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Background: Individual perceptions, socio-cultural beliefs and health system factors are key determinants of people's health seeking behavior and are widely cited as the causes of delayed breast cancer diagnosis among women from structurally vulnerable settings. Asking: "how do women with a non-lived experience of cancer understand the disease and, what informs their health seeking behaviors?", we qualitatively explored, individual, sociocultural and health system elements from a conceptual model derived from the Socioecological, Health Belief and Cancer Stigma Frameworks, to understand perspectives of breast cancer in a South African urban community setting. Methods: Using a deductive approach and allowing new themes to emerge inductively, we investigated phenomenologically, breast cancer perceptions among 34 women from Soweto, Johannesburg (aged 35–74 years) in 6 Focus Group Discussions. We then conducted 20 follow-up semi-structured in-depth interviews to explore novel themes and suggestions for increasing breast cancer screening. Results: Findings revealed some awareness of breast and other cancers, but confusion and gaps in understanding of the disease, resulting in socio-culturally influenced misperceptions of risks, causes, and outcomes following treatment of breast cancer. This fueled perceptions of profound fear and stigma against people with breast and other cancers. These findings together with participant perceptions of primary healthcare providers being unwelcoming, under-resourced, and insufficiently trained to deal with breast cancer, resulted in women reporting being reluctant to participating in screening/early detection care seeking behavior. Women only accessed primary care when experiencing extreme pain or ill-health. Participants suggested as solutions for future interventions, the need for sustained community engagement, harnessing existing clinic and community stakeholders and resources to provide clear and understandable breast cancer information and encouragement for screening uptake. Conclusions: Health literacy gaps surrounding breast cancer fuels socio-culturally influenced misperceptions, fear, stigma, and fatalism among women from Soweto. Women perceive primary care providers of having insufficient knowledge, skills, and resources to provide effective breast cancer screening services. Participants suggested the need for greater community engagement involving primary clinics and existing community stakeholders working collaboratively. Clear, understandable, and consistent information about breast cancer must be regularly disseminated and communities must be regularly encouraged to utilize breast cancer screening services. [ABSTRACT FROM AUTHOR]

Background: Over the past decade, the adoption of screening programs, digital mammography, and magnetic resonance imaging (MRI) has increased early-stage breast cancer diagnosis rates. Mortality rates have decreased due to early detection and improved treatments, including personalized therapies. Accelerated partial-breast irradiation (APBI) is emerging as a convenient and effective treatment for some patients, with studies exploring its preoperative use. Preoperative APBI, especially with MRI guidance, offers improved tumor targeting and potentially reduced side effects. Magnetic Resonance Imaging-Guided Single-Fraction Pre-Operative Radiotherapy for Early-Stage Breast Cancer (RICE trial) aims to assess the feasibility and efficacy of MRI-guided single-dose radiotherapy (RT) for early-stage breast cancer. Methods: The RICE study is a prospective, single-arm study evaluating single-fraction preoperative, APBI treatment for patients with early-stage breast cancer using a magnetic resonance imaging linear accelerator (MRI linac). Eligible patients enrolled in this study will have a core biopsy to confirm estrogen receptor-positive and HER2-negative sub-type. RT planning will use a planning computed tomography (CT) co-registered with a MRI with the patient in either the supine or prone position. For the diagnostic workup, [18F] fluorodeoxyglucose positron emission tomography/CT ([18F] FDG PET/CT) and [18F] fluoroestradiol positron emission tomography/CT ([18F] FES PET/CT) will be performed prior to treatment. Thirty patients will receive a single ablative RT dose of 21 Gray to the tumor. Pre-treatment and post-treatment MRI scans will be acquired at baseline and 5 weeks post-RT respectively. Breast-conserving surgery will be scheduled for 6 weeks after APBI treatment using the MRI linac. The primary study endpoint is the successful administration of a single fraction of preoperative breast RT under the guidance of an MRI linac. Secondary endpoints include evaluating the utility of MRI, [18F] FDG PET/CT, and [18F] FES PET/CT as a non-invasive method for assessing treatment response in patients undergoing single-fraction preoperative APBI. Conclusion: The RICE trial represents a significant step in breast cancer treatment, offering insights that could lead to treatment protocols with minimized RT appointments and enhanced patient outcomes. Trial registration: This trial is registered with the Australian New Zealand Clinical Trials Registry (ANZCTR). Registered 31st of May 2021. Registration number: ACTRN12621000659808. [ABSTRACT FROM AUTHOR]

Background: Gestational diabetes mellitus (GDM) is a common pregnancy complication characterized by insulin resistance. A link has been suggested between insulin resistance and breast cancer, which is the most common cancer in women. Hence, women with previous GDM may be at increased risk of developing breast cancer, yet, the existing evidence is conflicting. This study explored the association between GDM and incident breast cancer, including age at cancer diagnosis. Additionally, we investigated the potential impact of severity of insulin resistance during pregnancy and of subsequent diabetes development on the breast cancer risk. Methods: We conducted a nationwide, register-based cohort study including all women giving birth in Denmark from 1997 to 2018. We defined GDM and breast cancer based on ICD-10 codes. Premenopausal and postmenopausal breast cancer was pragmatically defined as age at outcome < 50 years and ≥ 50 years, respectively. A proxy for severity of insulin resistance during pregnancy was based on insulin treatment; subsequent diabetes was defined as presence of ICD-10 codes and/or antidiabetic medication after pregnancy. The statistical analyses included Cox regression, logistic regression and t-test. Results: Of 708,121 women, 3.4% had GDM. The median follow-up period was 11.9 years (range 0-21.9). The overall breast cancer risk was comparable in women with and without previous GDM (adjusted hazard ratio 0.96 [95% CI 0.83–1.12]). Premenopausal and postmenopausal breast cancer risk also did not differ; however, women with previous GDM had a breast cancer diagnosis at younger age (42.6 vs. 43.5 years, p-value 0.01). All-cause mortality was similar regardless of GDM history. Severity of insulin resistance during pregnancy and subsequent diabetes did not affect breast cancer risk. Conclusions: This large, population-based cohort study showed no higher risk of incident breast cancer in women with previous GDM compared to women without previous GDM after a median of almost 12 years of follow-up. This was evident irrespective of menopausal state. The breast cancer risk was not influenced by the severity of insulin resistance during pregnancy and by subsequent diabetes development. Regardless of GDM history, attention towards prevention, early detection and treatment of breast cancer should be prioritized. [ABSTRACT FROM AUTHOR]

Background/Objectives: Digital mammography (DM) has long been the standard for breast cancer screening, while digital breast tomosynthesis (DBT) offers an advanced 3D imaging modality capable of generating 2D Synthetic Mammography (SM) images. Despite SM's potential to reduce radiation exposure, many clinics favor DM, with DBT and SM, due to its perceived diagnostic reliability. This study investigates whether radiologists can replace DM with SM in breast cancer screening and diagnosis or if both modalities are necessary. Methods: We retrospectively analyzed DM and SM images from 375 women aged 40–65 who underwent DM with DBT at King Khaled University Hospital from 2020–2022. Three radiologists evaluated the images using ACR BI-RADS, assessing diagnostic accuracy via the area under the receiver operating characteristic (ROC) curve (AUC). The agreement in cancer conspicuity, breast density, size, and calcifications were measured using weighted kappa (κ). Results: Among 57 confirmed cancer cases and 290 cancer-free cases, DM demonstrated higher sensitivity (82.5% vs. 78.9%) and diagnostic accuracy (AUC 0.800 vs. 0.783, p < 0.05) compared to SM. However, SM detected more suspicious calcifications in cancer cases (75.6% vs. 51.2%, p < 0.05). Agreement was fair for conspicuity (κ = 0.288) and calcifications (κ = 0.409), moderate for density (κ = 0.591), and poor for size (κ = 0.254). Conclusions: while SM demonstrates enhanced effectiveness in detecting microcalcifications, DM still proves superior in overall diagnostic accuracy and image clarity. Therefore, although SM offers certain advantages, it remains slightly inferior to DM and cannot yet replace DM in breast cancer screening. [ABSTRACT FROM AUTHOR]

