Improve Breast Cancer Diagnosis Research Articles

Artificial Intelligence in Breast Cancer Diagnosis and Treatment: Advances in Imaging, Pathology, and Personalized Care

Breast cancer is the most prevalent cancer worldwide, affecting both low- and middle-income countries, with a growing number of cases. In 2024, about 310,720 women in the U.S. are projected to receive an invasive breast cancer diagnosis, alongside 56,500 cases of ductal carcinoma in situ (DCIS). Breast cancer occurs in every country of the world in women at any age after puberty but with increasing rates in later life. About 65% of women with the BRCA1 and 45% with the BRCA2 gene variants develop breast cancer by age 70. While these genes account for 5% of breast cancers, their prevalence is higher in certain populations. Advances in early detection, personalised medicine, and AI-driven diagnostics are improving outcomes by enabling a more precise analysis, reducing recurrence, and minimising treatment side effects. Our paper aims to explore the vast applications of artificial intelligence within the diagnosis and treatment of breast cancer and how these advancements can contribute to elevating patient care as well as discussing the potential drawbacks of such integrations into modern medicine. We structured our paper as a non-systematic review and utilised Google Scholar and PubMed databases to review literature regarding the incorporation of AI in the diagnosis and treatment of non-palpable breast masses. AI is revolutionising breast cancer management by enhancing imaging, pathology, and personalised treatment. In imaging, AI can improve the detection of cancer in mammography, MRIs, and ultrasounds, rivalling expert radiologists in accuracy. In pathology, AI enhances biomarker detection, improving HER2 and Ki67 assessments. Personalised medicine benefits from AI’s predictive power, aiding risk stratification and treatment response. AI also shows promise in triple-negative breast cancer management, offering better prognosis and subtype classification. However, challenges include data variability, ethical concerns, and real-world validation. Despite limitations, AI integration offers significant potential in improving breast cancer diagnosis, prognosis, and treatment outcomes.

Grating-based phase-contrast computed tomography for breast tissue at an inverse compton source

The introduction of mammography screening programs has significantly reduced breast cancer mortality rates. Nevertheless, some lesions remain undetected, especially in dense breast tissue. Studies have shown that phase-contrast imaging can improve breast cancer diagnosis by increasing soft tissue contrast. Furthermore, grating-based phase-contrast imaging enables the simultaneous acquisition of absorption, phase-contrast, and scattering, so-called dark-field images. The latter allows the classification of microcalcifications. In addition, breast computed tomography (BCT) systems can identify and discriminate overlapping but clinically relevant structures. This study investigates the benefit of combining grating-based phase-contrast with BCT. We explore the potential of grating-based phase-contrast breast computed tomography (gbpc-BCT) with a breast phantom and a freshly dissected fibroadenoma. Improved image contrast could be achieved with radiation doses comparable to those used in clinical BCT.

Advanced breast diffusion-weighted imaging: what are the next steps? A proposal from the EUSOBI International Breast Diffusion-weighted Imaging working group.

This study by the EUSOBI International Breast Diffusion-weighted Imaging (DWI) working group aimed to evaluate the current and future applications of advanced DWI in breast imaging. A literature search and a comprehensive survey of EUSOBI members to explore the clinical use and potential of advanced DWI techniques and a literature search were involved. Advanced DWI approaches such as intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and diffusion tensor imaging (DTI) were assessed for their current status and challenges in clinical implementation. Although a literature search revealed an increasing number of publications and growing academic interest in advanced DWI, the survey revealed limited adoption of advanced DWI techniques among EUSOBI members, with 32% using IVIM models, 17% using non-Gaussian diffusion techniques for kurtosis analysis, and only 8% using DTI. A variety of DWI techniques are used, with IVIM being the most popular, but less than half use it, suggesting that the study identified a gap between the potential benefits of advanced DWI and its actual use in clinical practice. The findings highlight the need for further research, standardization and simplification to transition advanced DWI from a research tool to regular practice in breast imaging. The study concludes with guidelines and recommendations for future research directions and clinical implementation, emphasizing the importance of interdisciplinary collaboration in this field to improve breast cancer diagnosis and treatment. Advanced DWI in breast imaging, while currently in limited clinical use, offers promising improvements in diagnosis, staging, and treatment monitoring, highlighting the need for standardized protocols, accessible software, and collaborative approaches to promote its broader integration into routine clinical practice. Increasing number of publications on advanced DWI over the last decade indicates growing research interest. EUSOBI survey shows that advanced DWI is used primarily in research, not extensively in clinical practice. More research and standardization are needed to integrate advanced DWI into routine breast imaging practice.

