Breast Cancer Screening Methods Research Articles

Perceived barriers and factors influencing uptake of breast cancer screening among women: a population-based cross-sectional study

Breast cancer (BC) screening plays a major role in the prevention of BC through early detection and timely treatment. This study aims to determine the level of uptake of BC screening and associated factors. A community-based analytical cross-sectional study was conducted in Dodoma City, Tanzania from July to December 2020. The study included women aged 8 years and above without a known history of breast cancer. Multivariable logistic regression was used to determine the socio-demographic factors associated with BC screening. P value < 0.05 was considered significant. A total of 354 study participants were included in the present study. The mean age of participants was 31.0 ± 11.8 years. The majority of study participants (67.5%, n = 239) were aware of BC screening. However, only (35.3%, n = 125) reported to have ever practised BC screening. Breast self-examination was the most (16.4%, n = 58) frequently used method for BC screening among study participants. Lack of knowledge of all methods of BC screening was the barrier that was perceived by the vast majority (60.2%, n = 213) of the study participants. Having low family income was the only predictor of failure to practice BC screening. In this study, most of the women were aware of BC, however, few of them had undergone breast cancer (BC) screening at the time of the interview. The study also found that the main barrier to BC screening was the lack of knowledge about BC among the study participants. Immediate measures are necessary to increase women's awareness of BC. Such as community sensitization on the importance of screening, can help improve the uptake of BC screening and the early detection of BC.

Exploring breast cancer screening fear through a psychosocial lens.

Breast cancer screening is crucial for early detection and treatment. Yet, underutilization persists due to various psychosocial factors. This manuscript delves into the multifaceted fears that hinder screening adherence. The literature provides a framework categorizing breast cancer screening fears into generalized cancer fear, fear of screening components, and fear of screening outcomes. In this review, we explore fear of screening components (concerns regarding radiation, discomfort, and pain) and fear of screening outcomes (disability and mortality apprehension, treatment fears, obligation anxiety, and financial concerns) as undesirable, and potentially addressable, aspects of breast cancer screening fear. False-positive results exacerbate these anxieties, prolonging distress and impacting patients' lives beyond the screening process. Addressing these concerns requires reframing current screening approaches to prioritize patient comfort, cultural sensitivity, and accessibility. To address current psychosocial challenges in breast cancer screening, this manuscript advocates for modifying breast cancer screening methods to improve adherence and patient well-being.

Abstract PO5-19-11: A novel portable breast cancer screening method - initial institutional experience