Background: Breast density impacts upon breast cancer risk and recurrence, but its influence on breast cancer-specific survival is unclear. This study examines the influence of mammographic breast density (MBD) at diagnosis on breast cancer-specific survival. Methods: The data of 224 patients diagnosed with breast cancer were analyzed. Two area-based MBD measurement tools—AutoDensity and LIBRA—were used to measure MBD via a mammogram of the contralateral breast acquired at the time of diagnosis. These patients were split into two groups based on their percent breast density (PBD): high (PBD ≥ 20%) versus low (PBD < 20%). Breast cancer-specific survival in each of these PBD groups was assessed at a median follow-up of 34 months using Kaplan–Meier analysis and the Cox proportional hazards model. Results: The proportion of women with low PBD who died from breast cancer was significantly higher than that seen with high PBD (p = 0.01). The 5-year breast cancer-specific survival was poorer among women with low PBD than those with high PBD (0.348; 95% CI: 0.13–0.94) vs. 0.87; 95% CI: (0.8–0.96); p < 0.001)]. Women with higher breast density demonstrated longer survival regardless of the method of PBD measurement: LIBRA [log-rank test (Mantel–Cox): 9.4; p = 0.002)]; AutoDensity [log-rank test (Mantel–Cox) 7.6; p = 0.006]. Multivariate analysis also demonstrated that there was a higher risk of breast cancer-related deaths in women with low PBD (adjusted HR: 5.167; 95% CI: 1.974–13.521; p = 0.001). Conclusion: Women with <20% breast density at breast cancer diagnosis demonstrate poor survival regarding the disease. The impact of breast density on survival is not influenced by the method of measurement. [ABSTRACT FROM AUTHOR]

Breast cancer is the most prevalent cancer among women, and its diagnosis requires the accurate identification and classification of histological features for effective patient management. Artificial intelligence, particularly through deep learning, represents the next frontier in cancer diagnosis and management. Notably, the use of convolutional neural networks and emerging Vision Transformers (ViT) has been reported to automate pathologists' tasks, including tumor detection and classification, in addition to improving the efficiency of pathology services. Deep learning applications have also been extended to the prediction of protein expression, molecular subtype, mutation status, therapeutic efficacy, and outcome prediction directly from hematoxylin and eosin-stained slides, bypassing the need for immunohistochemistry or genetic testing. This review explores the current status and prospects of deep learning in breast cancer diagnosis with a focus on whole-slide image analysis. Artificial intelligence applications are increasingly applied to many tasks in breast pathology ranging from disease diagnosis to outcome prediction, thus serving as valuable tools for assisting pathologists and supporting breast cancer management. [ABSTRACT FROM AUTHOR]

We aimed to generate a model of cancer-related fatigue (CRF) of clinical importance 2 years after diagnosis of breast cancer building on clinical and behavioral factors and integrating pre-treatment markers of systemic inflammation. Women with stage I-III hormone receptor-positive/human epidermal growth factor receptor 2-negative breast cancer were included from the multimodal, prospective CANTO cohort (NCT01993498). The primary outcome was global CRF of clinical importance [European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire (QLQ)-C30 ≥40/100] 2 years after diagnosis (year 2). Secondary outcomes included physical, emotional, and cognitive CRF (EORTC QLQ-FA12). All pre-treatment candidate variables were assessed at diagnosis, including inflammatory markers [interleukin (IL)-1α, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, interferon γ, IL-1 receptor antagonist, tumor necrosis factor-α, and C-reactive protein], and were tested in multivariable logistic regression models implementing multiple imputation and validation by 100-fold bootstrap resampling. Among 1208 patients, 415 (34.4%) reported global CRF of clinical importance at year 2. High pre-treatment levels of IL-6 (quartile 4 versus 1) were associated with global CRF at year 2 [adjusted odds ratio (aOR): 2.06 (95% confidence interval [CI] 1.40-3.03); P = 0.0002; area under the receiver operating characteristic curve = 0.74]. Patients with high pre-treatment IL-6 had unhealthier behaviors, including being frequently either overweight or obese [62.4%; mean body mass index 28.0 (standard deviation 6.3 kg/m2)] and physically inactive (53.5% did not meet World Health Organization recommendations). Clinical and behavioral associations with CRF at year 2 included pre-treatment CRF [aOR versus no pre-treatment CRF: 3.99 (95% CI 2.81-5.66)], younger age [aOR per 1-year decrement: 1.02 (95% CI 1.01-1.03)], current tobacco smoking [aOR versus never: 1.81 (95% CI 1.26-2.58)], and worse insomnia or pain [aOR per 10-unit increment: 1.08 (95% CI 1.04-1.13), and 1.12 (95% CI 1.04-1.21), respectively]. Secondary analyses indicated additional associations of IL-2 [aOR per log-unit increment: 1.32 (95% CI 1.03-1.70)] and IL-10 [0.73 (95% CI 0.57-0.93)] with global CRF and of C-reactive protein [1.42 (95% CI 1.13-1.78)] with cognitive CRF at year 2. Emotional distress was consistently associated with physical, emotional, and cognitive CRF. This study proposes a bio-behavioral framework linking pre-treatment systemic inflammation with CRF of clinical importance 2 years later among a large prospective sample of survivors of breast cancer. • 34.4% of 1208 stage I-III breast cancer survivors had cancer-related fatigue of clinical importance 2 years after diagnosis. • High pre-treatment levels of the pro-inflammatory cytokine IL-6 were associated with global fatigue 2 years later. • Individuals with high levels of IL-6 had higher BMI and were less physically active than those with lower levels. • Higher pre-treatment IL-2 and IL-10 were also associated with higher and lower likelihood of global fatigue, respectively. • Higher C-reactive protein was associated with higher likelihood of cognitive fatigue. [ABSTRACT FROM AUTHOR]

Objectives: Breast cancer is a type of cancer caused by the uncontrolled growth of cells in the breast tissue. In a few cases, erroneous diagnosis of breast cancer by specialists and unnecessary biopsies can lead to various negative consequences. In some cases, radiologic examinations or clinical findings may raise the suspicion of breast cancer, but subsequent detailed evaluations may not confirm cancer. In addition to causing unnecessary anxiety and stress to patients, such diagnosis can also lead to unnecessary biopsy procedures, which are painful, expensive, and prone to misdiagnosis. Therefore, there is a need for the development of more accurate and reliable methods for breast cancer diagnosis. Methods: In this study, we proposed an artificial intelligence (AI)‐based method for automatically classifying breast solid mass lesions as benign vs malignant. In this study, a new breast cancer dataset (Breast‐XD) was created with 791 solid mass lesions belonging to 752 different patients aged 18 to 85 years, which were examined by experienced radiologists between 2017 and 2022. Results: Six classifiers, support vector machine (SVM), K‐nearest neighbor (K‐NN), random forest (RF), decision tree (DT), logistic regression (LR), and XGBoost, were trained on the training samples of the Breast‐XD dataset. Then, each classifier made predictions on 159 test data that it had not seen before. The highest classification result was obtained using the explainable XGBoost model (X2GAI) with an accuracy of 94.34%. An explainable structure is also implemented to build the reliability of the developed model. Conclusions: The results obtained by radiologists and the X2GAI model were compared according to the diagnosis obtained from the biopsy. It was observed that our developed model performed well in cases where experienced radiologists gave false positive results. [ABSTRACT FROM AUTHOR]