A study on breast cancer image classification based on particle swarm algorithm and transfer learning

Breast cancer is a major disease that poses a serious threat to the lives and health of women. A new framework was proposed to address the common challenges of high dimensional and data imbalances in image classification. This framework integrates particle swarm optimization (PSO) and transfer learning into a convolutional neural network model based on the ResNet34 architecture. The respective strengths complement each other to enhance the performance and efficiency of the classification model. Through parameter optimization and functional selection of PSO, the global search of the model has been improved. Transfer learning lets the model use large pre-trained datasets to learn more quickly on small sample datasets, which is especially helpful in areas where there are a lot of images that don’t have labels. Experimental findings reveal that our framework attains a 97.83% accuracy rate on the dataset and notably shortens the training cycle, demonstrating its effectiveness in improving breast cancer diagnosis performance with small sample sizes.

Optimizing breast cancer diagnosis: Harnessing the power of nature-inspired metaheuristics for feature selection with soft voting classifiers

Breast cancer is a widespread and serious condition that poses a significant threat to women's health globally, contributing significantly to mortality rates. Machine learning tools play a critical role in both the effective management and early detection of this disease. Feature selection (FS) methods are essential for identifying the most impactful features to improve breast cancer diagnosis. These methods reduce data dimensionality, eliminate irrelevant information, enhance learning accuracy, and improve the comprehensibility of results. However, the increasing complexity and dimensionality of cancer data pose substantial challenges to many existing FS methods, thereby reducing their efficiency and effectiveness. To overcome these challenges, numerous studies have demonstrated the success of nature-inspired optimization (NIO) algorithms across various domains. These algorithms excel in mimicking natural processes and efficiently solving complex optimization problems. Building on these advancements, we propose an innovative approach that combines powerful feature selection methods based on NIO techniques with a soft voting classifier. The NIO techniques employed include the Genetic Algorithm, Cuckoo Search, Salp Swarm, Jaya, Flower Pollination, Whale Optimization, Sine Cosine, Harris Hawks, and Grey Wolf Optimization algorithms. The Soft Voting Classifier integrates various machine learning models, including Support Vector Machines, Gaussian Naive Bayes, Logistic Regression, Decision Tree, and Gradient Boosting. These are used to improve the effectiveness and accuracy of breast cancer diagnosis. The proposed approach has been empirically evaluated using a variety of evaluation measures, such as F1 score, precision, recall, accuracy and Area Under the Curve (AUC), for performance comparison with individual machine learning techniques. The results demonstrate that the soft-voting ensemble technique, particularly when combined with feature selection based on the Jaya algorithm, outperforms all individual classifiers on the breast cancer dataset. It achieves the highest scores for accuracy, precision, recall, F1-score, and AUC, with values of 99.6 %, 99.21 %, 100 %, 99.6 %, and 99.6 %, respectively. Following closely, both the Genetic Algorithm and Salp Swarm feature selection demonstrate strong performance, with scores of 99.5 %, 100 %, 99 %, 99.5 %, and 99.5 %, respectively. Furthermore, the approach's effectiveness in identifying the minimal set of relevant features has been evaluated. Given the critical importance of selecting relevant features in complex cancer datasets, the proposed method proves to be a valuable tool for achieving early detection and improved interpretability. This method can also support healthcare professionals in making well-informed decisions regarding cancer diagnosis and treatment strategies.

