Improve Breast Cancer Diagnosis Research Articles

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.

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

Enhanced Breast Cancer Detection Using Modified Particle Swarm Optimization-Based Back Propagation Neural Network

Breast cancer detection faces challenges due to late diagnosis and inaccurate results, leading to improper treatment. Particle Swarm Optimization (PSO), a common optimization technique, often gets trapped in local optima, resulting in high false negatives and computational time. To address this, a study devised a modified PSO-based Back Propagation Neural Network (BPNN) using White Shark Optimization (WSO) for more accurate breast cancer detection. The research collected 1097 mammogram images from two medical centers and categorized them into benign, and malignant groups. Image preprocessing involved techniques like Otsu's method and contrast limited adaptive histogram equalization, with Fuzzy C Means used for segmentation. Region of Interest (ROI) extraction utilized Gabor filter and Local Binary Pattern, followed by weighted average fusion of textural features. WSPSO was then applied for optimal feature selection, and BPNN classified the identified features. MATLAB R2016a implemented the system. Evaluation metrics included accuracy, precision, sensitivity, specificity, False Positive Rate (FPR), and Computation Time (CT). Compared to PSO-BPNN, WSPSO-BPNN demonstrated superior performance. The accuracy, precision, sensitivity, specificity, FPR and CT of WSPSO-BPNN for malignant dataset were 96.88%, 99.08%, 96.43%, 97.92%, 2.08%, and 32.38 seconds, respectively, while the corresponding values for PSO-BPNN were 93.13%, 96.33%, 93.75%, 91.67%, 8.33% and 44.03 seconds, respectively. Also for the accuracy, precision, sensitivity, specificity, FPR and CT of WSPSO-BPNN for benign dataset were 96.15%, 98.14%, 96.34%, 95.71%, 4.29%, and 40.04 seconds, respectively, while the corresponding values for PSO-BPNN were 93.59%, 96.27%, 94.51%, 91.43%, 8.57% and 61.94 seconds respectively showcasing its potential for improving breast cancer diagnosis. Keywords: Back Propagation Neural Network, Breast Cancer, Classification, Mammogram Images, Particle Swarm Optimization, White Shark Optimization. Oyerinde, I.M., Olabiyisi, S.O., Falohun, A.A. &amp; Awodoye, O.O. (2024); Enhanced Breast Cancer Detection Using Modified Particle Swarm Optimization-Based Back Propagation Neural Network. Journal of Advances in Mathematical &amp; Computational Science. Vol. 12, No. 1. Pp 53-62. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N1P5

A systematic review of multimodal application of quantum dots in breast cancer diagnosis: Effective parameters, status and future perspectives

Breast cancer (BC) is now the greatest common malignancy type among women, and many women die from it each year. Early detection obviously is critical in this disease to effectively treat BC and prevent this tragedy. Among a numerous variety of novel diagnosis techniques that are employed to detect BC, nanoparticles (NPs) due to their superior properties have attracted the researchers’ attention. Various types of NPs, both alone and in conjunction with other methods including computed tomography (CT) and magnetic resonance imaging (MRI), have been employed for improving breast cancer diagnosis. The benefits of quantum dot (QD), a novel grouping of NPs with distinctive characteristics in the fields of size, surface modification techniques, optical properties, cellular uptake, and biomedical applications, are discussed in this work. In current review, issued studies concerned with the use of QDs in BC diagnosis were collected in a systematic and comprehensive manner. The QDs hold promise for non-invasive, fast, inexpensive, and precise early BC imaging also detection techniques.

Breast Cancer Detection Based on Convolutional Neural Networks

Abstract: Breast cancer remains a critical health concern, demanding early detection and accurate classification for effective treatment. In this research, we conduct a comparative study between a custom-designed Convolutional Neural Network (CNN) and the pre-trained DenseNet121 model for breast cancer detection and classification. We begin by curating a comprehensive dataset of breast cancer images and apply appropriate data preprocessing techniques for optimal model input. The dataset is divided into training, validation, and testing sets to evaluate model performance. The CNN model is constructed with multiple convolutional and pooling layers, followed by fully connected layers for classification. Meanwhile, the DenseNet121, a powerful pre-trained model, is fine-tuned for breast cancer detection. Through rigorous evaluation, we assess both models using metrics such as accuracy, precision, recall, F1 score, and AUC-ROC. Our results demonstrate that the DenseNet121 outperforms the custom CNN model, achieving higher accuracy and reliability in identifying and classifying breast cancer. To ensure wider accessibility, we integrate the superior DenseNet121 model into a user-friendly web-based interface using Python Flask. This interface empowers medical professionals and the general public to perform real-time breast cancer predictions with ease. Ethical considerations are paramount, ensuring data privacy, security, and transparency in all model predictions. In conclusion, our comparative study highlights the superiority of the pre-trained DenseNet121 model over a custom CNN for breast cancer detection and classification. By leveraging advanced deep learning techniques and a user-friendly interface, our research contributes to improved breast cancer diagnosis and patient care on a broader scale

A structured combination of ensemble classifier and filter-based feature selection to improve breast cancer diagnosis.

