Early Detection Of Breast Cancer Research Articles

Clinicopathological and Ultrasonographic Characteristics of Breast Cancer in Young Women.

Purpose: The early detection of breast cancer in women under the age of 40 has posed significant challenges. This can be attributed in part to the limited research conducted on the breast cancer in this age group, particularly with regards to large sample sizes. We aimed to address this gap by analyzing and comparing the ultrasound imaging and pathological characteristics of breast cancer in women aged under 40 and those aged 40 and above. Methods: A retrospective assessment was conducted to examine the ultrasound imaging and clinicopathologic characteristics of 555 women with surgically confirmed breast cancers. The patient cohort consisted of 160 individuals below the age of 40 and 395 individuals aged 40 years and above. Results: Our study identified the breast cancer in patients under 40 years was more likely to show regular shape (p = 0.043) compared with tumors in patients who were 40 years and over. Furthermore, in young female patients (<40 years), irregular shape was correlated with the HER2-enriched type (p = 0.02), circumscribed margin (p = 0.001), and a lack of calcifications (p = 0.02) were associated with the triple-negative type. In another group (≥40 years), only a lack of calcifications (p = 0.003) were associated with the triple-negative type. Conclusion: Breast cancer in women under the age of 40 exhibits distinct ultrasonographic characteristics patterns that vary across different immunophenotypes, which may provide certain predictive information for physicians.

Assessing the Health Belief Model of Students with Regard to Breast Cancer: A Descriptive Study

Background: Breast cancer continues to be a substantial worldwide health issue, impacting millions of individuals and families annually. Gaining insight into individuals' perception and reaction to the risk of breast cancer is crucial for advancing early detection and successful prevention methods. The Health Belief Model (HBM) is a commonly utilised theoretical framework for examining health behaviours associated with breast cancer. According to the Health Belief Model (HBM), individuals are more inclined to take measures to prevent or manage a health threat if they believe that they are at risk of developing the condition, perceive the condition to have severe consequences, believe that taking action would be advantageous in reducing the threat, and perceive minimal obstacles to taking action. The study seeks to evaluate the Health Belief Model of students in relation to breast cancer. Methods: A descriptive research design was adopted. Self-administered semi-structured questionnaires were used to elicit information from 365 female participants. Purposive sampling approach was used to choose Tamale Girls and Ghana Senior High Schools from the Tamale Metropolis and simple random technique was used to identify the study respondents. Data was analysed using SPSS. Statistics like frequency and percentages were used to represent the findings. Results: The study revealed that students possessed a moderate-to-high level of information regarding the causes, effects, and preventions of breast cancer. This knowledge was mostly influenced by the type or category of learning. Overall, a minimal number of students participated in the screening process for breast cancer. Among the three techniques, breast self-examination was the most commonly utilised, followed by mammography and clinical breast examination. Factors such as age, family size, religion, and social class were found to be associated with breast cancer screening. Conclusion: Students generally exhibited a modest degree of perceived vulnerability to breast cancer. They also demonstrated a favourable disposition towards the advantages of breast cancer prevention and early detection measures.

Revolutionizing breast ultrasound diagnostics with EfficientNet-B7 and Explainable AI

Breast cancer is a leading cause of mortality among women globally, necessitating precise classification of breast ultrasound images for early diagnosis and treatment. Traditional methods using CNN architectures such as VGG, ResNet, and DenseNet, though somewhat effective, often struggle with class imbalances and subtle texture variations, leading to reduced accuracy for minority classes such as malignant tumors. To address these issues, we propose a methodology that leverages EfficientNet-B7, a scalable CNN architecture, combined with advanced data augmentation techniques to enhance minority class representation and improve model robustness. Our approach involves fine-tuning EfficientNet-B7 on the BUSI dataset, implementing RandomHorizontalFlip, RandomRotation, and ColorJitter to balance the dataset and improve model robustness. The training process includes early stopping to prevent overfitting and optimize performance metrics. Additionally, we integrate Explainable AI (XAI) techniques, such as Grad-CAM, to enhance the interpretability and transparency of the model’s predictions, providing visual and quantitative insights into the features and regions of ultrasound images influencing classification outcomes. Our model achieves a classification accuracy of 99.14%, significantly outperforming existing CNN-based approaches in breast ultrasound image classification. The incorporation of XAI techniques enhances our understanding of the model’s decision-making process, thereby increasing its reliability and facilitating clinical adoption. This comprehensive framework offers a robust and interpretable tool for the early detection and diagnosis of breast cancer, advancing the capabilities of automated diagnostic systems and supporting clinical decision-making processes.