Improving Breast Cancer Diagnosis in Mammograms with Progressive Transfer Learning and Ensemble Deep Learning

Improving Breast Cancer Diagnosis in Mammograms with Progressive Transfer Learning and Ensemble Deep Learning

Enhancing Breast Cancer Detection: Leveraging Convolutional Neural Networks

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

Predictive breast cancer diagnosis using ensemble fuzzy model

Breast cancer continues to be a major global health challenge, necessitating reliable diagnostic methods for early detection and improved patient outcomes. This study introduces a novel ensemble fuzzy model for predictive breast cancer diagnosis, integrating multiple deep-learning classifiers with fuzzy logic to enhance decision-making. Traditional diagnostic approaches often struggle with the complexity and heterogeneity of breast cancer data, which this new model addresses through an innovative ensemble technique. The ensemble model combines the strengths of Inception-V4, Inception-ResNet, and Inception V3/V4 + BN, with fuzzy logic for adaptive priority assignment based on confidence scores. The method employs a re-parameterized Gompertz function to assign fuzzy ranks to constituent classifiers, allowing flexible fusion strategies. The proposed model is evaluated on two benchmark breast cancer datasets: the Digital Database for Screening Mammography (DDSM) and the Breast Cancer Histopathological Image Classification (BACH) dataset. It achieves high performance across key metrics, including accuracy, precision, recall, and F1 score, consistently outperforming individual classifiers. On the DDSM dataset, the ensemble fuzzy model attains an accuracy of 0.97, a recall of 0.93, a precision of 0.95, and an F1 score of 0.96. Similarly, on the BACH dataset, the proposed method records an accuracy of 97.05%, a recall of 99.31%, a precision of 95.44%, and an F1 score of 97.37%, demonstrating its robust capability to identify positive instances and maintain a balanced performance. These results highlight the potential of the ensemble fuzzy model to improve breast cancer diagnosis, offering a reliable solution to the inherent challenges in this field.

Exploring Dimensionality Reduction Techniques for Improved Breast Cancer Diagnosis

Breast cancer diagnosis is a critical area in medical research, where the challenge lies not only in accurate identification but also in managing the inherent complexity of high-dimensional datasets. This paper navigates this challenge by exploring dimensionality reduction techniques to enhance diagnostic accuracy. The primary objective of this research was to employ dimensionality reduction methods to refine breast cancer diagnosis, with a focus on improving accuracy and interpretability. The study investigates the impact of preprocessing techniques on a high-dimensional dataset, aiming to uncover meaningful patterns for effective diagnostic models. Starting with a dataset including 569 observations and 30 attributes, careful examination reveals imbalances in the dataset (63% benign, 37% malignant). To deal with multicollinearity, we use the coefficients of Pearson correlation to find and eliminate highly correlated features. Subsequent data transformation, utilizing min-max normalization, ensures uniform weighting. Principal Component Analysis (PCA) is then leveraged for comprehensive dimensionality reduction. Visualizations through scree plots and bi-plots underscore the efficacy of early principal components in distinguishing benign from malignant cases. Our results demonstrate a notable 24% reduction in data dimensionality, affirming the process's efficiency. This abstract beckon the exploration of detailed findings, emphasizing dimensionality reduction's pivotal role in refining breast cancer diagnosis for more accurate, efficient, and interpretable models.

Exploring human mammaglobin: as a possible diagnostic and prognostic indicator in breast cancer tissue

Breast cancer is a major global health issue, with high diagnosis rates worldwide, especially in less developed areas, leading to significant mortality rates. This review focuses on the molecular characteristics of breast cancer, emphasizing the role of human mammaglobin-A (hMAM-A) as a diagnostic and prognostic marker. hMAM-A, a dimeric protein from the secretoglobin family, is produced exclusively by breast tissue and shows elevated levels in breast cancer cases, making it a highly accurate marker for disease detection. The review also examines various factors influencing breast cancer, such as age, tobacco use, menopausal status, and hormone replacement therapy (HRT). Younger age at diagnosis is associated with poorer outcomes, highlighting the importance of early detection. Tobacco smoke increases mortality rates in breast cancer patients. Menopausal status affects molecular subtypes and risk factors, impacting treatment and prognosis. HRT has a complex relationship with breast cancer risk. The review concludes by discussing the need for novel biomarkers, including hMAM-A, to improve breast cancer diagnosis and management.