Breast cancer occurs when cells or tissues of the breast grow abnormally. On a global scale, Globally, cancer is a common and deadly type of disease that most affects women. These studies provide a new paradigm for skillful deep recognition of preprocessed images using deep neural networks. At this stage, the paper presents an efficient BreastNet model for breast cancer detection containing 48 layers, applied to three datasets namely breast ultrasound images (BUST), which includes 780 images classified into benign, malignant, normal and invasive ductal carcinoma (IDC) and BreakHis_v1_400X. Breast cancer images are processed using different feature extraction vectors. Medical image processing is a crucial step in adjusting the brightness and contrast of images. Gamma correction can help preserve detail in the darkest and brightest areas of an image. In addition to its combination of complex-frequency features, Geronimo-Hardin-Massopust (GHM) multiwave conversion is a type of multiwave conversion that offers many advantages for image processing tasks. By capturing low-frequency (soft areas) and high-frequency (edges and textures) components of an image more accurately, Discrete Wavelet Transform (DWT) processing can also In order to represent an image more efficiently, it is necessary to reduce the amount of data required while retaining important visual details. The proposed model, BreastNet, improved the classification accuracy performed on the proposed dataset by 95.88%, 93.30%, and 91.16%. The proposed model has a better execution rate than AlexNet, VGG16, Googlenet, Resnet18, and Resnet50 models. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common cancer type among women. Despite advanced treatment strategies, some patients still face challenges in disease control, prompting the exploration of new therapeutic approaches. N6-Methyladenosine (m6A) methylation modification regulates RNA and plays a crucial role in various tumor biological processes, closely linked to breast cancer occurrence, development, prognosis, and treatment. M6A regulators impact breast cancer progression, development, and drug resistance by modulating RNA metabolism and tumor-related pathways. Researchers have begun to understand the regulatory mechanisms of m6A methylation in breast cancer. This paper discusses the roles of m6A regulators in breast cancer progression, prognosis, and treatment, offering new perspectives for breast cancer diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common type of cancer among women globally. Automated breast cancer diagnosis improves healthcare by saving time and providing valuable assistance to pathologists. The focus of this research is the development of a deep learning framework named BCnet (Breast Cancer net), which aims to automatically diagnose breast cancer using histopathological images at various levels of magnification. We train the model from scratch using the BreakHis dataset, incorporating augmentation techniques. BCnet's performance is subsequently evaluated against current architectures that utilize the transfer learning approach. Furthermore, a Graphical User Interface (GUI) application was specifically designed for pathologists to use. BCnet was 95% accurate, while VGG-16, VGG-19, Xception, InceptionResnetV2, and Resnet152V2 were 78%, 79%, 77%, 79%, and 84% accurate, respectively. BCnet hence functions as an intelligent assistance system for pathologists and a potent tool for timely and precise identification, with the capacity to significantly influence patient outcomes. [ABSTRACT FROM AUTHOR]

Effective techniques for detection and classification are necessary to enhance the results of patients with common fatal diseases like breast cancer. With benefits including increased effectiveness and precision, DL and ML methods are currently being used as effective tools in the breast cancer diagnosis process. However, the efficiency and stability constraints of current approaches highlight the necessity for additional study. Therefore, this work suggested a new model of modified U-Net and convolutional networks for Breast Cancer (BC) segmentation and classification with new texture descriptors and the proposed model is named “U-TexBCNet”. First, mammography images are preprocessed by enhancing the image using the CLAHE filter and after inputting the preprocessed image into the modified U-Net-based segmentation for the segmentation process, the modified U-Net segmentation is named as “HexpNet”. Once the segmentation process is completed, the feature extraction process takes place. Here, the features include shape feature, color feature, VGG-16 feature, Inception V3 feature, GoogLeNet and modified LGTP feature extracted from the segmented image. Then, the extracted features are inputted into the hybrid classification model including modified CNN and LSTM. Lastly, the outcome from the classifiers (Modified CNN and LSTM) is averaged together to get the outcome of the proposed model. Ultimately, the results of the simulation are evaluated using both positive and negative criteria by contrasting them with the conventional models. [ABSTRACT FROM AUTHOR]

At the time of breast cancer diagnosis, most patients meet the diagnostic criteria to be classified as obese or overweight. This can significantly impact patient outcome: breast cancer patients with obesity (body mass index > 30) have a poorer prognosis compared to patients with a lean BMI. Obesity is associated with hyperleptinemia, and leptin is a well-established driver of metastasis in breast cancer. However, the effect of hyperleptinemia on angiogenesis in breast cancer is less well-known. Angiogenesis is an important process in breast cancer because it is essential for tumor growth beyond 1mm³ in size as well as cancer cell circulation and metastasis. This review investigates the role of leptin in regulating angiogenesis, specifically within the context of breast cancer and the associated tumor microenvironment in obese patients. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most prevalent cancers in women globally. Its treatment and prognosis are significantly influenced by the tumor microenvironment and tumor heterogeneity. Precision therapy enhances treatment efficacy, reduces unwanted side effects, and maximizes patients' survival duration while improving their quality of life. Spatial transcriptomics is of significant importance for the precise treatment of breast cancer, playing a critical role in revealing the internal structural differences of tumors and the composition of the tumor microenvironment. It offers a novel perspective in studying the spatial structure and cell interactions within tumors, facilitating more effective personalized treatments for breast cancer. This article will summarize the latest findings in the diagnosis and treatment of breast cancer from the perspective of spatial transcriptomics, focusing on the revelation of the tumor microenvironment, identification of new therapeutic targets, enhancement of disease diagnostic accuracy, comprehension of tumor progression and metastasis, assessment of drug responses, creation of highresolution maps of tumor cells, representation of tumor heterogeneity, and support for clinical decision-making, particularly in elucidating the tumor microenvironment, tumor heterogeneity, immunotherapy and their correlation with clinical outcomes. [ABSTRACT FROM AUTHOR]

Background and purpose: Among patients with solid tumors, those with breast cancer (BC) experience the most severe psychological issues, exhibiting a high global prevalence of depression that negatively impacts prognosis. Depression can be easily missed, and clinical markers for its diagnosis are lacking. Therefore, this study in order to investigate the diagnostic markers for BC patients with depression and anxiety and explore the specific changes of metabolism. Method and results: Thirty-eight BC patients and thirty-six matched healthy controls were included in the study. The anxiety and depression symptoms of the participants were evaluated by the 17-item Hamilton Depression Scale (HAMD-17) and Hamilton Anxiety Scale (HAMA). Plasma levels of glial fibrillary acidic protein (GFAP) and lipocalin-2 (LCN2) were evaluated using enzyme linked immunosorbent assay, and plasma lactate levels and metabolic characteristics were analyzed. Conclusion: This study revealed that GFAP and LCN2 may be good diagnostic markers for anxiety or depression in patients with BC and that plasma lactate levels are also a good diagnostic marker for anxiety. In addition, specific changes in metabolism in patients with BC were preliminarily explored. [ABSTRACT FROM AUTHOR]

Regenerative AI is transforming breast cancer diagnosis and treatment through enhanced imaging analysis, personalized medicine, drug discovery, and remote patient monitoring. AI algorithms can detect subtle patterns in mammograms and other imaging modalities with high accuracy, potentially leading to earlier diagnoses. In treatment planning, AI integrates patient-specific data to predict individual responses and optimize therapies. For drug discovery, generative AI models rapidly design and screen novel molecules targeting breast cancer pathways. Remote monitoring tools powered by AI provide real-time insights to guide care. Examples include Google's LYNA for analyzing pathology slides, Kheiron's Mia for mammogram interpretation, and Tempus's platform for integrating clinical and genomic data. While promising, challenges remain, including limited high-quality training data, integration into clinical workflows, interpretability of AI decisions, and regulatory/ethical concerns. Strategies to address these include collaborative data-sharing initiatives, user-centered design, explainable AI techniques, and robust oversight frameworks. In developing countries, AI tools like MammoAssist and Niramai's thermal imaging systemare improving access to screening. Overall, regenerative AI offers significant potential to enhance breast cancer care, but judicious implementation with awareness of limitations is crucial. Coordinated efforts across the healthcare ecosystem are needed to fully realize AI's benefits while addressing challenges. [ABSTRACT FROM AUTHOR]

