Detection Of Breast Cancer Research Articles

Contrast-enhanced mammography improves patient access to functional breast imaging.

Imaging research pathways focus increasingly on the development of individualised approaches to breast cancer detection, diagnosis and management. Detection of breast cancer with X-ray mammography may fail in some cancer subtypes with limited changes in morphology/tissue density and in women with dense breasts. International organisations offer recommendations for contrast-enhanced breast imaging, as it provides superior sensitivity for screening, local staging and assessment of neoadjuvant treatment response, when compared with standard X-ray mammography (including tomosynthesis) and breast ultrasound. Arguably, the evidence base is stronger for contrast-enhanced MRI (CE-MRI). Unfortunately, patient access to breast MRI in rural and remote areas is limited by practical limitations and equipment licensing restrictions. Moreover, breast MRI is an expensive test, likely to be out of reach for many women. Contrast-enhanced mammography (CEM) offers an attractive alternative to improve patient access to functional breast imaging. It is a new type of digital, dual energy X-ray mammography that can be performed on most modern units, following a relatively inexpensive hard- and software upgrade. In this paper, we review the rapidly accumulating evidence that CEM can provide similar diagnostic accuracy to CE-MRI, though at a significantly lower cost and offering greater comfort to the patient. The adoption of CEM can help meet the anticipated increased demand for CE-MRI.

Enhanced Breast Cancer Diagnosis Through an Integration of MSVM and Genetic Algorithms

Breast cancer remains a major health concern among women, often spreading from the breast to other parts of the body, and is the second leading cause of death in women. While early detection significantly increases the chances of successful treatment, current methods face challenges in accuracy and efficiency. This paper presents an approach for early-stage breast cancer detection using a combination of genetic algorithms and decision trees. The genetic algorithm, an iterative technique, enhances the speed of results, while the decision tree aids in classification. Additionally, a multi-support vector machine (MSVM) is employed for feature extraction and comparison with trained images. The proposed model is evaluated based on classification accuracy, precision, F-score, and recall. Simulation results demonstrate improved performance over existing systems

MR_NET: A Method for Breast Cancer Detection and Localization from Histological Images Through Explainable Convolutional Neural Networks

Breast cancer is the most prevalent cancer among women globally, making early and accurate detection essential for effective treatment and improved survival rates. This paper presents a method designed to detect and localize breast cancer using deep learning, specifically convolutional neural networks. The approach classifies histological images of breast tissue as either tumor-positive or tumor-negative. We utilize several deep learning models, including a custom-built CNN, EfficientNet, ResNet50, VGG-16, VGG-19, and MobileNet. Fine-tuning was also applied to VGG-16, VGG-19, and MobileNet to enhance performance. Additionally, we introduce a novel deep learning model called MR_Net, aimed at providing a more accurate network for breast cancer detection and localization, potentially assisting clinicians in making informed decisions. This model could also accelerate the diagnostic process, enabling early detection of the disease. Furthermore, we propose a method for explainable predictions by generating heatmaps that highlight the regions within tissue images that the model focuses on when predicting a label, revealing the detection of benign, atypical, and malignant tumors. We evaluate both the quantitative and qualitative performance of MR_Net and the other models, also presenting explainable results that allow visualization of the tissue areas identified by the model as relevant to the presence of breast cancer.

OB-TSASA Guided Soft Voting Ensemble Classifier for Breast Cancer Classification Using Thermal Infrared Images

Nowadays, the use of thermal infrared images has become more popular in breast cancer detection. Thermal image analysis with machine learning algorithms allows physicians to make accurate decisions on cancer diagnostics. Hence, this paper introduces a novel automated breast cancer classifier using thermal infrared images. Initially, the features from the DMR – IR dataset are extracted by an integrated approach of Kernel Principle Fast Independent-Component Analysis (KPFICA) and deep object-centric pooling convolution neural network (DOCP-CNN). Additionally, the DOCP-CNN hyperparameters are tuned by the Harris Hawks Optimization (HHO) algorithm. Next, the proposed oppositional based tunicate swarm algorithm with simulated annealing (OB-TSASA) optimization is used to select the relevant features from the extracted features. Finally, the OB-TSASA guided soft voting ensemble classifier is proposed to detect and classify cancerous patients. The experiment is conducted on the DMR – IR dataset to estimate the effectiveness of the proposed method. Furthermore, the proposed method produces outstanding classification results in terms of AUC, accuracy, F1-score, sensitivity, MCC, and kappa statistics. It is observed that the classification accuracy is 97.73% which is superior to other classifiers and the error rate is 2.27% which is very low compared to other methods.