Epigenetic Drug Interventions in Breast Cancer: A Narrative Review of Current Research and Future Directions

Breast cancer is a life-threatening disease known for its extensive molecular heterogeneity. The study of the breast cancer epigenome has revealed potential avenues for improving breast cancer treatment risk stratification, early detection, and treatment. With renewed interest in epigenetic-modifying pharmaceutical agents, namely DNA methyltransferase inhibitors (DNMTi), histone deacetylase inhibitors (HDACi), bromodomain and extra-terminal inhibitors (BETi), and enhancer of zeste homolog 2 inhibitors (EZH2i), there have been extensive preclinical and clinical studies to evaluate the safety and efficacy of these agents as potential treatments for breast cancer. In this review, we summarise and present the preclinical and clinical evidence for epigenetic drugs in treating breast cancer. We review the challenges associated with the translation of these findings into improved patient outcomes, namely the optimisation of dosage and treatment regimens, and the emergence of resistance. These challenges have been widely recognised in the field and are of utmost importance for the successful implementation of personalised medicine. While there is strong evidence that epigenetic alterations, consisting of changes in DNA methylation, histone modifications, and non-coding RNAs, play a crucial role in breast cancer initiation and development, additional research is warranted to elucidate the safety profile of long-term interventions involving epigenetic drugs and to validate the role of epigenetic markers in disease diagnosis, prognosis, and personalised treatment.

Value of Ultrasonography in Assessment of Screen-Detected Calcified Ductal Carcinoma In Situ: A Clinical Audit

Abstract Background: Approximately 80% of ductal carcinoma in situ (DCIS) cases are asymptomatic and manifest as microcalcifications, usually detected on screening mammograms. Stereotactic biopsy is used as the primary modality for histopathologic diagnosis. Ultrasonography has been relied upon as a potentially viable alternative for early breast cancer detection, especially in low- and medium-income countries. Aim: Determine the role and reliability of breast ultrasonography in the evaluation of screen-detected microcalcifications using histologically proven calcified DCIS as a case study. Materials and Methods: Cross-sectional evaluation of records of patients recalled for M3–M5 (indeterminate to malignant) type microcalcifications on screening mammograms, with a histological diagnosis of pure DCIS, in an NHS Trust Hospital, Kent, England, from March 2021 to April 2023. Results: Eighty women included were within the age range of 46–79 years, with mean age of 60 years. Background breast density was predominantly fatty in 51/80 (63.8%), without any correlation with mammographic abnormalities. Microcalcifications were classified as indeterminate (M3) in 42/80 (52.5%), while suspicious (M4) and malignant (M5) types were seen in 38/80 (47.5%). No targeted sonographic abnormalities were noted in 69 (86.2%) of the patients, while 11 (13.8%) had sonographic abnormalities. The predominant sonographic feature was non-mass hypoechogenicity in 6/11(55%). There was a correlation between mammographic code, lesion size, and ultrasonic abnormality, with 9/11 (81.8%) patients with sonographic lesions having suspicious and malignant type calcifications. The mean mammographic lesion size was significantly greater in women with abnormal ultrasound findings, 42 vs. 20 mm. Histological tumour grade was high grade in 10/11 (90.9%) lesions. Conclusion: The accuracy of breast ultrasonography as an adjunct in the detection of screen-detected microcalcifications and subsequent guided biopsy is higher when dealing with malignant type microcalcifications &gt;15 mm in size.

Community Pharmacists' Readiness for Breast Cancer Mammogram Promotion: A National Survey from Jordan.

