment. The application of artificial intelligence (AI) allows faster and more precise analysis of medical imaging, which contributes to the early detection of tumors and lowers the number of false-negative results. This review article analyzed 60 scientific papers and using the most recent findings about this topic, searched for AI implementation in breast cancer research and how AI may improve overall survival outcomes for breast cancer patients.

Inception ResNet v2 for Early Detection of Breast Cancer in Ultrasound Images

Breast cancer is one of the leading causes of death in women. Early detection through breast ultrasound images is important and can be improved using machine learning models, which are more accurate and faster than manual methods. Previous research has shown that the use of the CNN (Convolutional Neural Network) algorithm in breast cancer detection still does not achieve high accuracy. This study aims to improve the accuracy of breast cancer detection using the Inception ResNet v2 transfer learning method and data augmentation. The data is divided into training, validation and testing data consisting of 3 classes, namely Benign, Malignant and Normal. The augmentation process includes rotation, zoom, and rescale. The model trained using CNN and Inception ResNet v2 showed good performance by producing the highest accuracy of 89.72% in the training data evaluation data and getting 90% accuracy in the prediction test stage with data testing. This study shows that the combination of data augmentation and the Inception ResNet v2 architecture can improve the accuracy of breast cancer detection in CNN models.