Artificial intelligence-based automated determination in breast and colon cancer and distinction between atypical and typical mitosis using a cloud-based platform

Artificial intelligence (AI) technology in pathology has been utilized in many areas and requires supervised machine learning. Notably, the annotations that define the ground truth for the identification of different confusing process pathologies, vary from study to study. In this study, we present our findings in the detection of invasive breast cancer for the IHC/ISH assessment system, along with the automated analysis of each tissue layer, cancer type, etc. in colorectal specimens. Additionally, models for the detection of atypical and typical mitosis in several organs were developed using existing whole-slide image (WSI) sets from other AI projects. All H&E slides were scanned by different scanners with a resolution of 0.12–0.50 μm/pixel, and then uploaded to a cloud-based AI platform. Convolutional neural networks (CNN) training sets consisted of invasive carcinoma, atypical and typical mitosis, and colonic tissue elements (mucosa-epithelium, lamina propria, muscularis mucosa, submucosa, muscularis propria, subserosa, vessels, and lymph nodes). In total, 59 WSIs from 59 breast cases, 217 WSIs from 54 colon cases, and 28 WSIs from 23 different types of tumor cases with relatively higher amounts of mitosis were annotated for the training. The harmonic average of precision and sensitivity was scored as F1 by AI. The final AI models of the Breast Project showed an F1 score of 94.49% for Invasive carcinoma. The mitosis project showed F1 scores of 80.18%, 97.40%, and 97.68% for mitosis, atypical, and typical mitosis layers, respectively. Overall F1 scores for the current results of the colon project were 90.02% for invasive carcinoma, 94.81% for the submucosa layer, and 98.02% for vessels and lymph nodes. After the training and optimization of the AI models and validation of each model, external validators evaluated the results of the AI models via blind-reader tasks. The AI models developed in this study were able to identify tumor foci, distinguish in situ areas, define colonic layers, detect vessels and lymph nodes, and catch the difference between atypical and typical mitosis. All results were exported for integration into our in-house applications for breast cancer and AI model development for both whole-block and whole-slide image-based 3D imaging assessment.

The effectiveness of a mobile mammography units in breast cancer screening

Breast cancer remains the leading cause of death in the female population. Screening studies are conducted in order to detect the disease at an early stage. At the same time, domestic authors emphasize the lack of alternatives to preventive mammography in breast cancer screening and the prospects of using mobile mammographs for early diagnosis of breast cancer and precancerous breast diseases.Aim. To evaluat the results of the use of mobile mammography units for active and early detection of breast cancer.Materials and methods. The research uses methods of content analysis, information and analytical materials of Russian and foreign researchers, and forms of federal statistical observation. Statistical research methods were used to analyze the data obtained.Results. Regression analysis with a very low probability of error (p = 0.001) showed that the larger the total volume of mammographic studies with preventive purposes in the subjects of the Russian Federation, the higher the proportion of cases of active detection of breast cancer, although this relationship is rather weak. Taking into account the obtained value of R2 (0.192), only 19.2% of the increase in the proportion of breast cancer cases detected actively can be explained by an increase in the volume of preventive mammograms. The correlation analysis showed that the volumes of preventive mammograms performed using mobile devices in the subjects of the Russian Federation were not associated with either the proportion of breast cancer cases detected actively or with the proportion of breast cancer cases detected in the early stages. At the same time, it is worth noting that the obtained value of p ≥ 0.5 indicates a statistically insignificant result of evaluating the relationship between variables, the interpretation of which should be performed with caution.Conclusion. Despite the high cost of mobile mammography units, the increase in their number in the subjects of the Russian Federation and the growth in their activities, the contribution of these diagnostic devices to solving the problem of early and active diagnosis of breast cancer remains uncertain. More promising are personalized approaches to the timely detection of cancers, including breast cancer, based on an assessment of the individual risk of their occurrence.