Mammography is the gold standard screening technique for early detection of breast cancer. This study aimed to assess the knowledge of community pharmacists of different aspects emphasized by the JBCP programs. This study also identifies the attitudes and barriers towards promoting early detection services. This study was a cross-sectional survey of community pharmacists in Jordan. Pharmacists were randomly selected and asked to complete an electronic questionnaire. Inclusion criteria: a pharmacist with a bachelor's degree or higher and registered at the JPA working in a community pharmacy. The questionnaire included demographic and socioeconomic information, knowledge, attitudes towards breast cancer screening mammography services, and barriers towards participation in the promotion of these services. A total of 1,088 community pharmacists were approached, 1,000 (91.8%) completed the questionnaire. Participants had an average age of 34 years ± 10.8 and average experience of 9.1 ± 9.5 years. Only 48 (37.8%) of the female pharmacists aged 40 years or older underwent a mammogram. Knowledge of symptoms of breast cancer was the highest with a score of 755, followed by knowledge of risk factors (670) and finally early detection of breast cancer (540). Many barriers were reported by the community pharmacists including lack of educational materials and time constraints. Pharmacists with higher educational levels (p<0.001), of female gender (p<0.001), attended continuous cancer-related education (p<0.001), encountered a higher percentage of female customers (p<0.001), in a certain geographic location (p=0.003), underwent mammography (p=0.014), and encountered high frequency of inquiries on mammogram by the customers (p<0.001) were all associated with higher knowledge scores. Despite the reported barriers and insufficient knowledge in certain aspects of early detection of breast cancer, community pharmacists have positive attitudes and can be a valuable asset for awareness-raising efforts.

Socialization and Early Detection of Breast Cancer Using the Android Application “MamoApp”

Cancer is a public health problem in Indonesia with a prevalence of 136.2/100,000 population. One of the most common cancers, especially in women, is breast cancer. The highest incidence of breast cancer in women is 42.1 per 100,000 population with an average death rate of 17 per 100,000 population. One of the countermeasures for breast cancer is case finding with early detection carried out through clinical breast examination (CBE) and breast self-examination (SADARI). One of the features developed by Postgraduate students of the Institute of Health Sciences STRADA Indonesia is a breast cancer early detection application called “MamoApp”. In this community service activity, 2 aspects of activities will be carried out in the Dharma Wanita group of DLHKP office in Kediri city. The first activity is to conduct a socialization in the form of providing health counseling to the Dharma Wanita group about SADARI techniques, the second activity will be carried out early detection of breast cancer using the MamoApp android application. Mamoapp application contains an educational video on awareness examination, then respondents fill out a questionnaire to detect the presence or absence of symptoms of breast cancer, the time used for filling in is approximately 10 minutes. From the results of filling out the questionnaire through the MamoApp application, we will immediately know the conclusion of the examination results, namely: “Indicated Cancer” or ‘Not Indicated Cancer’, so that participants can immediately find out whether the person concerned is indicated by breast cancer or not. From the results of the questionnaire of 38 participants, there were 2 participants who indicated breast cancer and 36 participants did not indicate breast cancer. Participants who indicated breast cancer by the facilitator were advised to do a further examination to the doctor for a more detailed examination.

Quality Assessment of YouTube Videos As Information Source for Breast Self-Examination.

Introduction Breast self-examination (BSE) is essential for early detection of breast cancer to lower the disease's morbidity and death rate. Education about the proper application reinforces its effectiveness. YouTube is an emerging modality for education distribution. Thus, we aimed to evaluate the quality and reliability of BSE videos on YouTube. Materials and methods A web search of YouTube was conducted using the term "breast self-examination". The first 50 relevant videos found through this search were compiled and evaluated. Video reliability was evaluated by applying benchmark criteria from the Journal of the American Medical Association (JAMA). The educational quality of the videos was evaluated using the Global Quality Score (GQS) and the guidelines' comprehensiveness score for BSE-specific instructions. Results The mean number of views was 311,625.9. Medical sources were the most common upload sources, which were found in 60% of the analyzed videos (30 videos), while examination demonstration was the most common type of video content (33 videos, 66%), followed by examination information (15 videos, 30%). However, a significant association was found between videos containing both examination information and demonstration and better educational quality. Regarding video reliability, 34% of videos (17 videos) scored 0, and only 2% (one video) scored four. According to the GQS, only 8% (four videos) were of excellent quality, while the majority (20 videos, 40%)were of suboptimal quality. Based on the BSE comprehensiveness score, the mean score was seven out of nine. Conclusions Videos containing examination information and demonstrations showed the best educational quality. Although most of the YouTube videos of BSE showed a high comprehensiveness score for BSE-specific instructions, their JAMA reliability and GQS scores were poor.