Abstract Background: Breast ultrasound (US) is a remarkable supplementary tool alongside mammography and magnetic resonance imaging (MRI) for the detection of breast lesions. US imaging is a fast, reliable method to identify and localize breast pathology. Innovative technologies that aim to timely identification and prompt diagnosis of suspicious lesions in breast cancer screening remain a clinical need. ATUSA is a B-type rechargeable US, shaped as a wearable device. It uses a linear array of transducers with water acting as a coupling medium that recreates a 3-dimensional image of the scanned breast. The ATUSA portability makes this device a practical breast screening method during a healthcare office visit. We report the initial feasibility of ATUSA to detect breast lesions in an outpatient setting from a trial in progress. Methodology: Interim prospective data from 35 female patients presenting with a mass of equal or greater than 1 cm in size, with a baseline handheld US image and pathology reports that evidenced a cancer diagnosis or a lesion suspicious of malignancy. Breast cancer types included invasive ductal carcinoma (IDC), ductal carcinoma in situ (DCIS) and invasive lobular carcinoma (ILC), invasive mammary carcinoma (IMC) with mixed ductal and lobular features and atypical lobular hyperplasia (ALH). ATUSA and baseline handheld reference US images were cross-referenced from June 2022 to June 2023. Basic demographics, clinical characteristics, mammogram and/or MRI, and pathology reports were included in the analysis. Patients were followed up for a 30-day period to note occurrence of any adverse event (AE). Institutional Review Board (IRB) approved this study (protocol H-42616). Results: The 35 patients had a mean age of 57 years (range: 25 – 79), mean BMI 29.6 kg/m2 (range: 18.1 – 49.3). Mammographic breast density was fibroglandular tissue 15 (42.8%), followed by heterogeneously dense (HD) 13 (3.7%), mixed HD and fibroglandular 1 (2.8%), mildly dense 1 (2.8%), extremely dense 2 (5.7%), and extremely fatty 1 (2.8%). Breast sizes were 1 (2.8 %) A cup, 11 (31.4%) B cups, 1 (2.8%) B – C cups, 5 (14.2%) C cups, 1 (2.8%) C – D cups, 13 (37.1%) D cups, and 3 (8.5%) DD cups. The 35 scanned lesions had a diagnosis (compared with final resected pathology) classified as IDC 18 (51.4 %), DCIS 2 (5.7 %), mixed IDC and DCIS 6 (17.1 %), ILC 3 (8.5 %), IMC with mixed features 2 (5.7 %), IMC with DCIS 1 (2.8 %), ALH 1 (2.8 %), and 2 (5.7 %) benign masses. ATUSA successfully identified 17/35 (48.5 %) lesions from this group as malignant, from which 4 were hard to distinguish due to shadowing artifact and 3 were underestimated in size. A total of 15 (42.8 %) malignant lesions were missed by ATUSA. No AEs related to the ATUSA device were reported. Conclusion: The ATUSA provides the means to produce a good quality US image during a clinic visit or where a facility lacks conventional US imaging. When compared to the handheld US, ATUSA can be safely used in the outpatient setting without the need of trained specialists but requires further development and testing to match conventional breast imaging. Table 1. Basic clinical characteristics &amp; demographics Citation Format: Margarita Riojas-Barrett, Jessica Montalvan, Mary Bajomo, Ivan Marin, Quan Dang Nguyen, Karla A Sepulveda, Elizabeth Bonefas, Alastair Thompson, Stacey Carter. A novel portable breast cancer screening method - initial institutional experience [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-19-11.

Abstract PO2-13-12: Serum Thioredoxin 1 Enhances Breast Cancer Detection in Mammography and Breast Ultrasound, Including Dense Breasts

Abstract Background: Breast cancer (BC), when detected in its early stages, generally carries a more favorable prognosis than other types of cancer. Currently, the screening and early detection of breast cancer rely predominantly on image-oriented diagnostic methods. However, these methods inherently fall short in capturing the dynamic nature of cancer cells. Mammography has known limitations in clinical sensitivity, particularly in cases with high breast density. While breast ultrasound can mitigate some of these limitations, there remain unmet needs in this field. Previously, we reported the promising clinical performance (Sensitivity: 96.43%, Specificity: 97.32%) of serum thioredoxin 1 (Trx1) as a potential novel means for detecting breast cancer. In this study, we aim to evaluate the extent to which serum Trx1 can enhance the accuracy of breast cancer diagnosis, including in dense breasts, within the imaging-based diagnostic system powered by mammography and ultrasonography. Methods: We have generated monoclonal antibodies against Trx1 and developed an ELISA kit (DxMe® BC Kit, E&amp;S Healthcare, Korea) that quantifies Trx1 in serum. A total of 308 biopsy-confirmed breast cancer patients, who had undergone mammography prior to their final diagnosis, were recruited. The clinical information of these 308 patients was retrospectively examined and collected. The data from 254 individuals, who also had results from breast ultrasound, and 261 individuals, who had records of breast density, were analyzed collectively. Freshly collected blood was prepared as previously indicated, and then tested with the kit to determine the level of Trx1. All serum samples were measured twice, and statistical analyses were conducted using ROC analysis, one-way ANOVA, and unpaired t-tests. Results: Clinical sensitivity for BC detection was analyzed using the Trx1 levels of the 254 subjects who underwent both mammography and breast ultrasound. The respective sensitivities of mammography, breast ultrasound, and the DxMe® BC Kit were 72.83%, 92.91%, and 96.46%. With combined analysis of mammography, breast ultrasound, and Trx1, the sensitivity increased to 98.43% for mammography + breast ultrasound, and 98.82% for mammography + Trx1. When breast ultrasound + Trx1 was considered, the sensitivity rose to 99.61%. Likewise, the sensitivity of mammography + breast ultrasound + Trx1 was also measured at 99.61%. Among the 261 breast cancer patients with available information about breast density, 186 patients (71.3%) had dense breasts. The clinical sensitivity of mammography and Trx1 was specifically analyzed for these dense breast cases. Mammography demonstrated a sensitivity of 64.3%, while the DxMe® BC kit exhibited a sensitivity of 97.2%. When mammography and Trx1 were analyzed in parallel, sensitivity increased to 99.7%. Conclusion: This study demonstrated that the DxMe® BC kit, which quantifies serum Trx1 levels, can enhance the accuracy of conventional image-based diagnostics for breast cancer screening and early detection by more than 25%. This improvement in sensitivity was observed when mammography and breast ultrasound were each used in conjunction with Trx1, with no significant difference noted between these two combinations. Notably, in the case of dense breasts, the Trx1 test improved sensitivity by more than 30% compared to mammography. Given that sensitivity increases when mammography and Trx1 tests are used together, it showed that incorporating the Trx1 test alongside traditional mammography could significantly enhance diagnostic performance in dense breasts up to 35%. This approach has the potential to streamline the breast cancer diagnostic process, reduce time and cost, and improve the reliability of current breast cancer screening and early detection methods, regardless of breast density. Citation Format: Young Kim, Hyemi Ko, Yeri Lee, Keunkoo Shin, Jongam Song, Jeryoung Kim, Kyounghoon Suh, Jinsun Lee. Serum Thioredoxin 1 Enhances Breast Cancer Detection in Mammography and Breast Ultrasound, Including Dense Breasts [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-13-12.