Breast cancer remains a global health problem requiring effective diagnostic methods for early detection, in order to achieve the World Health Organization's ultimate goal of breast self-examination. A literature review indicates the urgency of improving diagnostic methods and identifies thermography as a promising, cost-effective, non-invasive, adjunctive, and complementary detection method. This research explores the potential of using machine learning techniques, specifically Bayesian networks combined with convolutional neural networks, to improve possible breast cancer diagnosis at early stages. Explainable artificial intelligence aims to clarify the reasoning behind any output of artificial neural network-based models. The proposed integration adds interpretability of the diagnosis, which is particularly significant for a medical diagnosis. We constructed two diagnostic expert models: Model A and Model B. In this research, Model A, combining thermal images after the explainable artificial intelligence process together with medical records, achieved an accuracy of 84.07%, while model B, which also includes a convolutional neural network prediction, achieved an accuracy of 90.93%. These results demonstrate the potential of explainable artificial intelligence to improve possible breast cancer diagnosis, with very high accuracy. [ABSTRACT FROM AUTHOR]

Minimally invasive interventional surgery is commonly used for diagnosing and treating breast cancer, but the high fluidity and deformability of breast tissue reduce intervention accuracy. This study proposes a bionic breast interventional robot that mimics the scorpion's predation process, actively manipulating tissue deformation to control target displacement and enhance accuracy. The robot's structure is designed using a modular method, and its kinematics and workspace are analyzed and solved. To address the nonlinear breast tissue deformation problem, a hierarchical tissue method is proposed to simplify the three-dimensional problem into a two-dimensional one. A two-dimensional tissue deformation solver is established based on the minimum energy method for quick resolution. The problem is treated as quasi-static, deriving the displacement relationship between external manipulation points and internal tissue targets. The method of active manipulation of tissue deformation is simulated using MATLAB (2019-b) software to verify the feasibility of the method. Results show maximum errors of 1.7 mm for prostheses and 2.5 mm for in vitro tissues in the X and Y directions. This method improves intervention accuracy in breast surgery and offers a new solution for breast cancer diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Breast cancer remains a major health concern worldwide, requiring the advancement of early detection methods to improve prognosis and treatment outcomes. In this sense, mammography is regarded as the gold standard in breast cancer screening and early detection. However, in a scenario where extensive analysis is required, a large set of mammograms conducted by radiologists may carry out false negative or false positive diagnoses. Therefore, artificial intelligence has emerged in recent years as a method for enhancing timing in breast cancer diagnosis. Nonetheless, preprocessing stages are required to prepare the mammography dataset to enhance learning models to correctly identify breast anomalies. In this paper, we introduce a novel method employing convolutional neural networks (CNNs) to segment the pectoral muscle in 1288 mediolateral oblique mammograms (MLOs), thereby addressing class imbalance and overfitting between classes, and dataset augmentation based on translation, rotation, and scale transformation. The effectiveness of the model was assessed through a confusion matrix and performance metrics, highlighting an average Dice coefficient of 0.98 and a Jaccard index of 0.96. The outcomes demonstrate the model capability to accurately identify three classes: pectoral muscle, breast, and background. This study emphasizes the importance of tackling class imbalance problems and augmenting data for the training of models for reliable early breast cancer detection. [ABSTRACT FROM AUTHOR]

Screening mammography is a part of routine well-woman care. It is important to understand how receipt of a false-positive mammography result may influence a woman's decision regarding whether and when to return for routine screening. This cohort study evaluates the association between screening mammography results and the probability of subsequent screening. Visual Abstract. Association Between False-Positive Results and Return to Screening Mammography in the Breast Cancer Surveillance Consortium Cohort: Screening mammography is a part of routine well-woman care. It is important to understand how receipt of a false-positive mammography result may influence a woman's decision regarding whether and when to return for routine screening. This cohort study evaluates the association between screening mammography results and the probability of subsequent screening. Background: False-positive results on screening mammography may affect women's willingness to return for future screening. Objective: To evaluate the association between screening mammography results and the probability of subsequent screening. Design: Cohort study. Setting: 177 facilities participating in the Breast Cancer Surveillance Consortium (BCSC). Patients: 3 529 825 screening mammograms (3 184 482 true negatives and 345 343 false positives) performed from 2005 to 2017 among 1 053 672 women aged 40 to 73 years without a breast cancer diagnosis. Measurements: Mammography results (true-negative result or false-positive recall with a recommendation for immediate additional imaging only, short-interval follow-up, or biopsy) from 1 or 2 screening mammograms. Absolute differences in the probability of returning for screening within 9 to 30 months of false-positive versus true-negative screening results were estimated, adjusting for race, ethnicity, age, time since last mammogram, BCSC registry, and clustering within women and facilities. Results: Women were more likely to return after a true-negative result (76.9% [95% CI, 75.1% to 78.6%]) than after a false-positive recall for additional imaging only (adjusted absolute difference, −1.9 percentage points [CI, −3.1 to −0.7 percentage points]), short-interval follow-up (−15.9 percentage points [CI, −19.7 to −12.0 percentage points]), or biopsy (−10.0 percentage points [CI, −14.2 to −5.9 percentage points]). Asian and Hispanic/Latinx women had the largest decreases in the probability of returning after a false positive with a recommendation for short-interval follow-up (−20 to −25 percentage points) or biopsy (−13 to −14 percentage points) versus a true negative. Among women with 2 screening mammograms within 5 years, a false-positive result on the second was associated with a decreased probability of returning for a third regardless of the first screening result. Limitation: Women could receive care at non-BCSC facilities. Conclusion: Women were less likely to return to screening after false-positive mammography results, especially with recommendations for short-interval follow-up or biopsy, raising concerns about continued participation in routine screening among these women at increased breast cancer risk. Primary Funding Source: National Cancer Institute. [ABSTRACT FROM AUTHOR]

This study aimed to analyze the behavioral determinants of breast cancer (BC) diagnosis delays in France. To do so, we investigated whether time discounting, risk tolerance, and personality traits influenced the BC diagnosis delay of patients. We used original retrospective data collected on 2 large online patient networks from 402 women diagnosed of BC. The BC diagnosis delay was measured by the difference between the date of diagnosis and the date of first symptoms. Time discounting and risk tolerance are measured with both self-reported questions and hypothetical lotteries. Personality traits are measured with the 10-item Big Five indicator. Ordinary least square and probit models were used to analyze whether these behavioral characteristics influenced the BC diagnosis delay. Results showed that risk tolerance and time discounting were not significantly associated with the BC diagnosis delay. However, we found a longer diagnosis delay for women with a neuroticism personality trait (standardized coefficients ranged from 0.104 [ P -value =.036] to 0.090 [ P -value =.065]). Overall, our findings underline the need for an increased consideration of cancer screening public health policy for women with mental vulnerabilities since such vulnerabilities were found to be highly correlated with a neuroticism personality trait. • The breast cancer diagnosis delay is long if it exceeds 3 months. • Women with a neuroticism personality trait have a longer diagnosis delay. • Public health policies should give greater attention to women with a neuroticism personality trait. [ABSTRACT FROM AUTHOR]

Granular cell tumors of breast are rare neoplasms, majority of which are benign. Their imaging appearances are often indistinguishable from breast cancer. They may demonstrate a few differentiating features and unlike breast cancer, typically follow a benign course. Histopathology and immunochemistry form the cornerstone of diagnosis of granular cell tumor. In this article, we presented a series of three cases of granular cell tumors with variable presentations. Our goal is to increase familiarity for these neoplasms and for the readers to be able to distinguish them from the more common entity of breast cancer, as their prognosis and management differ. [ABSTRACT FROM AUTHOR]