Single Vesicle Surface Protein Profiling and Machine Learning-Based Dual Image Analysis for Breast Cancer Detection.

Single-vesicle molecular profiling of cancer-associated extracellular vesicles (EVs) is increasingly being recognized as a powerful tool for cancer detection and monitoring. Mask and target dual imaging is a facile method to quantify the fraction of the molecularly targeted population of EVs in biofluids at the single-vesicle level. However, accurate and efficient dual imaging vesicle analysis has been challenging due to the interference of false signals on the mask images and the need to analyze a large number of images in clinical samples. In this work, we report a fully automatic dual imaging analysis method based on machine learning and use it with dual imaging single-vesicle technology (DISVT) to detect breast cancer at different stages. The convolutional neural network Resnet34 was used along with transfer learning to produce a suitable machine learning model that could accurately identify areas of interest in experimental data. A combination of experimental and synthetic data were used to train the model. Using DISVT and our machine learning-assisted image analysis platform, we determined the fractions of EpCAM-positive EVs and CD24-positive EVs over captured plasma EVs with CD81 marker in the blood plasma of pilot HER2-positive breast cancer patients and compared to those from healthy donors. The amount of both EpCAM-positive and CD24-positive EVs was found negligible for both healthy donors and Stage I patients. The amount of EpCAM-positive EVs (also CD81-positive) increased from 18% to 29% as the cancer progressed from Stage II to III. No significant increase was found with further progression to Stage IV. A similar trend was found for the CD24-positive EVs. Statistical analysis showed that both EpCAM and CD24 markers can detect HER2-positive breast cancer at Stages II, III, or IV. They can also differentiate individual cancer stages except those between Stage III and Stage IV. Due to the simplicity, high sensitivity, and high efficiency, the DISVT with the AI-assisted dual imaging analysis can be widely used for both basic research and clinical applications to quantitatively characterize molecularly targeted EV subtypes in biofluids.

A Multi-label Artificial Intelligence Approach for Improving Breast Cancer Detection With Mammographic Image Analysis.

Breast cancer remains a major global health concern. This study aimed to develop a deep-learning-based artificial intelligence (AI) model that predicts the malignancy of mammographic lesions and reduces unnecessary biopsies in patients with breast cancer. In this retrospective study, we used deep-learning-based AI to predict whether lesions in mammographic images are malignant. The AI model learned the malignancy as well as margins and shapes of mass lesions through multi-label training, similar to the diagnostic process of a radiologist. We used the Curated Breast Imaging Subset of Digital Database for Screening Mammography. This dataset includes annotations for mass lesions, and we developed an algorithm to determine the exact location of the lesions for accurate classification. A multi-label classification approach enabled the model to recognize malignancy and lesion attributes. Our multi-label classification model, trained on both lesion shape and margin, demonstrated superior performance compared with models trained solely on malignancy. Gradient-weighted class activation mapping analysis revealed that by considering the margin and shape, the model assigned higher importance to border areas and analyzed pixels more uniformly when classifying malignant lesions. This approach improved diagnostic accuracy, particularly in challenging cases, such as American College of Radiology Breast Imaging-Reporting and Data System categories 3 and 4, where the breast density exceeded 50%. This study highlights the potential of AI in improving the diagnosis of breast cancer. By integrating advanced techniques and modern neural network designs, we developed an AI model with enhanced accuracy for mammographic image analysis.

Breast cancer imaging-clinical experience with two-dimensional-shear wave elastography: A retrospective study.

Breast cancer morbidity has been increasing worldwide, but treatments are improving. The therapeutic response depends on the stage at which the disease is diagnosed. Therefore, early diagnosis has never been more essential for successful treatment and a reduction in mortality rates. Radiology plays a pivotal role in cancer detection, and advances in ultrasound (US) palpation have shown promising results for breast cancer imaging. The addition of two-dimensional-shear wave elastography (2D-SWE) US in the routine breast imaging exam can increase early cancer detection and promote better surveillance. To evaluate the clinical applications of 2D-SWE US in breast cancer detection and its combination with other imaging modalities. The 200 consecutive female patients aged 50-80 were examined to evaluate palpable breast lesions. All patients underwent mammography, bright mode (B-mode) US, and 2D-SWE followed by US-guided biopsy in two consecutive sessions. Combining B-mode and shear wave US imaging with X-ray mammography revealed 100% of the suspicious lesions, resulting in greater sensitivity, specificity, and negative predictive value. The result improves compared to either B-mode or 2D-SWE alone (P = 0.02). Combining 2D-SWE with conventional US and X-ray techniques improves the chance of early cancer detection. Including 2D-SWE in regular breast imaging routines can reduce the need for biopsies and improve the chance of early cancer detection and survivability with the proper line of therapy.