Breast Cancer Screening: Can We Justify Deescalation?

Novel breast cancer screening methods that detect greater numbers of occult (nonpalpable) tumors have been rapidly incorporated into clinical practice, with the aim of reducing mortality. Yet, tumor detection has never been validated as a proper surrogate outcome measure for breast cancer mortality. Moreover, the detection of greater numbers of occult cancers increases the risk of overdiagnosis, which refers to detection of tumors that pose no threat to life and would never have been detected in the absence of screening. With recent advances in breast cancer therapy, many cancers that were previously curable only if detected as occult tumors with mammography screening are perhaps now curable even when detected as small palpable tumors, thereby giving us an opportunity to deescalate screening and mitigate the risk of overdiagnosis. Thus, a randomized trial comparing screening mammography versus screening clinical breast examination (CBE), with breast cancer mortality as the endpoint, is now warranted. In such a trial, hand-held ultrasound might aid in the interpretation of screening CBE findings. In conclusion, recent improvements in breast cancer therapy provide the justification to assess the deescalation of breast cancer screening. See related article by Farber et al., p. 671.

Automated breast ultrasound in breast cancer screening of mammographically dense breasts: added values

BackgroundFull-field digital mammography (FFDM) is the primary screening method for breast cancer, yet the number of cancers that can be missed with mammography is considerable, notably in female with dense breast. In this study, we compared the diagnostic yield and the clinical significance of FFDM for breast cancer detection in female with dense breasts versus its performance when complemented by automated breast ultrasound (ABUS).ResultsThis retrospective study was performed during the period between January 2022 and December 2022 including 500 females with dense breast (ACR C&D), who underwent screening using FFDM and ABUS. The images were retrospectively interpreted, and statistical assessments were done comparing the FFDM results alone and after complemented with ABUS. Significance was considered at a p value less than 0.05. The use of FFDM with supplemental ABUS has reduced the numbers of recall and showed improved breast cancer detection with increased positive predictive value (from 74.5 to 83.5%). In comparison, using FFDM alone and associated with ABUS, there was moderate agreement with a kappa test of 0.51; p < 0.001.ConclusionABUS can be a useful and powerful diagnostic imaging tool when adjunct to FFDM for screening of dense breast. In this study, ABUS showed less false-negative results and improved the sensitivity of cancer detection.