Women with a personal history of breast cancer (PHBC) are at an increased risk of either a local recurrence or a new primary breast cancer. Thus, surveillance is essential for the detection of recurrent disease at the earliest possible stage, allowing for prompt treatment, and potentially improving overall survival. Nowadays, mammography follow-up is the only surveillance imaging technique recommended by international guidelines. Nevertheless, sensitivity of mammography is lower after breast cancer treatment, particularly during the first 5 years, due to increased density or post-treatment changes. Contrast-enhanced breast imaging techniques, such as MRI or contrast-enhanced mammography (CEM), are very sensitive to detect malignant enhancement, especially in dense breasts. This Special Report will provide arguments in favor of and against breast cancer follow-up with MRI or CEM, in a debate style between experts in Breast Imaging. Finally, the scientific points of pros and cons arguments will be summarized to help objectively decide the best follow-up strategy for women with a personal history of breast cancer. Clinical relevance statement: A personalized approach to follow-up imaging after conservative breast cancer treatment could optimize patient outcomes, using mammography as a baseline for most patients, and MRI or CEM selectively in patients with higher risks for a recurrence. Key Points: • Women with a personal history of breast cancer are at an increased risk of either a local recurrence or a new primary breast cancer. • Breast cancer survivors may benefit from additional imaging with MRI/CEM, in case of increased risk of a second breast cancer, with dense breasts or a cancer diagnosis before age 50 years. • As survival after local recurrence seems to depend on the initial stage at diagnosis, imaging should be more focused on detecting tumors in the earliest stages. [ABSTRACT FROM AUTHOR]

As one of the important concepts in epidemiology, herd immunity was recommended to control the COVID-19 pandemic. Inspired by this technique, the Coronavirus Herd Immunity Optimiser has recently been introduced, demonstrating promising results in addressing optimisation problems. This particular algorithm has been utilised to address optimisation problems widely; However, there is room for enhancement in its performance by making modifications to its parameters. This paper aims to improve the Coronavirus Herd Immunity Optimisation algorithm to employ it in addressing breast cancer diagnosis problem through feature selection. For this purpose, the algorithm was discretised after the improvements were made. The Opposition-Based Learning approach was applied to balance the exploration and exploitation stages to enhance performance. The resulting algorithm was employed in the diagnosis of breast cancer, and its performance was evaluated on ten benchmark functions. According to the simulation results, it demonstrates superior performance in comparison with other well-known approaches of the similar nature. The results demonstrate that the new approach performs well in diagnosing breast cancer with high accuracy and less computational complexity and can address a variety of real-world optimisation problems. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common type of cancer in women and poses a significant health risk to women globally. Developments in computer-aided diagnosis (CAD) systems are focused on specific tasks of classification and segmentation, but few studies involve a completely integrated system. In this study, a comprehensive CAD system was proposed to screen ultrasound, mammograms and magnetic resonance imaging (MRI) of breast cancer, including image preprocessing, breast cancer classification, and tumour segmentation. First, the total variation filter was used for image denoising. Second, an optimised XGBoost machine learning model using EfficicnetB0 as feature extraction was proposed to classify breast images into normal and tumour. Third, after classifying the tumour images, a hybrid CNN deep learning model integrating the strengths of MobileNet and InceptionV3 was proposed to categorise tumour images into benign and malignant. Finally, Attention U-Net was used to segment tumours in annotated datasets while classical image segmentation methods were used for the others. The proposed models in the designed CAD system achieved an accuracy of 96.14% on the abnormal classification and 94.81% on tumour classification on the BUSI dataset, improving the effectiveness of automatic breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

Breast cancer has become a major worldwide health issue, accounting for a large portion of the mortality rate among women. As a result, the need for early detection techniques to enhance prognosis is increasing. Many techniques are being used to detect breast cancer early, and treatment outcomes are frequently favorable when the disease is detected early. Mammography is a commonly used and very successful method for identifying breast cancer among these modalities. Through additional image processing operations like resizing and normalizing, this technology allows the detection of malignant spots from mammography pictures of the affected area. The goal of our research is to improve breast cancer detection and diagnosis speed and accuracy. In this study, we investigate the use of deep learning methods, particularly the visual geometry group (VGG16) and ResNet50 models, for mammography-based breast cancer detection. We assess the performance of the VGG16 and ResNet50 models by training and testing on a mammogram dataset that consists of 322 images from the mammographic image analysis society (MIAS) dataset. The suggested models aim to classify these images into normal, benign, and malignant groupings. Our results show better performance when compared to existing approaches. The proposed methods VGG16 and ResNet50 show promising results, achieving a classification accuracy of 91.23% and 99.01% respectively. [ABSTRACT FROM AUTHOR]

This study of the features of the biochemical composition of biological fluids in patients with breast cancer, including saliva, allows us to identify some indicators as metabolic predictors of the presence of the disease. Objectives: to study the influence of the menopause factor and body mass index (BMI) on the biochemical composition of saliva and to evaluate the applicability of metabolic markers of saliva for the diagnosis of breast cancer. Methods: The case–control study involved 1438 people (breast cancer, n = 543; fibroadenomas, n = 597; control, n = 298). A comprehensive study of the biochemical composition of saliva was carried out using 36 parameters. Results: When comparing the salivary biochemical composition in breast cancer, fibroadenomas, and controls, it is necessary to take into account the menopausal status, as well as BMI (less than 25 or more) for the group of patients with preserved menstrual function. A complex of biochemical parameters has been identified that change in saliva during breast cancer, regardless of menopause and BMI (total protein, urea, uric acid, NO, α-amino acids, GGT), as well as specific parameters that must be taken into account when analyzing individual subgroups (imidazole compounds, LDH, catalase, α-amylase). During the study of a separate group of patients with leaf-shaped (phyllodes) tumors, we found similarities with breast cancer in the changes in some biochemical parameters that can be attributed to metabolites of malignant growth (protein, α-amino acids, calcium, NO, pyruvate, peroxidase, α-amylase). Conclusions: We demonstrated changes in a wide range of salivary biochemical parameters depending on the presence of fibroadenomas and breast cancer. From the point of view of clinical practice, this may be useful information for monitoring the condition of patients with fibroadenomas, which are difficult to unambiguously classify based on instrumental diagnostics alone. [ABSTRACT FROM AUTHOR]

Globally, breast cancer is the leading cause of mortality for women. It has had an impact on the lives of all individuals, regardless of gender—males, females, and transgender people. However, it is more common among women. Its fatality rate can be decreased with early discovery and treatment. Machine Learning (ML) is critical to the early detection of breast cancer. ML's use has grown in a variety of fields during the last decade. Analytical modelling with ML is mostly restricted to statistical approaches such as image recognition, resonance spectroscopy, and mass spectrometry. This study gives an in-depth look at breast cancer and the many ML approaches used to identify it. A thorough examination of breast cancer diagnosis using machine learning is provided, comprising classification, prediction, and detection. Technical concerns with present prediction models and measuring methods (used to determine how active malignant and healthy tissues are) are highlighted to make future recommendations. [ABSTRACT FROM AUTHOR]