Discovery of Plasma Lipids as Potential Biomarkers Distinguishing Breast Cancer Patients from Healthy Controls

The development of a sensitive and specific blood test for the early detection of breast cancer is crucial to improve screening and patient outcomes. Existing methods, such as mammography, have limitations, necessitating the exploration of alternative approaches, including circulating factors. Using 598 prospectively collected blood samples, a multivariate plasma-derived lipid biomarker signature was developed that can distinguish healthy control individuals from those with breast cancer. Liquid chromatography with high-resolution and tandem mass spectrometry (LC-MS/MS) was employed to identify lipids for both extracellular vesicle-derived and plasma-derived signatures. For each dataset, we identified a signature of 20 lipids using a robust, statistically rigorous feature selection algorithm based on random forest feature importance applied to cross-validated training samples. Using an ensemble of machine learning models, the plasma 20-lipid signature generated an area under the curve (AUC) of 0.95, sensitivity of 0.91, and specificity of 0.79. The results from this study indicate that lipids extracted from plasma can be used as target analytes in the development of assays to detect the presence of early-stage breast cancer.

Referral and disease patterns of patients treated at the Nicosia General Hospital Breast Centre

Abstract Background Cyprus has one of the lowest mortality rates from breast cancer in Europe. The reasons behind this have not been investigated but it is speculated that the ease of access to Breast Centers in combination with an increased public awareness play a role. The aim of this study was to investigate referral and disease presentation patterns of patients treated at the Nicosia General Hospital (NGH) Breast Centre and define opportunities for effective public health interventions. Methods A retrospective study of all patients (585) with in situ or invasive breast cancer treated at the NGH Breast Centre between February 2017 and November 2023 was conducted. Data on referral sources, symptoms, and cancer stage at the time of diagnosis were analyzed using Stata 18. Results Most patients were referrals from the national population screening program (42.2%), followed by self-referrals (40.6%), and General Practitioner (GP)/or other specialist referrals (17.2%). Half of the patients (50.3%) were asymptomatic at diagnosis. In addition, 8% of the patients diagnosed with invasive breast cancer were Stage I, 65% Stage II, and 27% Stage III. Conclusions Our study provides important insights forming the basis of public health activities for the early detection of breast cancer based on the fact that 20% of newly diagnosed breast cancer cases in Cyprus receive treatment in NGH Breast Center. Although adherence to the national population screening program does not meet desired expectations, a significant proportion of breast cancers were detected via this program highlighting its effectiveness in early detection of asymptomatic cancers. A large proportion of patients were self-referred underlining the high degree of breast cancer awareness in Cyprus but also the media-induced anxiety which leads to early investigations. Key messages • Screening program and breast cancer awareness campaigns are effective in detecting breast cancers early. • These activities should be sustained to further reduce breast cancer mortality rate.

Breast Lesion Detection for Ultrasound Images Using MaskFormer

This study evaluates the performance of the MaskFormer model for segmenting and classifying breast lesions using ultrasound images, addressing ultrasound’s limitations. Ultrasound used for breast cancer detection faces challenges like low image contrast and difficulty in the detection of small or multiple lesions, further complicated by variability based on operator skill. Initial experiments with U-Net and other CNN-based models revealed constraints, such as early plateauing in model loss, which indicated suboptimal learning and performance. In contrast, MaskFormer demonstrated continuous improvement, achieving higher precision in breast lesion segmentation and significantly reducing both false positives and false negatives. Comparative analysis showed MaskFormer’s superior performance, with the highest precision and recall rates for malignant lesions and an overall mean average precision (mAP) of 0.943. The model’s ability to detect a diverse range of breast lesions, including those potentially missed by the human eye, especially by less experienced practitioners, underscores its potential. These findings suggest that integrating AI models like MaskFormer could greatly enhance ultrasound performance for breast cancer detection, providing reliable, operator-independent image analysis and potentially improving patient outcomes on a global scale.