Non-invasive screening and subtyping for breast cancer by serum SERS combined with LGB-DNN algorithms

Early detection of breast cancer and its molecular subtyping is crucial for guiding clinical treatment and improving survival rate. Current diagnostic methods for breast cancer are invasive, time consuming and complicated. In this work, an optical detection method integrating surface-enhanced Raman spectroscopy (SERS) technology with feature selection and deep learning algorithm was developed for identifying serum components and building diagnostic model, with the aim of efficient and accurate noninvasive screening of breast cancer. First, the high quality of serum SERS spectra from breast cancer (BC), breast benign disease (BBD) patients and healthy controls (HC) were obtained. Chi-square tests were conducted to exclude confounding factors, enhancing the reliability of the study. Then, LightGBM (LGB) algorithm was used as the base model to retain useful features to significantly improve classification performance. The DNN algorithm was trained through backpropagation, adjusting the weights and biases between neurons to improve the network's predictive ability. In comparison to traditional machine learning algorithms, this method provided more accurate information for breast cancer classification, with classification accuracies of 91.38 % for BC and BBD, and 96.40 % for BC, BBD, and HC. Furthermore, the accuracies of 90.11 % for HR+/HR- and 88.89 % for HER2+/HER2- can be reached when evaluating BC patients' molecular subtypes. These results demonstrate that serum SERS combined with powerful LGB-DNN algorithm would provide a supplementary method for clinical breast cancer screening.

IJCM_128A: Understanding the Breast Cancer Knowledge, Screening practises and Needs among the women in rural South India: A Community-based mixed method study.

Background: Breast cancer (BC) is the leading cancer in India among women accounting for 13.5% of all cancer cases. One of the leading causes of high breast cancer deaths is poor awareness about risk factors, symptoms and screening leading to the late presentation at an advanced stage. Objectives: 1. To understand the knowledge of breast cancer symptoms and risk factors, perceptions and practises of breast cancer screening. 2. To do a need assessment of the people in the community regarding screening of breast cancer. Methodology: A community-based mixed method study was conducted at Chengalpattu, Tamil Nadu and primary data was collected from 401 women aged 18-60 years. Structured questionnaire was used to collect quantitative data to assess the knowledge of breast cancer. The need assessment on screening was done by Focus group discussions (FGD). Mean, median, proportions and chi-square test were used to analyse quantitative data and thematic content analysis for qualitative data. Results: The study found that 71.3% of the women were aware of breast cancer. The women who were aware of breast cancer considered lump in breast (78.3%), pain in one breast (66%) as the common symptoms. Women who were aware of breast cancer reported early menarche (16.8%), late menopause (15.4%), nulliparity (20%) and family history of cancer (49%) as the risk factors of breast cancer. 65% of the women were aware of at least one of the methods of breast cancer screening. More awareness programs, organising screening camps in the village, reducing the cost of screening tests were some of the needs addressed during the FGD. Conclusion: Knowledge of symptoms, risk factors and screening practises of breast cancer were low which necessitates the enhancement of knowledge on breast cancer to reduce the burden in the community.

Awareness regarding breast cancer amongst women in Pakistan: A systematic review and meta-analysis.

Breast cancer (BCa) is the most common cause of cancer death in Pakistan. In 2019, Pakistan saw the highest global BCa-associated death rate. But do Pakistani women know about the various aspects of BCa? And how prevalent are BCa screening methods amongst Pakistani females? These questions formed the basis for our study. We conducted this review in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. On September 1, 2023, we searched PubMed, Embase, Scopus, and Google Scholar, and performed a citation search to search for eligible studies published in 2010 or after, using the following terms: "breast cancer" and "Pakistan". Observational studies that evaluated BCa awareness and/or practice amongst Pakistani females who were not associated with medicine were eligible. We used the National Institutes of Health quality assessment tool to assess the risk of bias. We conducted a proportion meta-analysis to calculate pooled prevalences for variables. Responses from 9766 Pakistani women across 18 included studies showed alarmingly low levels of BCa knowledge: risk factors, 42.7% (95% CI: 34.1%-51.4%); symptoms, 41.8% (95% CI: 26.2%-57.5%); diagnostic modalities, 36.3% (95% CI: 23.1%-49.4%); treatments, 46.6% (95% CI: 13.5%-79.8%). Prevalence of breast self-examination (BSE) and ever having undergone a clinical breast exam (CBE) was 28.7% (95% CI: 17.9%-39.6%) and 15.3% (95 CI: 11.2%-19.4%), respectively. BCa knowledge was significantly associated with better educational status, age, and socioeconomic status. On average, only two in five Pakistani women are aware of one or more risk factors, symptoms, or diagnostic modalities. Approximately one in two women know about possible BCa treatment. Less than one in three women practice regular BSE, and less than one in five women have ever undergone a CBE.