Deep Location Soft-Embedding-Based Network With Regional Scoring for Mammogram Classification.

Early detection and treatment of breast cancer can significantly reduce patient mortality, and mammogram is an effective method for early screening. Computer-aided diagnosis (CAD) of mammography based on deep learning can assist radiologists in making more objective and accurate judgments. However, existing methods often depend on datasets with manual segmentation annotations. In addition, due to the large image sizes and small lesion proportions, many methods that do not use region of interest (ROI) mostly rely on multi-scale and multi-feature fusion models. These shortcomings increase the labor, money, and computational overhead of applying the model. Therefore, a deep location soft-embedding-based network with regional scoring (DLSEN-RS) is proposed. DLSEN-RS is an end-to-end mammography image classification method containing only one feature extractor and relies on positional embedding (PE) and aggregation pooling (AP) modules to locate lesion areas without bounding boxes, transfer learning, or multi-stage training. In particular, the introduced PE and AP modules exhibit versatility across various CNN models and improve the model's tumor localization and diagnostic accuracy for mammography images. Experiments are conducted on published INbreast and CBIS-DDSM datasets, and compared to previous state-of-the-art mammographic image classification methods, DLSEN-RS performed satisfactorily.

Proteomic Profiling of Serum of Women with Bi-Rads 1 to 5: Identification of Potential Complementary Biomarkers for Early Detection of Breast Cancer

Proteomic Profiling of Serum of Women with Bi-Rads 1 to 5: Identification of Potential Complementary Biomarkers for Early Detection of Breast Cancer

Sociodemographic drivers influencing the adoption of Breast Self-Examination amongst women in Kaduna State, Northern Nigeria

Breast cancer is a significant public health concern globally, with a disproportionate impact on women in developing countries such as Nigeria. Early detection through practices like Breast Self-Examination (BSE) is crucial for improving treatment outcomes and survival rates. Breast self-examination is a valuable tool for early breast cancer detection among Nigerian women. This paper examines the current state of BSE practice among Nigerian women, identifying factors influencing its uptake and exploring strategies for enhancing its implementation. A cross-sectional research study was conducted to assess the socio demographic determinants of uptake of breast cancer screening amongst women in Kaduna state. This study which sampled a total of 254 respondents, out of which a larger proportion (48.8%) of the respondents were between the ages of 25-34 years, reveals that 80.3% of respondents have heard about breast cancer. Amongst screening methods known, BSE is most prevalent with 77.5% (203) and 22% (57) of respondent knowing about BSE and mammography respectively with about 68% being aware of how to conduct Breast Self-Examination. A statistical significance was found between constructs of three selected socio-demographic characteristics - Age (p=0.027), level of education (p=0.000), and health insurance (p=0.000) significantly associated with women’s adoption of breast self-examination. Improving access to quality healthcare, including breast cancer screening and counselling services, is essential to accelerating adoption of BSE. Addressing the complex factors influencing BSE practice requires a comprehensive approach that combines education, community engagement, and reducing the financial barrier to accessing mammography services.

Circulating miR-30b-5p Level in Plasma as a Potential Biomarker for Early Detection of Breast Cancer

Circulating miR-30b-5p Level in Plasma as a Potential Biomarker for Early Detection of Breast Cancer

ViT-MAENB7: An innovative breast cancer diagnosis model from 3D mammograms using advanced segmentation and classification process