Advances in technology have led to the emergence of computerized diagnostic systems as intelligent medical assistants. Machine learning approaches cannot replace professional humans, but they can change the treatment of diseases such as cancer and be used as medical assistants. Breast cancer treatment can be very effective, especially when the disease is detected in the early stages. Feature selection and classification are common data mining techniques in machine learning that can provide breast cancer diagnosis with high speed, low cost and high precision. This paper proposes a new intelligent approach using an integrated filter-evolutionary search-based feature selection and an optimized ensemble classifier for breast cancer diagnosis. The selected features mainly relate to the viable solution as the selected features are successfully used in the breast cancer disease classification process. The proposed feature selection method selects the most informative features from the original feature set by integrating adaptive thresholder information gain-based feature selection and evolutionary gravity-search-based feature selection. Meanwhile, classification model is done by proposing a new intelligent multi-layer perceptron neural network-based ensemble classifier. The simulation results show that the proposed method provides better performance compared to the state-of-the-art algorithms in terms of various criteria such as accuracy, sensitivity and specificity. Specifically, the proposed method achieves an average accuracy of 99.42% on WBCD, WDBC and WPBC datasets from Wisconsin database with only 56.7% of features. Systems based on intelligent medical assistants configured with machine learning approaches are an important step toward helping doctors to detect breast cancer early.

Survey and comparative analysis of machine learning algorithms for breast cancer diagnosis: A comprehensive review

Breast cancer is a significant health concern worldwide, and early and accurate diagnosis plays a crucial role in improving patient outcomes. Machine learning algorithms have emerged as powerful tools for analyzing complex medical data and aiding in the diagnosis of breast cancer. This paper provides an overview of the application of machine learning algorithms in breast cancer diagnosis. The findings indicate that machine learning algorithms, such as support vector machines (SVM), random forests, artificial neural networks (ANN), and deep learning models, have been extensively explored for breast cancer diagnosis. These algorithms leverage the vast amounts of available data, including patient demographics, medical history, imaging data (mammography, ultrasound, MRI), and genetic profiles, to identify patterns and make predictions. One of the primary applications of machine learning algorithms in breast cancer diagnosis is the classification of tumors as malignant or benign. By training on labeled datasets, these algorithms can learn to differentiate between cancerous and non-cancerous cases, thus assisting in accurate tumor diagnosis. Additionally, machine learning algorithms can be used to predict the likelihood of cancer recurrence, which helps guide treatment decisions and post-treatment monitoring. Feature selection and extraction techniques also play a vital role in breast cancer diagnosis using machine learning algorithms. These techniques aim to identify the most relevant features or biomarkers associated with breast cancer, reducing the dimensionality of the data and enhancing the performance of the models. Feature selection algorithms, such as recursive feature elimination and correlation-based feature selection, contribute to the identification of critical indicators for accurate diagnosis. Furthermore, the integration of different data sources and modalities, such as combining clinical data with imaging data or genetic data, has shown promise in improving breast cancer diagnosis accuracy. By fusing multiple types of information, machine learning algorithms can leverage the complementary nature of these data sources to enhance diagnostic capabilities. Despite the advancements made, challenges remain in the field of breast cancer diagnosis using machine learning algorithms. Issues such as data quality, interpretability of models, and generalizability to diverse populations need to be addressed to ensure the reliable and equitable application of these algorithms in clinical practice.

Retracted: Value of Artificial Neural Network Ultrasound in Improving Breast Cancer Diagnosis.

[This retracts the article DOI: 10.1155/2022/1779337.].

How Radiomics Can Improve Breast Cancer Diagnosis and Treatment.

Recent technological advances in the field of artificial intelligence hold promise in addressing medical challenges in breast cancer care, such as early diagnosis, cancer subtype determination and molecular profiling, prediction of lymph node metastases, and prognostication of treatment response and probability of recurrence. Radiomics is a quantitative approach to medical imaging, which aims to enhance the existing data available to clinicians by means of advanced mathematical analysis using artificial intelligence. Various published studies from different fields in imaging have highlighted the potential of radiomics to enhance clinical decision making. In this review, we describe the evolution of AI in breast imaging and its frontiers, focusing on handcrafted and deep learning radiomics. We present a typical workflow of a radiomics analysis and a practical "how-to" guide. Finally, we summarize the methodology and implementation of radiomics in breast cancer, based on the most recent scientific literature to help researchers and clinicians gain fundamental knowledge of this emerging technology. Alongside this, we discuss the current limitations of radiomics and challenges of integration into clinical practice with conceptual consistency, data curation, technical reproducibility, adequate accuracy, and clinical translation. The incorporation of radiomics with clinical, histopathological, and genomic information will enable physicians to move forward to a higher level of personalized management of patients with breast cancer.