Evaluating the Role of Breast Ultrasound in Early Detection of Breast Cancer in Low- and Middle-Income Countries: A Comprehensive Narrative Review.

Breast cancer, affecting both genders, but mostly females, exhibits shifting demographic patterns, with an increasing incidence in younger age groups. Early identification through mammography, clinical examinations, and breast self-exams enhances treatment efficacy, but challenges persist in low- and medium-income countries due to limited imaging resources. This review assesses the feasibility of employing breast ultrasound as the primary breast cancer screening method, particularly in resource-constrained regions. Following the PRISMA guidelines, this study examines 52 publications from the last five years. Breast ultrasound, distinct from mammography, offers advantages like radiation-free imaging, suitability for repeated screenings, and preference for younger populations. Real-time imaging and dense breast tissue evaluation enhance sensitivity, accessibility, and cost-effectiveness. However, limitations include reduced specificity, operator dependence, and challenges in detecting microcalcifications. Automatic breast ultrasound (ABUS) addresses some issues but faces constraints like potential inaccuracies and limited microcalcification detection. The analysis underscores the need for a comprehensive approach to breast cancer screening, emphasizing international collaboration and addressing limitations, especially in resource-constrained settings. Despite advancements, notably with ABUS, the primary goal is to contribute insights for optimizing breast cancer screening globally, improving outcomes, and mitigating the impact of this debilitating disease.

Mammography with deep learning for breast cancer detection.

X-ray mammography is currently considered the golden standard method for breast cancer screening, however, it has limitations in terms of sensitivity and specificity. With the rapid advancements in deep learning techniques, it is possible to customize mammography for each patient, providing more accurate information for risk assessment, prognosis, and treatment planning. This paper aims to study the recent achievements of deep learning-based mammography for breast cancer detection and classification. This review paper highlights the potential of deep learning-assisted X-ray mammography in improving the accuracy of breast cancer screening. While the potential benefits are clear, it is essential to address the challenges associated with implementing this technology in clinical settings. Future research should focus on refining deep learning algorithms, ensuring data privacy, improving model interpretability, and establishing generalizability to successfully integrate deep learning-assisted mammography into routine breast cancer screening programs. It is hoped that the research findings will assist investigators, engineers, and clinicians in developing more effective breast imaging tools that provide accurate diagnosis, sensitivity, and specificity for breast cancer.

A Descriptive Study to Assess Knowledge Regarding Breast Cancer Screening among Reproductive Age Women of Bharatpur-12, Chitwan

Background: Incidence of breast cancer in Nepal has increased in recent years. Screening helps in early detection of cancer and early diagnosis and timely treatment of breast cancer lead to better prognosis.&#x0D; Methodology: Cross sectional study on “ Knowledge Regarding Breast Cancer Screening among Reproductive Aged women of Bharatpur-12 Chitwan” was carried out to identify the knowledge regarding breast cancer screening. Non- probability purposive sampling technique was used to select the sample and sample size was 50. Data was collected by using structured questionnaire. Collected data was entered in SPSS version 22 and analysis was done using descriptive statistics.&#x0D; Findings: Findings of the study reveals that respondent’s mean age was 33.50±8.734. Regarding the religion, majority (90%) were hindu, similarly 52% were Bhramin. More that half (52%) had secondary education and 44% were engaged in business. In regard to marital status, 76% were married. Study findings summarized the level of knowledge in to three categories where, majority (52%) of the respondents had poor level of knowledge, less than half (44%) had moderate level of knowledge and only 4% had good level of knowledge. Conclusion majority of women in community are unaware about the use of different methods of breast cancer screening so awareness program for the breast cancer screening is essential.

Deep learning algorithm using bispectrum analysis energy feature maps based on ultrasound radiofrequency signals to detect breast cancer.