Abstract PO3-07-02: Radiomics Models for B-mode Breast Ultrasound and Strain Elastography to improve Breast Cancer Diagnosis (INSPiRED 005): An International, Multicenter Analysis

Abstract Background: Breast Elastography, a technique that quantifies tissue stiffness, has been evaluated to objectify and improve the performance of B-mode breast ultrasound. However, large prospective trials showed benefits in BI-RADS 4a breast masses only and a high operator dependency. Modern Artificial Intelligence techniques for automated image processing like radiomics, a technique where quantified features are extracted from images, may overcome these limitations. We aimed to develop and validate radiomics models based on B-mode and Strain Elastography (SE) images for patients with BI-RADS 3 or 4 breast masses and compare their performance to the respective human experts. Methods: This is a secondary analysis of an international, multicenter trial (NCT02638935), evaluating the performance of SE in women with BI-RADS 3 or 4 breast masses. Women were recruited at 12 institutions in 7 countries and underwent B-mode breast ultrasound as well as SE. B-mode images were saved and re-assed by three ultrasound readers ( >10 years of experience), resulting in three independent assessments and a final consensus assessment. SE was interpreted using the E-/B ratio. B-mode and strain images were manually segmented and quantitative radiomics features were extracted using pyradiomics. We used 10-fold cross-validation to build machine learning models (XGBoostTree, MARS) based on data of 11 of 12 study sites. The data of the 12th (largest) study site was used as external validation set. Performance metrics included sensitivity, specificity and area under the receiver operator characteristic curve (AUROC). Results: The study included a total of 1288 patients, 1206 with evaluable B-mode images and 1190 with evaluable Strain images. Mean age was 46.6 years (SD 16.02) and a total number of 29.0% (350 of 1206) and 28.9% (344 of 1190) breast masses were malignant in the B-mode and Strain cohort, respectively. Distribution of BI-RADS categories was 33.0%, 34.5%, 14.5%, and 18.0% for BI-RADS 3, 4a, 4b, and 4c, respectively. In the external validation set (n = 342), the B-mode radiomics model (XGBoostTree) achieved an AUROC of 0.86 (95% CI 0.82 to 0.90), with a sensitivity of 97.4% (95% CI 0.93 to 1.00, 113 of 116) and a specificity of 27.0% (95% CI 0.21 to 0.33, 61 of 226). The model showed equivalent performance compared to the three ultrasound readers (P = 0.133); see also Table 1. In the external validation set (n = 333), the Strain radiomics model (MARS) achieved an AUROC of 0.84 (95% CI 0.79 to 0.88), with a sensitivity of 100% (95% CI 47.0 to 58.0, 115 of 115) and a specificity of 25.5% (95% CI 0.22 to 0.34, 60 of 218). The model showed equivalent performance compared to the three ultrasound readers (P = 0.696) and performed significantly better compared to SE (P = 0.002); see also Table 1. Sensitivity of the strain model was descriptively higher (100% vs. 97.4%, see Table 1). Both models were well-calibrated. Conclusion: This is the largest development and validation study for radiomics models based on B-mode breast ultrasound and SE, to date. The radiomics models performed on par with human readers, with the strain radiomics model showing potential to identify initially missed carcinomas in BI-RADS 3 breast masses. Future implementation studies may evaluate the performance of these image analysis algorithms in clinical routine and their integration into the multi-modal breast cancer diagnostics process, including mammography and MRI. Table 1. Diagnostic performance metrics Citation Format: André Pfob, Tanja He, Lie Cai, Richard G. Barr, Volker Duda, Zaher Alwafai, Corinne Balleyguier, Dirk-André Clevert, Sarah Fastner, Christina Gomez, Manuela Goncalo, Ines Gruber, Markus Hahn, André Hennigs, Panagiotis Kapetas, Sheng-Chieh Lu, Juliane Nees, Ralf Ohlinger, Fabian Riedel, Matthieu Rutten, Benedikt Schaefgen, Anne Stieber, Riku Togawa, Mitsuhiro Tozaki, Sebastian Wojcinski, Cai Xu, Geraldine Rauch, Jörg Heil, Chris Sidey-Gibbons, Michael Golatta. Radiomics Models for B-mode Breast Ultrasound and Strain Elastography to improve Breast Cancer Diagnosis (INSPiRED 005): An International, Multicenter Analysis [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 PO3-07-02.