Key LncRNAs Associated with Distant Metastasis in Breast Cancer: A System Biology Analysis.

Breast cancer (BC) is the most prevalent cancer among women globally. Metastasis is the leading cause of mortality in most cancers. Early BC detection before metastasis can enhance survival rates. Understanding BC metastasis mechanisms could aid in developing metastasis-specific treatments. The role of long non-coding RNAs (lncRNA) in cancer progression is recognized, yet the importance of specific lncRNAs in BC, despite potential alterations, remains inadequately explored. We utilized bioinformatics tools to identify novel lncRNAs dysregulated in metastasis. To achieve this objective, the gene expression profile of GSE102484, encompassing metastatic and non-metastatic BC tissue samples, was analyzed using the limma package in R with cut-off criteria set at an adjusted p-value < 0.005 and |fold change (FC)| ≥ 0.5. We used WGCNA analysis to find co-expression genes for lncRNAs. Then, we identified hub genes and performed pathway enrichment to better understand the results. Considering the defined criteria, eight novels of dysregulated lncRNAs and top 10 miRNAs were identified. Dysregulated lncRNAs are found in yellow, green, brown, purple, and turquoise co-expression modules from WGCNA analysis. Enrichment analysis of these co-expressed modules revealed relevant pathways to metastasis, such as epithelial-to-mesenchymal transition and integrin cell-surface interactions, as well as regulation of HIF1-alpha. In addition, SDPR, TGFB1I1, ILF3, KIF4A, and COL5A1 were identified as hub genes. Based on DElncRNA-miRNADEmRNA connections and co-expression, we ultimately constructed lncRNA-associated ceRNA axes. The current study may identify novel lncRNAs implicated in BC metastasis; still, additional research is required to determine the potential functions of these lncRNAs in BC metastasis.

Proportion of Breast Self Examination Practice and its Associated Factors among Female Health Professionals in Government Medical College Hospital , Thiruvananthapuram.

Breast cancer is a leading cause of death among females across the globe. Breast self- examination is considered as a cornerstone testing method for breast cancer screening and early detection. Breast Self Examination is one of the simple, quick and cost free procedures for early detection of breast cancer among women. The Present study was intended to estimate the proportion of Breasts self examination practice and its associated factors among female health professionals in Government Medical College Hospital, Thiruvananthapuram. The research design adopted for this study was descriptive cross sectional design. The sample consists of 330 female health professionals. Sample fulfilling the eligibility criteria were selected through simple random sampling method and data were collected by giving questionnaire. The collected data was analyzed by using SPSS version 23 and the results were expressed in descriptive and inferential statistics. The collected data showed that about 68% of the participants were doing breast self examination practice and there was a significant association between breast self examination practice and associated factors such as age, basic qualification, highest qualification, profession, years of experience, screening for breast cancer and knowledge and attitude regarding breast self examination practice.The study concluded that adressing all these factors can improve breast self examination practice among female health professionals.

G4 & the balanced metric family – a novel approach to solving binary classification problems in medical device validation & verification studies