Ultrasonography is an important imaging method for clinical breast cancer screening. As the original echo signals of ultrasonography, ultrasound radiofrequency (RF) signals provide abundant tissue macroscopic and microscopic information and have important development and utilization value in breast cancer detection. In this study, we proposed a deep learning method based on bispectrum analysis feature maps to process RF signals and realize breast cancer detection. The bispectrum analysis energy feature maps with frequency subdivision were first proposed and applied to breast cancer detection in this study. Our deep learning network was based on a weight sharing network framework for the input of multiple feature maps. A feature map attention module was designed for multiple feature maps input of the network to adaptively learn both feature maps and features that were conducive to classification. We also designed a similarity constraint factor, learning the similarity and difference between feature maps by cosine distance. The experiment results showed that the areas under the receiver operating characteristic curves of our proposed method in the validation set and two independent test sets for benign and malignant breast tumor classification were 0.913, 0.900, and 0.885, respectively. The performance of the model combining four ultrasound bispectrum analysis energy feature maps in breast cancer detection was superior to that of the model using an ultrasound grayscale image and the model using a single bispectrum analysis energy feature map in this study. The combination of deep learning technology and our proposed ultrasound bispectrum analysis energy feature maps effectively realized breast cancer detection and was an efficient method of feature extraction and utilization of ultrasound RF signals.

Comprehensive Analysis of Mammography Images Using Multi-Branch Attention Convolutional Neural Network

Breast cancer profoundly affects women’s lives; its early diagnosis and treatment increase patient survival chances. Mammography is a common screening method for breast cancer, and many methods have been proposed for automatic diagnosis. However, most of them focus on single-label classification and do not provide a comprehensive analysis concerning density, abnormality, and severity levels. We propose a method based on the multi-label classification of two-view mammography images to comprehensively diagnose a patient’s condition. It leverages the correlation between density type, lesion type, and states of lesions, which radiologists usually perform. It simultaneously classifies mammograms into the corresponding density, abnormality type, and severity level. It takes two-view mammograms (with craniocaudal and mediolateral oblique views) as input, analyzes them using ConvNeXt and the channel attention mechanism, and integrates the information from the two views. Finally, the fused information is passed to task-specific multi-branches, which learn task-specific representations and predict the relevant state. The system was trained, validated, and tested using two public domain benchmark datasets, INBreast and the Curated Breast Imaging Subset of DDSM (CBIS-DDSM), and achieved state-of-the-art results. The proposed computer-aided diagnosis (CAD) system provides a holistic observation of a patient’s condition. It gives the radiologists a comprehensive analysis of the mammograms to prepare a full report of the patient’s condition, thereby increasing the diagnostic precision.

Awareness, attitude, and practices of adult females in Aseer Region, Saudi Arabia, about early detection of breast cancer

Aim of Study: To assess the awareness level of women about breast cancer as a major threat to women’s health and the importance of its early detection. Methods: This study followed a cross-sectional research design, which included 400 adult Saudi women, without breast cancer, who live in Aseer Region, Saudi Arabia. An online study questionnaire was developed in a simple Arabic language. It comprised participants’ personal characteristics; their awareness about breast cancer; assessment of risk factors for breast cancer; and their attitude and screening practices for breast cancer. Results: Only 59.4% of the participants were aware that breast cancer could be inherited in some families. The majority of the participants (78%) were aware that breastfeeding could protect against breast cancer. Early menarche and late menopause were recognized as risk factors for breast cancer by only 13.5% and 21.3% of the respondents respectively. Similarly, only 24.3% of the respondents answered correctly that the late age of first full-term pregnancy is a risk factor for breast cancer. More than half of the respondents (54.5%) were aware that hormonal replacement therapy is a risk factor for breast cancer. Women who had higher levels of education showed significantly more sufficient knowledge regarding breast cancer risk factors compared to those who were illiterate (48.2% versus 16.4%, P&lt;0.001). The majority of women, 91.5%, who have heard of BSE, recognized that it should bedone to search for tumours, while only 37.8%, 18.5%, and 27.4% recognized that the objective of BSE is to look for nipple discharge, nipple changes, and changes in the skin of the breast, respectively. Most of the participants in the 18-30 years age group (76.0%) have heard of BSE as compared to only 13.5% of those aged over 50. This association between women’s ages and hearing of BSE was statistically significant (P&lt;0.001). The majority of non-married women have heard of BSE (82.9%) as opposed to 53.1% of married women (P&lt;0.001). More employed women have heard of BSE than non-employed women (81.0% vs. 54.0%, p&lt;0.001). More women with a university education have heard of BSE than those who are illiterate and those who have a school education (79.8% vs. 18.2% and 62.5% respectively, P&lt;0.001). Conclusions: Knowledge levels of adult Saudi women in Aseer Region about breast cancer risk factors and screening methods are suboptimal. The awareness and practice of BSE among them is generally poor. Primary healthcare professionals have a limited role in educating women regarding breast cancer issues. Key Words: Awareness, attitude, practice, Women, Early detection of breast cancer, Saudi Arabia.