After breast cancer diagnosis, individuals have to cope with various psychological, social, and physical consequences. One such consequence is chronic pain, which can persist even after the completion of active treatments and detrimentally impact quality of life. The published literature highlights the impact of chronic pain on cognitive functions and on decision-making processes. However, little is known about the imbalance in decision-making among cancer survivors suffering from chronic pain. This study aims to explore the cognitive impairment associated with risky decision-making in breast cancer survivors experiencing chronic pain, using the Iowa Gambling Task (IGT). Sixty women voluntarily participated in this prospective, cross-sectional study. Among them, twenty had a history of breast cancer and exhibited a significant level of chronic pain, twenty had previously received a cancer diagnosis but did not experience chronic pain, and twenty had not been diagnosed with cancer. The results revealed that women with chronic pain tend to make significantly more disadvantageous choices in the IGT compared to the other groups. Moreover, participants with chronic pain tend to believe that circumstances will change regardless of their efforts to achieve something. This way of thinking may contribute to reinforcing the observed imbalance in decision-making. Our hypothesis suggests that chronic pain may trigger a "Cascade Effect", exerting multiple influences on decision-making and behaviors. The cognitive overload provoked by chronic pain in breast cancer survivors may not only affect individual cognitive functions, but also have a "Cascade Effect" on other psychological dimensions. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common malignant tumor among women worldwide and remains one of the leading causes of death among women. Its incidence and mortality rates are continuously rising. In recent years, with the rapid advancement of deep learning (DL) technology, DL has demonstrated significant potential in breast cancer diagnosis, prognosis evaluation, and treatment response prediction. This paper reviews relevant research progress and applies DL models to image enhancement, segmentation, and classification based on large-scale datasets from TCGA and multiple centers. We employed foundational models such as ResNet50, Transformer, and Hover-net to investigate the performance of DL models in breast cancer diagnosis, treatment, and prognosis prediction. The results indicate that DL techniques have significantly improved diagnostic accuracy and efficiency, particularly in predicting breast cancer metastasis and clinical prognosis. Furthermore, the study emphasizes the crucial role of robust databases in developing highly generalizable models. Future research will focus on addressing challenges related to data management, model interpretability, and regulatory compliance, ultimately aiming to provide more precise clinical treatment and prognostic evaluation programs for breast cancer patients. [ABSTRACT FROM AUTHOR]

Background: Despite known benefits of physical activity in reducing breast cancer risk, its impact on mammographic characteristics remain unclear and understudied. This study aimed to investigate associations between pre-diagnostic physical activity and mammographic features at breast cancer diagnosis, specifically mammographic breast density (MBD) and mammographic tumor appearance (MA), as well as mode of cancer detection (MoD). Methods: Physical activity levels from study baseline (1991–1996) and mammographic information from the time of invasive breast cancer diagnosis (1991–2014) of 1116 women enrolled in the Malmö Diet and Cancer Study cohort were used. Duration and intensity of physical activity were assessed according to metabolic equivalent of task hours (MET-h) per week, or World Health Organization (WHO) guideline recommendations. MBD was dichotomized into low-moderate or high, MA into spiculated or non-spiculated tumors, and MoD into clinical or screening detection. Associations were investigated through logistic regression analyses providing odds ratios (OR) with 95% confidence intervals (CI) in crude and multivariable-adjusted models. Results: In total, 32% of participants had high MBD at diagnosis, 37% had non-spiculated MA and 50% had clinical MoD. Overall, no association between physical activity and MBD was found with increasing MET-h/week or when comparing women who exceeded WHO guidelines to those subceeding recommendations (ORadj 1.24, 95% CI 0.78–1.98). Likewise, no differences in MA or MoD were observed across categories of physical activity. Conclusions: No associations were observed between pre-diagnostic physical activity and MBD, MA, or MoD at breast cancer diagnosis. While physical activity is an established breast cancer prevention strategy, it does not appear to modify mammographic characteristics or screening detection. [ABSTRACT FROM AUTHOR]

Objective: This study explores the experience of body-image changes throughout the trajectory of breast cancer. Methods: We conducted qualitative in-depth interviews with 53 women (n = 53) representing diverse age groups, with a mean age of 48.61 years (SD = 13.12), from various regions across the Czech Republic. The interviews were audio/video recorded and divided into two phases. The initial phase consisted of a narrative part, followed by a semi-structured interview. Thematic analysis was employed to identify and explore the topics that held significant importance to the participants. Results: This research investigated how body image and emotional responses evolve in women with breast cancer during various stages of the illness. Throughout the breast cancer journey, women undergo diverse body image changes, with some of them being described as particularly traumatic, such as hair loss and preventive operations. The effects of breast cancer extend beyond physical scars, influencing femininity and womanhood in the long run. The experience of womanhood is marked by visible and non-visible effects that persist for years after treatment. Conclusion: Study has provided valuable insights into the emotional journey that women experience during breast cancer diagnosis, treatment, and survivorship. The illness trajectory framework has proven to be a valuable tool for understanding the complexities of body image concerns and the emotional implications faced by breast cancer patients. By acknowledging the significance of body image issues and providing targeted interventions, healthcare providers can improve the psychosocial well-being and overall quality of life for breast cancer survivors. Further research in this area is needed to develop and refine supportive interventions that address the unique challenges women face during their breast cancer journey. Ultimately, by fostering a sensitive and comprehensive approach to care, we can empower women to navigate their breast cancer journey with greater resilience and confidence. [ABSTRACT FROM AUTHOR]

Background: In 2022, our team launched the pioneering national proficiency testing (PT) scheme for the pathological diagnosis of breast cancer, rapidly establishing its credibility throughout China. Aiming to continuously monitor and improve the proficiency of Chinese pathologists in breast pathology, the second round of the PT scheme was initiated in 2023, which will expand the number of participating institutions, and will conduct a nationwide investigation into the interpretation of HER2 0, 1+, and 2+/FISH- categories in China. Methods: The methodology employed in the current round of PT scheme closely mirrors that of the preceding cycle in 2022, which is designed and implemented according to the "Conformity assessment—General requirements for proficiency testing"(GB/T27043—2012/ISO/IEC 17043:2010). More importantly, we utilized a statistics-based method to generate assigned values to enhance their robustness and credibility. Results: The final PT results, published on the website of the National Quality Control Center for Cancer (http://117.133.40.88:3927), showed that all participants passed the testing. However, a few institutions demonstrated systemic biases in scoring HER2 0, 1+, and 2+/FISH- with accuracy levels below 59%, considered unsatisfactory. Especially, the concordance rate for HER2 0 cases was only 78.1%, indicating challenges in distinguishing HER2 0 from low HER2 expression. Meanwhile, areas for histologic type and grade interpretation improvement were also noted. Conclusions: Our PT scheme demonstrated high proficiency in diagnosing breast cancer in China. But it also identified systemic biases in scoring HER2 0, 1+, and 2+/FISH- at some institutions. More importantly, our study highlighted challenges in the evaluation at the extreme lower end of the HER2 staining spectrum, a crucial area for further research. Meanwhile, it also revealed the need for improvements in interpreting histologic types and grades. These findings strengthened the importance of robust quality assurance mechanisms, like the nationwide PT scheme conducted in this study, to maintain high diagnostic standards and identify areas requiring further training and enhancement. [ABSTRACT FROM AUTHOR]

Breast cancer is a prevalent global health concern affecting millions of women annually. Breast cancer diagnosis can lead to impairment in multiple areas of psychosocial well-being, including physical, social, and emotional functioning. Most of the articles discuss about the treatment aspects but the psychological needs are not much discussed. We carried out 17 semi-structured interview questions in English with women aged 35 to 65 who had been diagnosed with stage I-IV invasive breast cancer within the last four years in 10 participants. Ten participants were recruited from a large rural hospital in India. The findings unveil the emotional turmoil, support systems, coping mechanisms, communication challenges, preparation strategies, and financial considerations that shape the cancer journey. Notably, patients exhibit resilience amidst emotional upheaval, relying on familial and professional support networks. The study emphasizes the importance of structured psychosocial interventions and patient-centered care to address these needs, ultimately enhancing patients' resilience throughout their treatment journey. Possibilities for future research and implications for clinical practice are addressed. [ABSTRACT FROM AUTHOR]