BREAST CANCER DETECTION USING MACHINE LEARNING ALOGRITHM

Breast cancer is the most common and second most common cause of cancer in women. [3] Early diagnosis of breast cancer can provide better treatment and thus increase survival rates. Data classification using machine learning is widely used in cancer diagnosis and early cancer detection. The purpose of this literature review is to focus on the use of machine learning in classifying existing data in the early detection and diagnosis of breast cancer. When much scientific literature is examined, it is clearly seen that there are many methods for the diagnosis of breast cancer. The aim of this study is to provide a comprehensive review and recommendations on cancer screening and diagnosis. "Breast cancer type classification using machine learning."[1]Provides an overview of current research on multiple breast cancer cases using data mining techniques to improve breast cancer diagnosis and prognosis.. Key Words: breast cancer, machine learning, artificial neural networks, decision tree, support vector machine nearest neighbor, healthcare system, Wisconsin breast cancer database

A Comprehensive Analysis on the Efficacy of Machine Learning-Based Algorithms for Breast Cancer Classification

This research focuses on using machine learning to make breast cancer classification better. In this research various machine learning algorithm such as Decision Tree, Linear Discriminant, Support Vector Machine, K-Nearest Neighbors, Probabilistic Neural Network, Logistic Regression, Recurrent Neural Network, and Ensemble Method are used. We tested them using two different ways of splitting the data—90/10 and 70/30—and we also picked important features to consider. The Ensemble Method came in second place with accuracies of 98.2% and 97.6%. The Deep Neural Network performed really well too, with accuracies of 96.2% in the 90/10 split and 89.1% in the 70/30 split. We also found that selecting the right features improved accuracy a lot. This shows how important it is to choose the best features to make the models better. These results show that machine learning can be used to classify breast cancer effectively. The numbers prove that the Deep Neural Network and Ensemble methods have high accuracy, and selecting the right features makes them even better. The research outcomes introduces machine learning techniques that can improve breast cancer diagnosis, potentially changing the way doctors make decisions and improving patient outcomes.

Harnessing the power of radiomics and deep learning for improved breast cancer diagnosis with multiparametric breast mammography

Breast cancer, with its high mortality, faces diagnostic challenges due to variability in mammography quality and breast densities, leading to inconsistencies in radiological interpretations. Computer-aided diagnostic (CAD) systems, while helpful, struggle with accurately interpreting lesion characteristics such as morphology, density, and size. To address this, our study developed advanced deep-learning algorithms to improve the detection, localization, risk assessment, and classification of breast lesions, aiming to reduce false positives on human intervention and tackle slow convergence rates. Key innovations of the approach include preprocessing techniques, advanced filtering, and data augmentation strategies to optimize model performance, mitigating over- and under-fitting concerns. A significant development is the Chaotic Leader Selective Filler Swarm Optimization (cLSFSO) method, which effectively detects breast-dense lesions by extracting textural and statistical features. Additionally, the study adapted deep learning models like modified VGGNet and SE-ResNet152 through transfer learning, significantly enhancing their capability to distinguish between normal and suspicious mammography regions. The study also introduces hybrid deep neural network-based approaches, including CNN+LSTM and CNN+SVM, for diagnosing and grading cancerous polyps from the pre-segmented ROIs. Besides, the transfer learning paradigm is employed to boost the efficacy in classifying breast masses and reducing computing time by modifying the final layer of the proposed pre-trained models. The integration of Grad-CAM techniques further refines our analysis, leading to more accurate assessments and improved diagnosis of breast anomalies. Evaluated using benchmark and private datasets, our algorithms demonstrated a sensitivity of 0.99 and an overall AUC of 0.99, indicating significant improvements in mammogram analysis. These advancements aid radiologists, potentially improving patient outcomes and contributing to medical imaging and AI in healthcare.