Exome Sequencing Reveals Novel Germline Variants in Breast Cancer Patients in the Southernmost Region of Thailand.

Germline carriers of pathogenic variants in cancer susceptibility genes are at an increased risk of breast cancer (BC). We characterized germline variants in a cohort of 151 patients diagnosed with epithelial BC in the southernmost region of Thailand, where the predominant ethnicity differs from that of the rest of the country. Whole exome sequencing was used to identify and subsequently filter variants present in 26 genes known to be associated with cancer predisposition. Of the 151 individuals assessed, 23, corresponding to 15.2% of the sample, exhibited the presence of one or more pathogenic or likely pathogenic variants associated with BC susceptibility. We identified novel germline truncating variants in BRIP1, CHEK2, MSH6, PALB2, and PTEN and annotated variants of uncertain significance (VUSs), both novel and previously documented. Therefore, it is advisable to use genetic testing as an additional risk screening method for BC in this area.

Comparative Study of Ultrasound Tissue Motion Tracking Techniques for Effective Breast Ultrasound Elastography

Breast cancer is the most common and deadly cancer in women, where early detection is of the utmost importance as survival rates decrease with the advancement of the disease. Most available methods of breast cancer screening and evaluation lack the ability to effectively differentiate between benign and malignant lesions without a biopsy. Ultrasound elastography (USE) is a cost-effective method that can potentially provide an initial malignancy assessment at the bedside. One of the challenges, however, is the uncertainty of tissue displacement data when performing USE due to out-of-plane movement of the tissue during mechanical stimulation, in addition to the computational efficiency necessary for real-time image reconstruction. This work presents a comparison of four different theoretically sound displacement estimators for their ability in tissue Young’s modulus reconstruction level with an emphasis on quality-to-runtime ratio to determine which estimators are most suitable for real-time USE systems. The methods are known in literature as AM2D, GLUE, OVERWIND, and SOUL methods. The effectiveness of each method was assessed as a stand-alone method or in combination with a strain field enhancement technique known as STREAL, which was recently developed using tissue mechanics-based regularization. The study was performed using radiofrequency US data pertaining to in silico and tissue mimicking phantoms in addition to clinical data. This data was used to generate tissue displacement fields employed to generate axial and lateral strain images before Young’s modulus images were reconstructed. The study indicates that the AM2D displacement estimator, which is an older and computationally less involved method, along with a tissue-mechanics-based image processing algorithm, performs very well, with high CNR, SNR, and preservation of tumor heterogeneity obtained at both strain and stiffness image levels, while its computation run-time is much lower compared to other estimation methods. As such, it can be recommended for incorporation in real-time USE systems.

Rapid Segmentation and Diagnosis of Breast Tumor Ultrasound Images at the Sonographer Level Using Deep Learning.

Breast cancer is one of the most common malignant tumors in women. A noninvasive ultrasound examination can identify mammary-gland-related diseases and is well tolerated by dense breast, making it a preferred method for breast cancer screening and of significant clinical value. However, the diagnosis of breast nodules or masses via ultrasound is performed by a doctor in real time, which is time-consuming and subjective. Junior doctors are prone to missed diagnoses, especially in remote areas or grass-roots hospitals, due to limited medical resources and other factors, which bring great risks to a patient's health. Therefore, there is an urgent need to develop fast and accurate ultrasound image analysis algorithms to assist diagnoses. We propose a breast ultrasound image-based assisted-diagnosis method based on convolutional neural networks, which can effectively improve the diagnostic speed and the early screening rate of breast cancer. Our method consists of two stages: tumor recognition and tumor classification. (1) Attention-based semantic segmentation is used to identify the location and size of the tumor; (2) the identified nodules are cropped to construct a training dataset. Then, a convolutional neural network for the diagnosis of benign and malignant breast nodules is trained on this dataset. We collected 2057 images from 1131 patients as the training and validation dataset, and 100 images of the patients with accurate pathological criteria were used as the test dataset. The experimental results based on this dataset show that the MIoU of tumor location recognition is 0.89 and the average accuracy of benign and malignant diagnoses is 97%. The diagnosis performance of the developed diagnostic system is basically consistent with that of senior doctors and is superior to that of junior doctors. In addition, we can provide the doctor with a preliminary diagnosis so that it can be diagnosed quickly. Our proposed method can effectively improve diagnostic speed and the early screening rate of breast cancer. The system provides a valuable aid for the ultrasonic diagnosis of breast cancer.