Background: Invasive lobular carcinoma (ILC) is challenging to stage accurately using mammography (MG) and ultrasound (US) with undiagnosed ipsilateral and contralateral cancer resulting in poor patient outcomes including return to surgery. Our institution employs routine staging breast MRI in ILC for this reason. However, increased time for further imaging/biopsies contributes to patient anxiety and potentially delays definite management. We aimed to quantify the frequency of staging MRI‐detected additional lesions requiring biopsy or follow‐up, the added cancer detection rate and MRI prompted change in surgical management. Methods: An observational study on staging breast MRI for newly diagnosed ILC at a tertiary Western Australian hospital from January 2019 to August 2022. Standardized 3T MRI protocol was performed, double read by unblinded fellowship‐trained radiologists. Histopathology from biopsy, surgery, or first annual surveillance was the reference standard for additional MRI‐detected lesions. Results: One hundred ten MRI studies demonstrated 49 (45%) patients had at least one additional clinically significant MRI‐detected lesion. Thirty‐one patients had an additional ipsilateral lesion detected, of which 18 (58%) proved malignant; 14 (45%) multifocal and 4 (13%) multicentric ILC. Additional work‐up of MRI‐detected lesions averaged a 9‐day delay to definitive surgery compared to patients with a negative or definitively benign MRI. MRI changed surgical planning in 11 of 110 cases from breast conservation surgery (BCS) to mastectomy and there were two contralateral cancers diagnosed. BCS reoperation rate was 11%. Conclusion: Staging MRI for ILC identifies clinically significant lesions in nearly half of patients, predominantly ipsilateral multifocal disease, without significant delay to definitive surgery. [ABSTRACT FROM AUTHOR]

Diagnostic radiology is concerned with obtaining images of the internal organs using radiological imaging procedures. These images are then interpreted by a diagnostic radiologist, who produces a textual report that assists in the diagnosis of illness or injury. Early detection of certain illnesses, particularly cancer, is critical, and the reports produced by diagnostic radiologists play a key role in this process. To develop models for the early detection of cancer, text classification techniques can be applied to radiological reports. However, this process requires access to a dataset of radiology reports, which is not widely available. Currently, radiology report datasets exist for high-resource languages such as English and Dutch, but not for low-resource languages such as Turkish. This article describes the collection of a mammography report dataset for Turkish, consisting of 62 reports from real patients that were manually labeled by an expert for diagnosing breast cancer. Basic machine learning models were applied to this dataset using pre-trained BERT, DistilBERT, and an ensemble learning hard voting approach. The results showed that BERT on Turkish achieved the best performance, with a 91% F1-score. Hard Voting, which combined the results of BERTTurkish, BERTClinical, and BERTMultilingual, achieved the highest F1-score of 93%. The results show that BERT and Hard Voting outperform the other machine learning models for breast cancer diagnosis from Turkish radiology reports. [ABSTRACT FROM AUTHOR]

This article discusses the proposed U.S. Preventive Services Task Force recommendation on breast cancer screening for women in their 40s. It addresses the arguments against screening in this age group, such as false-positive results and overdiagnosis, and provides evidence to counter these claims. The article highlights the flaws in the discussion around overdiagnosis and emphasizes the importance of well-designed studies. It also argues that early randomized controlled trials used to inform screening policy are outdated and flawed. The article concludes by emphasizing the benefits of screening for women in their 40s, as breast cancer is diagnosed in thousands of women in this age group annually in the United States. [Extracted from the article]

The present study was conducted on a group of women with breast cancer (60 women) and a group of healthy women as a control group (30 women) aged (27-85) years. Samples were collected from women with breast cancer from the Teaching Oncology Hospital - Medical City, Baghdad for the period from the first of October 2023 until the end of February 2024. The activity of servivin was asses and the level of glutathione, glutathione peroxidase, malondialdehyde and the following results were obtained: 1-There was a significant increase in the group of breast cancer women for both servivin, and malondialdehyde compared to the control group. 2- There was a significant decrease in the level of glutathione-and glutathione peroxidase . 3-The existence of a significant correlation between survivin and each of GSH and GPX. Conclusions: The results of the present study showed a significant increase in the level of survivin in women with breast cancer in conjunction with a significant correlation with GSH, GPX, MDA and BMI, which supports the possibility of considering survivin as an indicator for breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

A novel nomogram incorporating artificial intelligence (AI) and clinical features for enhanced ultrasound prediction of benign and malignant breast masses. This study analyzed 340 breast masses identified through ultrasound in 308 patients. The masses were divided into training (n = 260) and validation (n = 80) groups. The AI-based analysis employed the Samsung Ultrasound AI system (S-detect). Univariate and multivariate analyses were conducted to construct nomograms using logistic regression. The AI-Nomogram was based solely on AI results, while the ClinAI- Nomogram incorporated additional clinical factors. Both nomograms underwent internal validation with 1000 bootstrap resamples and external validation using the independent validation group. Performance was evaluated by analyzing the area under the receiver operating characteristic (ROC) curve (AUC) and calibration curves. The ClinAI-Nomogram, which incorporates patient age, AI-based mass size, and AI-based diagnosis, outperformed an existing AI-Nomogram in differentiating benign from malignant breast masses. The ClinAI-Nomogram surpassed the AI-Nomogram in predicting malignancy with significantly higher AUC scores in both training (0.873, 95% CI: 0.830–0.917 vs. 0.792, 95% CI: 0.748–0.836; p = 0.016) and validation phases (0.847, 95% CI: 0.763–0.932 vs. 0.770, 95% CI: 0.709–0.833; p < 0.001). Calibration curves further revealed excellent agreement between the ClinAI-Nomogram's predicted probabilities and actual observed risks of malignancy. The ClinAI- Nomogram, combining AI alongside clinical data, significantly enhanced the differentiation of benign and malignant breast masses in clinical AI-facilitated ultrasound examinations. [ABSTRACT FROM AUTHOR]

Summary: Breast cancer is the most prevalent kind of tumor to occur in females and the primary cause of death for women. Early detection is perhaps the most successful strategy to minimize breast cancer mortality. Early diagnosis necessitates a consistent and efficient diagnostics method that allows doctors to differentiate benign from malignant breast cancers without a surgical sample. The goal of this endeavor is to develop a sophisticated breast cancer diagnosis method. The primary goal of the paper is to reduce the death rate among women by promoting early detection of breast cancer. First, pre‐processing techniques such as median filtering and contrast limiting adaptive histogram equalization are used to the obtained raw images. By doing this, the machine‐learning model's computational complexity is decreased and the image quality is enhanced. K‐means clustering is used to segregate the pre‐processed image. Additionally, features including the enhanced local vector pattern, grey‐level co‐occurrence matrix and local vector patterns are produced in the course of the feature extraction stage. Finally, an optimized deep belief network (DBN) is carrying out the classification process. To boosts the classification accuracy, activation function of DBN (tanh, softmax, ReLu) as well as its weight function is optimized by the proposed grey wolf updated whale optimization algorithm The accuracy of the greywolf updated whale optimization algorithm+DBN is above 93% for datasets 1 and 2 when compared to extant models. Finally, calculation of the performance validates the proposed model's performance. [ABSTRACT FROM AUTHOR]

Our study develops a computer-aided diagnosis (CAD) system for breast ultrasound by presenting an innovative frequency domain technique for extracting mass irregularity features, thereby significantly boosting tumor classification accuracy. The experimental data consists of 5252 ultrasound breast tumor images, including 2745 benign tumors and 2507 malignant tumors. A Support Vector Machine was employed to classify the tumor as either benign or malignant, and the effectiveness of the proposed features set in distinguishing malignant masses from benign ones was validated. For the constructed CAD system, the performance indices' accuracy, sensitivity, specificity, PPV, and NPV were 92.91%, 89.94%, 91.38%, 90.29%, and 91.45%, respectively, and the area index in the ROC analysis (AUC) was 0.924, demonstrating our method's superiority over traditional spatial gray level dependence (SGLD), the ratio of depth to width, the count of depressions, and orientation features. Therefore, the constructed CAD system with the proposed features will be able to provide a precise and quick distinction between benign and malignant breast tumors with minimal training time in clinical settings. [ABSTRACT FROM AUTHOR]

In the realm of image-based breast cancer detection and severity assessment, this study delves into the revolutionary potential of sophisticated artificial intelligence (AI) techniques. By investigating image processing, machine learning (ML), and deep learning (DL), the research illuminates their combined impact on transforming breast cancer diagnosis. This integration offers insights into early identification and precise characterization of cancers. With a foundation in 125 research articles, this article presents a comprehensive overview of the current state of image-based breast cancer detection. Synthesizing the transformative role of AI, including image processing, ML, and DL, the review explores how these technologies collectively reshape the landscape of breast cancer diagnosis and severity assessment. An essential aspect highlighted is the synergy between advanced image processing methods and ML algorithms. This combination facilitates the automated examination of medical images, which is crucial for detecting minute anomalies indicative of breast cancer. The utilization of complex neural networks for feature extraction and pattern recognition in DL models further enhances diagnostic precision. Beyond diagnostic improvements, the abstract underscores the substantial influence of AI-driven methods on breast cancer treatment. The integration of AI not only increases diagnostic precision but also opens avenues for individualized treatment planning, marking a paradigm shift toward personalized medicine in breast cancer care. However, challenges persist, with issues related to data quality and interpretability requiring continued research efforts. Looking forward, the abstract envisions future directions for breast cancer identification and diagnosis, emphasizing the adoption of explainable AI techniques and global collaboration for data sharing. These initiatives promise to propel the field into a new era characterized by enhanced efficiency and precision in breast cancer care.