Improving Breast Cancer Diagnosis Accuracy by Particle Swarm Optimization Feature Selection

Breast cancer has been one of the leading causes of death among women in the world. Early detection of this disease can save patient’s lives and reduce mortality. Due to the large number of features involved in the diagnosis of this disease, the breast cancer diagnosis process can be time consuming. To reduce cost and time and improving accuracy of breast cancer diagnosis, this paper propose a feature selection algorithm based on particle swarm optimization (PSO) combined with machine learning methods for selection the most effective features for breast cancer diagnosis among all features. In order to evaluate the efficiency of the proposed feature selection method, it was tested on three most common breast cancer datasets available in the University of California, Irvine (UCI) repository named: Coimbra dataset (CD), Wisconsin Diagnostic Breast Cancer dataset (WDBC) and Wisconsin Prognostic Breast Cancer dataset (WPBC). In the Coimbra dataset with all its 9 features and without PSO feature selection algorithm the highest obtained accuracy was 87% by Support Vector Machine method, while with PSO feature selection algorithm the accuracy reached to 91% and the number of features was reduced from 9 to 4. In the WDBC dataset with all its 30 features and without PSO feature selection algorithm the highest obtained accuracy was 99% by Random Forest method, while with PSO feature selection algorithm the accuracy reached to 100% and the number of features was reduced from 30 to 19. In the WPBC dataset with all its 33 features and without PSO feature selection algorithm the highest obtained accuracy was 94% by Support Vector Machine method, while with PSO feature selection algorithm the accuracy reached to 96% and the number of features was reduced from 33 to 17. The results of this paper indicated that the proposed feature selection algorithm based on PSO algorithm can improve the accuracy of breast cancer diagnosis. While it has selected fewer and more effective features than the total number of features in the original datasets.

Integrating hybrid transfer learning with attention-enhanced deep learning models to improve breast cancer diagnosis.

Cancer, with its high fatality rate, instills fear in countless individuals worldwide. However, effective diagnosis and treatment can often lead to a successful cure. Computer-assisted diagnostics, especially in the context of deep learning, have become prominent methods for primary screening of various diseases, including cancer. Deep learning, an artificial intelligence technique that enables computers to reason like humans, has recently gained significant attention. This study focuses on training a deep neural network to predict breast cancer. With the advancements in medical imaging technologies such as X-ray, magnetic resonance imaging (MRI), and computed tomography (CT) scans, deep learning has become essential in analyzing and managing extensive image datasets. The objective of this research is to propose a deep-learning model for the identification and categorization of breast tumors. The system's performance was evaluated using the breast cancer identification (BreakHis) classification datasets from the Kaggle repository and the Wisconsin Breast Cancer Dataset (WBC) from the UCI repository. The study's findings demonstrated an impressive accuracy rate of 100%, surpassing other state-of-the-art approaches. The suggested model was thoroughly evaluated using F1-score, recall, precision, and accuracy metrics on the WBC dataset. Training, validation, and testing were conducted using pre-processed datasets, leading to remarkable results of 99.8% recall rate, 99.06% F1-score, and 100% accuracy rate on the BreakHis dataset. Similarly, on the WBC dataset, the model achieved a 99% accuracy rate, a 98.7% recall rate, and a 99.03% F1-score. These outcomes highlight the potential of deep learning models in accurately diagnosing breast cancer. Based on our research, it is evident that the proposed system outperforms existing approaches in this field.

m B C C f : Multilevel Breast Cancer Classification Framework Using Radiomic Features

Breast cancer characterization remains a significant and challenging issue in contemporary medicine. Accurately distinguishing between malignant and benign breast lesions is crucial for effective diagnosis and treatment. The anatomical structure of malignant breast ultrasound images is more chaotic than that of benign images due to disease pathologies. However, texture-based analysis alone often fails to identify the extent of chaoticness in malignant breast ultrasound images due to their vague appearance with normal echo patterns, leading to missed diagnoses and increased mortality rates. To address this issue, we proposed an angular feature-based multilevel breast cancer classification framework mBCCf that aims to improve the accuracy and efficiency of classification. The proposed framework mimics the radiologist interpretation procedure by identifying the chaoticness on the periphery of the breast lesion in a breast ultrasound image (level-1). If the lesion contains an acute angle in any part of the periphery, it can be characterized as malignant or otherwise benign. However, solely relying on level-1 analysis may result in misclassification, especially when benign lesions exhibit echo patterns that resemble malignant ones. To overcome this limitation and to make the proposed system highly sensitive, advanced texture-based analysis (using combined shape, texture, and angular features) is performed (level-2). Finally, the performance of the proposed system is evaluated using a cross-dataset (consisting of 1293 breast ultrasound images) and compared with the different individual feature extraction techniques. Encouragingly, our system demonstrated an accuracy of 96.99% for classifying malignant and benign tumors, which is also validated using statistical analysis. The implications of our research lie in its potential to significantly improve breast cancer diagnosis by providing a reliable, efficient, and sensitive tool for radiologists.