Özel Bir Hastanenin Kadın Hastaları Arasında Meme Kanseri Farkındalığı: Türkiye'de Risk Faktörleri, Semptomlar ve Tutumlar Üzerine Kesitsel Bir Çalışma

Background/Aims: The global prevalence of breast cancer continues to rise, necessitating heightened awareness, early detection, and effective management strategies. This study aimed to assess differences in breast cancer awareness, risk factors, symptoms, and attitudes among economically well-off female patients in Turkey.&#x0D; Material and Methods: A single-center survey was conducted at a private hospital, involving 189 economically well-off patients. Descriptive statistics, Chi-square tests, ANOVA, and Kruskal-Wallis tests were used for data analysis.&#x0D; Results: Participants had a mean age of 50.2 years, with the highest awareness of risk factors and symptoms observed in the 31-50 age group. Marital status and education were associated with breast cancer awareness, risk factors, symptoms, and screening methods. Education level correlated with enhanced knowledge of risk factors and symptoms. The prevalence of breast cancer risk awareness was 41.2%, with the most recognized risk factors being smoking and family history. Palpable lump (36.4%) and redness of breast skin (16.9%) were identified as common symptoms. Screening methods included self-breast examination (12.2%), physician examination (13.2%), ultrasonography (19%), magnetic resonance imaging (15.9%), and mammography (23.3%).&#x0D; Discussion: Breast cancer remains a critical global health concern, necessitating increased awareness and early detection. In Turkey, breast cancer poses a significant health burden. Socioeconomic factors impact awareness and outcomes, with education and marital status influencing awareness levels. The study highlights the need for tailored interventions and accessible screening programs to enhance awareness and early detection.&#x0D; Conclusion: This study sheds light on breast cancer awareness and attitudes among economically well-off female patients in Turkey. Education, marital status, and age play pivotal roles in shaping awareness levels. Targeted interventions and education are crucial for improving early detection, reducing mortality rates, and effectively addressing breast cancer.

Barriers to Using Breast Cancer Screening Methods Among Adult Females in Jeddah, Saudi Arabia: A Cross-Sectional Study.

Introduction Breast cancer (BC) is one of the most common cancers worldwide and it considerably increases morbidity and mortality globally. Screening methods, such as self-examination, clinical examination, and mammography, can help in early detection and treatment, which will help in improving survival rates and reducing mortality. While regular screening of the breast is essential to detect the earliest stages of breast cancer, not all women adhere to regular breast screening. Methods A cross-sectional study was conducted in Jeddah, Saudi Arabia, between December 2021 to July 2022. using an online self-administered questionnaire. The total number of responses (n = 328), Data was analyzed using SPSS 25. Results In this study, out of the 328 respondents, 18.9% reported undergoing regular mammography, 14.3% reported having regular clinical breast examinations, and 38.1% reported practicing regular breast self-examinations.. In addition, the participants' most known warning signs of BC were a lump under their armpit (69.1%). the most perceived barrier to breast self-examination (BSE) was Doing a breast examination will make her worry about what is wrong with her breast (47%). whereas the most barrier to clinical breast examination (CBE) was embarrassment (45.9%). On the other hand, the main barriers that prevented women from having mammograms were embarrassment (36%) and pain (32.6%). Conclusion The most perceived barrier to BSE was women's concern, while embarrassment and painful procedures were significant barriers to performing mammography and CBE. Therefore, adult females in Jeddah need educational programs to improve their knowledge and increase public awareness of breast cancer screening for early detection.