BackgroundIn medical device validation and verification studies, the area under the receiver operating characteristic curve (AUROC) is often used as a primary endpoint despite multiple reports showing its limitations. Hence, researchers are encouraged to consider alternative metrics as primary endpoints. A new metric called G4 is presented, which is the geometric mean of sensitivity, specificity, the positive predictive value, and the negative predictive value. G4 is part of a balanced metric family which includes the Unified Performance Measure (also known as P4) and the Matthews’ Correlation Coefficient (MCC). The purpose of this manuscript is to unveil the benefits of using G4 together with the balanced metric family when analyzing the overall performance of binary classifiers.ResultsSimulated datasets encompassing different prevalence rates of the minority class were analyzed under a multi-reader-multi-case study design. In addition, data from an independently published study that tested the performance of a unique ultrasound artificial intelligence algorithm in the context of breast cancer detection was also considered. Within each dataset, AUROC was reported alongside the balanced metric family for comparison. When the dataset prevalence and bias of the minority class approached 50%, all three balanced metrics provided equivalent interpretations of an AI’s performance. As the prevalence rate increased / decreased and the data became more imbalanced, AUROC tended to overvalue / undervalue the true classifier performance, while the balanced metric family was resistant to such imbalance. Under certain circumstances where data imbalance was strong (minority-class prevalence < 10%), MCC was preferred for standalone assessments while P4 provided a stronger effect size when evaluating between-groups analyses. G4 acted as a middle ground for maximizing both standalone assessments and between-groups analyses.ConclusionsUse of AUROC as the primary endpoint in binary classification problems provides misleading results as the dataset becomes more imbalanced. This is explicitly noticed when incorporating AUROC in medical device validation and verification studies. G4, P4, and MCC do not share this limitation and paint a more complete picture of a medical device’s performance in a clinical setting. Therefore, researchers are encouraged to explore the balanced metric family when evaluating binary classification problems.

Breast Cancer Detection System Using Machine Learning

AI is transforming the medical field, especially in early detection and categorization of breast cancer. Deep learning models like CNNI-BCC are displaying impressive capabilities in examining MRI images to precisely recognize different types of breast cancer. This advancement in technology has significant implications for enhancing patient outcomes. By utilizing AI, healthcare professionals can now receive more accurate and prompt diagnoses. CNNI- BCC's exceptional accuracy in analyzing MRI images allows for earlier detection of breast cancer, which is crucial for successful treatment. This technology enables doctors to identify potential abnormalities that may have been missed by traditional methods, leading to more effective interventions.

Combining metabolomics and machine learning to discover biomarkers for early-stage breast cancer diagnosis.

There is an urgent need for better biomarkers for the detection of early-stage breast cancer. Utilizing untargeted metabolomics and lipidomics in conjunction with advanced data mining approaches for metabolism-centric biomarker discovery and validation may enhance the identification and validation of novel biomarkers for breast cancer screening. In this study, we employed a multimodal omics approach to identify and validate potential biomarkers capable of differentiating between patients with breast cancer and those with benign tumors. Our findings indicated that ether-linked phosphatidylcholine exhibited a significant difference between invasive ductal carcinoma and benign tumors, including cases with inconsistent mammography results. We observed alterations in numerous lipid species, including sphingomyelin, triacylglycerol, and free fatty acids, in the breast cancer group. Furthermore, we identified several dysregulated hydrophilic metabolites in breast cancer, such as glutamate, glycochenodeoxycholate, and dimethyluric acid. Through robust multivariate receiver operating characteristic analysis utilizing machine learning models, either linear support vector machines or random forest models, we successfully distinguished between cancerous and benign cases with promising outcomes. These results emphasize the potential of metabolic biomarkers to complement other criteria in breast cancer screening. Future studies are essential to further validate the metabolic biomarkers identified in our study and to develop assays for clinical applications.

How social class shapes breast cancer risk perspectives and prevention practices of Australian midlife women: a qualitative study using the concept of ‘breast cancer candidacy’

BackgroundThe increasing incidence of breast cancer and disease burden is a significant public health concern. While 30% of breast cancers could be prevented through addressing modifiable risk factors, misconceptions among women about breast cancer risks hamper primary prevention. In the absence of primary prevention, secondary prevention such as mammography increases the early detection of breast cancer and improves health outcomes. However, current population-level screening rates indicate secondary prevention is suboptimal. More effective public health efforts to improve breast cancer prevention are required. Given breast cancer is socially patterned, this work explores how social class impacts women’s breast cancer prevention practices. This study uses the concepts of lay epidemiology and candidacy as a mechanism to understand women’s breast cancer risk perspectives. It engages Bourdieu’s relational social class theory to unpack how women’s social, cultural, and structured life contexts shape these perspectives and their considerations regarding primary and secondary prevention.MethodsIn this qualitative study 43 Australian midlife women (aged 45–64 years), were interviewed to explore their understandings of breast cancer risks, how they perceived their own risk, and how this shaped their prevention behaviours. A theory-informed thematic analysis applying Bourdieu’s concepts of habitus, capital, and fields to understand how women’s social class positions shapes risk perspectives and prevention practices was conducted.ResultsThis social class analysis showed differences in how women engage in breast cancer discourse, consider risks, and participate in breast cancer prevention. Middle-class women prioritise health promoting practices and were more likely than working-class and affluent women to attend mammography screening. Working-class women experience structural factors, like low income, stress and difficult life circumstances, which hamper primary prevention practices and for some screening is not considered or prioritised, and their decisions not to screen are less active. Affluent women often do not consider themselves at-risk due to their healthier ‘lifestyles. ’They suggest that this, and their knowledge of screening benefits and harms allows them to make informed decisions not to screen.ConclusionsWomen interpret and understand breast cancer risks differently and enact prevention practices within the parameters afforded by their social class positions. These findings are useful to inform improved public health approaches regarding both modifiable breast cancer risks and increasing mammography screening. To improve equity in breast cancer prevention efforts, such approaches must respond to limitations based on social class and address structural factors that impact prevention practices.