Breast MRI: Clinical Indications, Recommendations, and Future Applications in Breast Cancer Diagnosis.

This article aims to provide an updated overview of the indications for diagnostic breast magnetic resonance imaging (MRI), discusses the available and novel imaging exams proposed for breast cancer detection, and discusses considerations when performing breast MRI in the clinical setting. Breast MRI is superior in identifying lesions in women with a very high risk of breast cancer or average risk with dense breasts. Moreover, the application of breast MRI has benefits in numerous other clinical cases as well; e.g., the assessment of the extent of disease, evaluation of response to neoadjuvant therapy (NAT), evaluation of lymph nodes and primary occult tumor, evaluation of lesions suspicious of Paget's disease, and suspicious discharge and breast implants. Breast cancer is the most frequently detected tumor among women around the globe and is often diagnosed as a result of abnormal findings on mammography. Although effective multimodal therapies significantly decline mortality rates, breast cancer remains one of the leading causes of cancer death. A proactive approach to identifying suspicious breast lesions at early stages can enhance the efficacy of anti-cancer treatments, improve patient recovery, and significantly improve long-term survival. However, the currently applied mammography to detect breast cancer has its limitations. High false-positive and false-negative rates are observed in women with dense breasts. Since approximately half of the screening population comprises women with dense breasts, mammography is often incorrectly used. The application of breast MRI should significantly impact the correct cases of breast abnormality detection in women. Radiomics provides valuable data obtained from breast MRI, further improving breast cancer diagnosis. Introducing these constantly evolving algorithms in clinical practice will lead to the right breast detection tool, optimized surveillance program, and individualized breast cancer treatment.

Deep neural network incorporating domain and resolution transformations model for histopathological image classification

To accurately diagnose breast cancer, pathologists take biopsy and perform microscopic examination. However, this procedure is inconvenient, time-consuming, and requires high cost. To overcome these challenges, a deep neural network is used to offer a computer-based observation to categorize breast cancer as benign or malignant. In this study, the binary classification of breast cancer from histopathological images is proposed using multistage transfer learning incorporating domain and resolution transformations. We trained our models with a publicly available BreaKHis dataset. We modified and fine-tuned the well-known architectures, VGG19, ResNet (50 and 101), InceptionV3, Xception, Inception-ResNetV2, and NASNet-Large to classify 40 × magnification histopathology images as benign or malignant, then transferred the pre-trained weights for 100 × , 200 × , and 400 × magnification classifications. Translation of the proposed CNN based multistage transfer learning to vision transformer was also performed. Our approach achieved a remarkable performance by demonstrating a test accuracy of 93.97%100% and an F1-score of 0.911.00. The results from our study depict the use of multistage transfer learning improves the breast histopathological images classification performance. Therefore, the proposed approach might be applied in a clinical setting to offer improved breast cancer diagnosis.

Improving breast cancer diagnosis by incorporating raw ultrasound parameters into machine learning

The improved diagnostic accuracy of ultrasound breast examinations remains an important goal. In this study, we propose a biophysical feature-based machine learning method for breast cancer detection to improve the performance beyond a benchmark deep learning algorithm and to furthermore provide a color overlay visual map of the probability of malignancy within a lesion. This overall framework is termed disease-specific imaging. Previously, 150 breast lesions were segmented and classified utilizing a modified fully convolutional network and a modified GoogLeNet, respectively. In this study multiparametric analysis was performed within the contoured lesions. Features were extracted from ultrasound radiofrequency, envelope, and log-compressed data based on biophysical and morphological models. The support vector machine with a Gaussian kernel constructed a nonlinear hyperplane, and we calculated the distance between the hyperplane and each feature’s data point in multiparametric space. The distance can quantitatively assess a lesion and suggest the probability of malignancy that is color-coded and overlaid onto B-mode images. Training and evaluation were performed on in vivo patient data. The overall accuracy for the most common types and sizes of breast lesions in our study exceeded 98.0% for classification and 0.98 for an area under the receiver operating characteristic curve, which is more precise than the performance of radiologists and a deep learning system. Further, the correlation between the probability and Breast Imaging Reporting and Data System enables a quantitative guideline to predict breast cancer. Therefore, we anticipate that the proposed framework can help radiologists achieve more accurate and convenient breast cancer classification and detection.