Tumors are an important health concern in modern times. Breast cancer is one of the most prevalent causes of death for women. Breast cancer is rapidly becoming the leading cause of mortality among women globally. Early detection of breast cancer allows patients to obtain appropriate therapy, increasing their probability of survival. The adoption of 3-Dimensional (3D) mammography for the medical identification of abnormalities in the breast reduced the number of deaths dramatically. Classification and accurate detection of lumps in the breast in 3D mammography is especially difficult due to factors such as inadequate contrast and normal fluctuations in tissue density. Several Computer-Aided Diagnosis (CAD) solutions are under development to help radiologists accurately classify abnormalities in the breast. In this paper, a breast cancer diagnosis model is implemented to detect breast cancer in cancer patients to prevent death rates. The 3D mammogram images are gathered from the internet. Then, the gathered images are given to the preprocessing phase. The preprocessing is done using a median filter and image scaling method. The purpose of the preprocessing phase is to enhance the quality of the images and remove any noise or artifacts that may interfere with the detection of abnormalities. The median filter helps to smooth out any irregularities in the images, while the image scaling method adjusts the size and resolution of the images for better analysis. Once the preprocessing is complete, the preprocessed image is given to the segmentation phase. The segmentation phase is crucial in medical image analysis as it helps to identify and separate different structures within the image, such as organs or tumors. This process involves dividing the preprocessed image into meaningful regions or segments based on intensity, color, texture, or other features. The segmentation process is done using Adaptive Thresholding with Region Growing Fusion Model (AT-RGFM)”. This model combines the advantages of both thresholding and region-growing techniques to accurately identify and delineate specific structures within the image. By utilizing AT-RGFM, the segmentation phase can effectively differentiate between different parts of the image, allowing for more precise analysis and diagnosis. It plays a vital role in the medical image analysis process, providing crucial insights for healthcare professionals. Here, the Modified Garter Snake Optimization Algorithm (MGSOA) is used to optimize the parameters. It helps to optimize parameters for accurately identifying and delineating specific structures within medical images and also helps healthcare professionals in providing more precise analysis and diagnosis, ultimately playing a vital role in the medical image analysis process. MGSOA enhances the segmentation phase by effectively differentiating between different parts of the image, leading to more accurate results. Then, the segmented image is fed into the detection phase. The tumor detection is performed by the Vision Transformer-based Multiscale Adaptive EfficientNetB7 (ViT-MAENB7) model. This model utilizes a combination of advanced algorithms and deep learning techniques to accurately identify and locate tumors within the segmented medical image. By incorporating a multiscale adaptive approach, the ViT-MAENB7 model can analyze the image at various levels of detail, improving the overall accuracy of tumor detection. This crucial step in the medical image analysis process allows healthcare professionals to make more informed decisions regarding patient treatment and care. Here, the created MGSOA algorithm is used to optimize the parameters for enhancing the performance of the model. The suggested breast cancer diagnosis performance is compared to conventional cancer diagnosis models and it showed high accuracy. The accuracy of the developed MGSOA-ViT-MAENB7 is 96.6 %, and others model like RNN, LSTM, EffNet, and ViT-MAENet given the accuracy to be 90.31 %, 92.79 %, 94.46 % and 94.75 %. The developed model's ability to analyze images at multiple scales, combined with the optimization provided by the MGSOA algorithm, results in a highly accurate and efficient system for detecting tumors in medical images. This cutting-edge technology not only improves the accuracy of diagnosis but also helps healthcare professionals tailor treatment plans to individual patients, ultimately leading to better outcomes. By outperforming traditional cancer diagnosis models, the proposed model is revolutionizing the field of medical imaging and setting a new standard for precision and effectiveness in healthcare.

Comparative Analysis of Artificial Intelligence Algorithms for Breast Cancer Detection from Pathological Image in Burkina Faso.

Artificial Intelligence (AI) has revolutionized many fields, including medical imaging. This revolution has enabled the digitization of medical images, the development of algorithms allowing the use of data captured in natural language, and deep learning, enabling the development of algorithms for automatic processing of medical images from massive medical data. In Burkina Faso, early and accurate detection of breast cancer is a significant challenge due to limited resources and lack of specialized expertise. In this article, we examine the effectiveness of different artificial intelligence algorithms for breast cancer detection from pathological image.

Multiplex methylation detection assays using a blocking FRET probe with machine learning-assisted quantitative melting curve method targeting early-stage breast cancer

Breast cancer (BC) is the second most prevalent form of cancer, and poses a significant threat to public health. DNA methylation is an ideal marker for the early detection of BC. Fluorescence quantitative polymerase chain reaction (PCR)-based DNA methylation detection is simpler and faster but is constrained by its multiplexing capability and specificity. To address this, we developed a multiplex quantitative methylation PCR assay for the simultaneous analysis of methylation status at multiple sites specific to BC (cg11754974, cg13828440, cg18637238, and cg16652347). The machine learning model was trained using 1200 cases of multipeak data to enhance the melting curve resolution. Performance testing demonstrated the method’s ability to selectively amplify methylated genes at a DNA concentration of 1 × 105 copies μL−1, with high replicability (coefficient of variation <5 %) and mutation detection capabilities as low as 10 %. When applied to 80 clinical BC samples, the assay effectively distinguished patients with early-stage BC from normal controls, achieving an area under the curve of 0.8938, sensitivity of 83.33 %, and specificity of 84.62 %. Our essay exhibits superior clinical performance when compared to the quantitative methylation-specific PCR assays for noninvasive detection of early-stage BC, which is poised to become a favorable clinical diagnostic method for early-stage BC owing to its simplicity, speed, and capacity to improve diagnostic accuracy.