Aptamer-Based Recognition of Breast Tumor Cells: A New Era for Breast Cancer Diagnosis.

Breast cancer is one of the leading causes of death among women worldwide and can be classified into four major distinct molecular subtypes based on the expression of specific receptors. Despite significant advances, the lack of biomarkers for detailed diagnosis and prognosis remains a major challenge in the field of oncology. This study aimed to identify short single-stranded oligonucleotides known as aptamers to improve breast cancer diagnosis. The Cell-SELEX technique was used to select aptamers specific to the MDA-MB-231 tumor cell line. After selection, five aptamers demonstrated specific recognition for tumor breast cell lines and no binding to non-tumor breast cells. Validation of aptamer specificity revealed recognition of primary and metastatic tumors of all subtypes. In particular, AptaB4 and AptaB5 showed greater recognition of primary tumors and metastatic tissue, respectively. Finally, a computational biology approach was used to identify potential aptamer targets, which indicated that CSKP could interact with AptaB4. These results suggest that aptamers are promising in breast cancer diagnosis and treatment due to their specificity and selectivity.

Building pathology capacity in sub-Saharan Africa to improve breast cancer diagnosis and treatment: training laboratory technicians in high-quality manual immunohistochemistry

BackgroundTo address the need for a skilled workforce in breast cancer (BC) pathology in sub-Saharan Africa (SSA), we implemented an education program to train laboratory technicians in manual immunohistochemistry (IHC).MethodsA quality improvement education project was developed. Interactive webinars were held every six months with didactics and presentations from African experts with experience in IHC. We conducted knowledge assessments and surveys on current practice, equipment, and human resources. A digital mentorship platform (DMP) was created for discussions, sharing SOPs, and networking. For one year (2022–2023), we followed developments in pathology capacity, practice changes, and educational needs. A paired t-test was used to calculate the significance of changes in knowledge immediately after the webinar and comfort level with topics 35 days after the webinar.ResultsTwo hundred and sixty six participants from 10 SSA countries attended the first webinar, a series of six lectures on IHC theory, methods, and practice. Ninety-five participants from nine SSA countries provided a baseline assessment of pathology capacity and feedback. Mean knowledge increased by 17.4% immediately after the webinar (from 41.8% pre-webinar to 59.2% post, p = < 0.0001). Self-reported comfort level in topics 35 days after the webinar increased by 11.3%, but this was not statistically significant (mean 3.36 pre- to 3.74 post, p = 0.1). Over six months, recordings were accessed 412 times. After six months, the second webinar had 93 participants from eight SSA countries. Membership in the DMP increased from 64 to 172; recordings were viewed 412 times in six months; and 113 participants from nine SSA countries completed surveys. Among 74 respondents who perform IHC, 43.5% reported moderate or significant positive practice changes such as improved antigen retrieval techniques and optimization of preanalytical variables. Over half (52.7%, n = 39) reported the quality of slides had moderately or significantly improved. After one year, a third webinar had 98 participants from eight SSA countries. Thirty-eight completed surveys, DMP membership increased to 199, and 1 reported launching IHC in a lab in Nigeria.ConclusionsOur program 1) reached hundreds of participants and provided a baseline assessment of pathology capacity across nine SSA countries; 2) created a novel mechanism to build pathology capacity and assess progress with this cohort; and 3) improved practices and the preparation of slides for over half performing manual IHC. After one year, interest was sustained. Tracking impact on diagnosis and treatment of BC in the region is needed long-term.

Automatic Pectoral Muscles and Artifacts Removal in Mammogram Images for Improved Breast Cancer Diagnosis

Automatic Pectoral Muscles and Artifacts Removal in Mammogram Images for Improved Breast Cancer Diagnosis