Serum metabolite and metal ions profiles for breast cancer screening

Enhancing early-stage breast cancer detection requires integrating additional screening methods with current diagnostic imaging. Omics screening, using easily collectible serum samples, could serve as an initial step. Alongside biomarker identification capabilities, omics analysis allows for a comprehensive analysis of prevalent histological types—DCIS and IDC. Employing metabolomics, metallomics, and machine learning, could yield accurate screening models with valuable insights into organism responses. Serum samples of confirmed breast cancer patients were utilized to analyze metabolite and metal ion profiles, using two distinct analysis methods, proton NMR for metabolomics and ICP-OES for metallomics. The resulting responses were then subjected to discriminant analysis, progression biomarker exploration, examination of correlations between patients’ metabolites and metal ions, and the impact of age and menopause status. Measured NMR spectra and metabolite relative integrals were used to achieve statistically significant discrimination through MVA between breast cancer and control groups. The analysis identified 24 metabolites and 4 metal ions crucial for discrimination. Furthermore, four metabolites were associated with disease progression. Additionally, there were important correlations and relationships between metabolite relative integrals, metal ion concentrations, and age/menopausal status subgroups. Quantified relative integrals allowed for discrimination between studied subgroups, validated with a holdout set. Feature importance and statistical analysis for metabolomics and metallomics extracted a set of common entities which in combination provides valuable insights into ongoing molecular disturbances and disease progression.

A New Computer-Aided Diagnosis System for Breast Cancer Detection from Thermograms Using Metaheuristic Algorithms and Explainable AI

Advances in the early detection of breast cancer and treatment improvements have significantly increased survival rates. Traditional screening methods, including mammography, MRI, ultrasound, and biopsies, while effective, often come with high costs and risks. Recently, thermal imaging has gained attention due to its minimal risks compared to mammography, although it is not widely adopted as a primary detection tool since it depends on identifying skin temperature changes and lesions. The advent of machine learning (ML) and deep learning (DL) has enhanced the effectiveness of breast cancer detection and diagnosis using this technology. In this study, a novel interpretable computer aided diagnosis (CAD) system for breast cancer detection is proposed, leveraging Explainable Artificial Intelligence (XAI) throughout its various phases. To achieve these goals, we proposed a new multi-objective optimization approach named the Hybrid Particle Swarm Optimization algorithm (HPSO) and Hybrid Spider Monkey Optimization algorithm (HSMO). These algorithms simultaneously combined the continuous and binary representations of PSO and SMO to effectively manage trade-offs between accuracy, feature selection, and hyperparameter tuning. We evaluated several CAD models and investigated the impact of handcrafted methods such as Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), Gabor Filters, and Edge Detection. We further shed light on the effect of feature selection and optimization on feature attribution and model decision-making processes using the SHapley Additive exPlanations (SHAP) framework, with a particular emphasis on cancer classification using the DMR-IR dataset. The results of our experiments demonstrate in all trials that the performance of the model is improved. With HSMO, our models achieved an accuracy of 98.27% and F1-score of 98.15% while selecting only 25.78% of the HOG features. This approach not only boosts the performance of CAD models but also ensures comprehensive interpretability. This method emerges as a promising and transparent tool for early breast cancer diagnosis.