A reliable breast cancer diagnosis approach using an optimized deep learning and conformal prediction

Accuracy of breast cancer detection using histopathological images is mainly subject to the expertise of physicians. For this reason, there are certain levels of uncertainty during the decision making of breast cancer diagnosis. To increase the certainty of physicians’ decisions, computer-aided medical systems can be used as additional knowledge for diagnosis from histopathological images. Early detection of breast cancer contributes greatly to the survival rate of the patients, which is essentially dependent on the analysis of breast images. In the literature review, few studies have been proposed for breast cancer classification based on histopathological images. However, the performance of current methods is still inefficient and they are incapable for extracting important features from the images due to ineffective hyper-parameters optimization. In this paper, a reliable deep learning approach for breast cancer diagnosis is proposed using a random search algorithm and DenseNet-121-based transfer-learning model. For the transfer learning technique, the pre-trained DenseNet-121 model is adopted due to its capability to strengthen the feature representation and reduce the number of trainable parameters. The use of random search optimization finds the best combinations of hyper-parameters values to improve the accuracy of the classification approach. Several experiments are conducted on a benchmark public dataset of histopathological images with different magnification factors: 40×, 100×, 200×, and 400×. The experiments showed that the proposed approach achieved 98.96 %, 97.62 %, 97.08 %, and 96.42 % of accuracy for 40×, 100×, 200×, and 400× test images, respectively, demonstrating its effectiveness compared to the state-of-the-art models and methods. The reliability of proposed approach is achieved by quantifying the uncertainty of model outcomes using conformal prediction method, guarantying user-chosen levels of confidence.

Real-time thermography for breast cancer detection with deep learning

In this study, we propose a framework that enhances breast cancer classification accuracy by preserving spatial features and leveraging in situ cooling support. The framework utilizes real-time thermography video streaming for early breast cancer detection using Deep Learning models. Inception v3, Inception v4, and a modified Inception Mv4 were developed using MATLAB 2019. However, the thermal camera was connected to a mobile phone to capture images of the breast area for classification of normal and abnormal breast. This study’s training dataset included 1000 thermal images, where a FLIR One Pro thermal camera connected to a mobile device was used for the imaging process. Of the 1000 images obtained, 700 images were considered for the normal breast thermography class while the 300 images were suitable for the abnormal class. We evaluate Deep Convolutional Neural Network models, such as Inception v3, Inception v4, and a modified Inception Mv4. Our results demonstrate that Inception Mv4, with real-time video streaming, efficiently detects even the slightest temperature contrast in breast tissue sequences achieving a 99.748% accuracy in comparison to a 99.712% and 96.8% for Inception v4 and v3, respectively. The use of in situ cooling gel further enhances image acquisition efficiency and detection accuracy. Interestingly, increasing the tumor surface temperature by 0.1% leads to an average 7% improvement in detection and classification accuracy. Our findings support the effectiveness of Inception Mv4 for real-time breast cancer detection, especially when combined with in situ cooling gel and varying tumor temperatures. In conclusion, future research directions should focus on incorporating thermal video clips into the thermal images database, utilizing high-quality thermal cameras, and exploring alternative Deep Learning models for improved breast cancer detection.