Breast cancer is the most prevalent type of cancer that affects women worldwide and poses a serious risk to female mortality. In order to lower death rates and enhance treatment results, early detection is critical. Neutrosophic Set Theory (NST) and machine learning (ML) approaches are integrated in this study to provide a novel hybrid methodology (NS-ML) that improves breast cancer diagnosis. Using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the research transforms these data into Neutrosophic (N) representations to effectively capture uncertainties. When trained on the N-dataset instead of traditional datasets, ML algorithms such as Decision Tree (DT), Random Forest (RF), and Adaptive Boosting (AdaBoost) perform better. Notably, N-AdaBoost models achieve outstanding results with 99.12% accuracy and 100% precision, highlighting the efficacy of NS in enhancing diagnostic reliability. [ABSTRACT FROM AUTHOR]

Breast cancer constitutes a significant global health burden, while conventional diagnosis approaches may lack precision and can be discomforting for patients. Exosomes have emerged as promising biomarkers for breast cancer due to their participation in diverse pathological processes, and a convenient analysis platform is believed to greatly promote its application. In this study, we propose a novel digital PCR approach utilizing near‐infrared (NIR) photo‐responsive thermosensitive microcarriers integrated with black phosphorus for quantifying microRNA (miRNA) biomarkers within exosomes. Petal‐like biomimetic nanomaterials were firstly assembled for non‐specific exosome capture based on the affinity effect of avidin and biotin. Photothermal‐responsive microcarriers, fabricated using gelatin‐based substrates blended with photothermal nanocomposite, exhibited NIR‐induced heating and reversible phase transition properties. We optimized synthesis parameters on thermal response and established a programmable and controllable NIR light source module. The results indicated a significant elevation in the levels of biomarkers miRNA‐1246 and miRNA‐122, with fold increases ranging from 6.2 to 23.6 and 5.9 to 13.0, respectively, in breast cancer cell lines MCF‐7 and MDA‐MB‐231 compared to healthy control cells HUVEC. This study offers broad prospects for utilizing exosomes to resolve predictive biomarkers. [ABSTRACT FROM AUTHOR]

Breast cancer diagnosis through mammography is a pivotal application within medical image-based diagnostics, integral for early detection and effective treatment. While deep learning has significantly advanced the analysis of mammographic images, challenges such as low contrast, image noise, and the high dimensionality of features often degrade model performance. Addressing these challenges, our study introduces a novel method integrating Genetic Algorithms (GA) with pre-trained Convolutional Neural Network (CNN) models to enhance feature selection and classification accuracy. Our approach involves a systematic process: first, we employ widely-used CNN architectures (VGG16, VGG19, MobileNet, and DenseNet) to extract a broad range of features from the Medical Image Analysis Society (MIAS) mammography dataset. Subsequently, a GA optimizes these features by selecting the most relevant and least redundant, aiming to overcome the typical pitfalls of high dimensionality. The selected features are then utilized to train several classifiers, including Linear and Polynomial Support Vector Machines (SVMs), K-Nearest Neighbors, Decision Trees, and Random Forests, enabling a robust evaluation of the method's effectiveness across varied learning algorithms. Our extensive experimental evaluation demonstrates that the integration of MobileNet and GA significantly improves classification accuracy, from 83.33% to 89.58%, underscoring the method's efficacy. By detailing these steps, we highlight the innovation of our approach which not only addresses key issues in breast cancer imaging analysis but also offers a scalable solution potentially applicable to other domains within medical imaging. [ABSTRACT FROM AUTHOR]

Malignant papillary lesions, and in particular, encapsulated papillary carcinoma (EPC) of the breast, continue to present diagnostic challenges for the practising pathologist. In addition to the relative rarity of these lesions, the lack of evidence‐based diagnostic criteria, differences in the biological characteristics, and the clinical behaviour of in situ and invasive forms, variable use of immunohistochemical markers, and overlap with other tumour types including high‐grade circumscribed forms of invasive breast carcinomas has resulted in diagnostic discordance with potentially significant clinical and management implications. Pathologists should be familiar with the range of morphology observed in malignant papillary tumours, EPC, and EPC‐like tumours and the existence of tumours with overlapping features. In this review we summarize the common diagnostic pitfalls in malignant papillary tumours and provide an approach to the diagnostic evaluation and categorisation of these enigmatic entities. [ABSTRACT FROM AUTHOR]

Breast cancer (BC), the most prevalent cancer worldwide, poses a significant threat to women's health, often resulting in mortality. Early intervention is crucial for reducing mortality rates and improving recovery. Mammography plays a pivotal role in early detection through high-resolution imaging. Various classification techniques, including classical and deep learning (DL) methods, assist in diagnosing BC. Convolutional neural networks (CNN)-based classification with transfer learning enhances efficiency and accuracy, especially with limited datasets. This study evaluates the performance of different pretrained deep CNN architectures in classifying pathological mammography scans from the Mini-MIAS dataset. The results show that Xception, VGG16, VGG19, and MobileNetV2 achieve the highest accuracy (97%), with VGG19 demonstrating the fastest prediction speed (0.53 s). [ABSTRACT FROM AUTHOR]

Background: BRCA1:c.5017_5019del (p.His1673del) is a founder variant relatively frequent in Northern Italy. Despite previous suggestion of pathogenicity, variant classification in public databases is still conflicting, needing additional evidence. Methods: Maximum likelihood penetrance of breast/ovarian and other cancer types was estimated using full pedigree data from 53 informative Italian families. The effect of the variant on BRCA1‐ABRAXAS1 interaction was assessed using a GFP‐fragment reassembly‐based PPI assay. Results were combined with additional data from multiple sources to classify the variant according to ACMG/AMP classification rules specified for BRCA1/2. Results: Variant‐carriers displayed increased risk for ovarian cancer (HR = 33.0, 95% CI = 7.0–155.0; cumulative risk at age 70 = 27.6%, 95% CI = 12.6–40.0%) but not for breast cancer (HR = 0.7, 95% CI = 0.2–2.2). An increased risk of uterine cancer (HR = 8.0, 95% CI = 1.03–61.6) emerged, warranting further evaluation. Likelihood‐ratio in favor of pathogenicity was 98898642.82 under assumption of standard BRCA1 breast and ovarian penetrance, and 104240832.84 after excluding breast cancer diagnoses (based on penetrance results). Functional analysis demonstrated that the variant abrogates the BRCA1‐ABRAXAS1 binding, supporting the PS3 code assignment within the ACMG/AMP rule‐based model. Collectively, these findings allowed to classify the variant as pathogenic. Conclusion: Pathogenicity of BRCA1:c.5017_5019del(p.His1673del) has been confirmed; however, breast cancer risk in Italian families is not increased, unlike in families from other countries and in carriers of most BRCA1 pathogenic variants. The knowledge of atypical risk profiles for this and other variants will pave the way for personalized management based on specific genotype. [ABSTRACT FROM AUTHOR]