KRC-APM: Key region cutting and artificial prior model for breast cancer recognition in ultrasound images

Computer-aided analysis of ultrasound images is important for the early detection of breast cancer, obtaining more treatment time and improving the likelihood of survival. Current methods are either limited by predefined Regions of Interest (ROIs), suffer from a low proportion and variable locations of tumors, or lack appropriate modeling of tumor characteristics in ultrasound images. This paper proposes a framework called Key Region Cutting and Artificial Prior Model (KRC-APM) for breast cancer recognition in ultrasound images. Firstly, to avoid the reliance on predefined ROIs, as well as to increase the proportion of tumor areas and eliminate abundant background, we propose a Key Region Cutting (KRC) method, which analyzes the confidence levels for tumor areas and tolerantly cuts high-confidence areas into key regions. Next, to model the characteristics of tumors in ultrasound images, we design three types of diagnosis-related Artificial Priors (APs) under the guidance of experienced oncologists: Shape Modeling, Margin Analysis, and Echogenicity Pattern Indication. Finally, to fuse the key region with the extracted APs, we propose an Artificial Prior Model (APM), which synthetically learns the pattern of breast cancer. The proposed KRC-APM is validated on two public datasets. The experimental results demonstrate that the key regions identified by the KRC method and the designed APs significantly enhance the performance of breast cancer recognition. Furthermore, the proposed KRC-APM framework outperforms current state-of-the-art (SOTA) methods.

Using EMG to Better Understand Patient Pain and Stress During Routine Mammography

Since breast cancer is the most common cancer in women, it is recommended that women over the age of 40 get annual mammogram screenings. Regular mammograms allow for early breast cancer detection, and in turn, proper treatment and improved patient prognosis. However, the stress and discomfort associated with the mammogram procedure deter many women from routine screening. Most previous work attempting to characterize mammogram-related pain utilizes subjective, questionnaire-based methods. A more objective approach is needed to fully understand the stress and pain experienced by patients during the mammogram procedure. In recent years, bio signals such as surface electromyography (sEMG) have been used to quantify various physiological states including stress and pain. Advocating women’s health and early breast cancer detection, the Human-User Interaction Lab is the first research lab to use sEMG to quantitatively measure the stress and pain experienced by patients during mammography compressions.

Multimodal analysis of methylation and fragmentomic profiles in plasma cell free DNA for differentiation of benign and malignant breast tumors.

7 Background: Breast cancer ranks as the second leading cause of cancer-related mortality among women globally. Early detection of breast cancer is crucial for improving patient outcomes and reducing mortality rates. Liquid biopsy, which relies on circulating tumor DNA (ctDNA) shed by breast tumors into the bloodstream, presents a promising non-invasive approach for early breast cancer detection. However, accurately distinguishing between benign breast abnormalities and malignant tumors remains a significant clinical challenge, as misdiagnosis can lead to unnecessary invasive procedures. Methods: Herein, we employed a multimodal analysis approach, namely SPOT-MAS (Screen for the Presence of Tumor by DNA Methylation and Size), to profile alterations in methylation and fragment length patterns of cell-free DNA (cfDNA) from 169 breast cancer-confirmed patients and 99 patients diagnosed with benign breast lumps including cysts, fibroadenomas, and fibrocystic changes. A robust machine learning model was constructed using these signatures to differentiate between breast cancer patients and individuals with benign lesions. Results: Our genome-wide analyses identified distinct profiles of methylation changes, copy number alterations, and end motifs in cfDNA, enabling discrimination between breast cancer patients and individuals with benign conditions. Notably, we observed a significant enrichment of Thymine and Adenine at cfDNA cleavage sites in breast cancer patients. Moreover, our target sequencing analyses uncovered distinct methylation patterns in the regulatory regions of multiple genes, including hypermethylation in GPR126 or hypomethylation in TOP1 or MAFB in breast cancer cfDNA. Our multi-featured model achieved an AUC of 0.92 (95% CI: 0.88–0.97), a specificity of 97.44% and sensitivities of 57.14% and 61.70% for stage I and stage II–III patients, respectively. Furthermore, our multimodal assay effectively differentiated multiple molecular subtypes of breast tumors from benign lesions, achieving the highest sensitivity of 71.43% with Luminal A, followed by Luminal B, Triple Negative Breast Cancer (TNBC), Luminal B-HER2, and HER2 with sensitivities of 66.67%, 60%, 58.33% and 55.56%, respectively. Conclusions: Our findings demonstrate the potential of cancer-specific methylation and fragmentomic patterns in plasma cfDNA as novel biomarkers for accurately discriminating between breast cancer and benign lesions. This capability reduces the false-positive rate and helps avoid unnecessary biopsies in current clinical practice.