Detection Of Breast Cancer Research Articles

Detection of breast cancer in digital breast tomosynthesis with vision transformers.

Digital Breast Tomosynthesis (DBT) has revolutionized more traditional breast imaging through its three-dimensional (3D) visualization capability that significantly enhances lesion discernibility, reduces tissue overlap, and improves diagnostic precision as compared to conventional two-dimensional (2D) mammography. In this study, we propose an advanced Computer-Aided Detection (CAD) system that harnesses the power of vision transformers to augment DBT's diagnostic efficiency. This scheme uses a neural network to glean attributes from the 2D slices of DBT followed by post-processing that considers features from neighboring slices to categorize the entire 3D scan. By leveraging a transfer learning technique, we trained and validated our CAD framework on a unique dataset consisting of 3,831 DBT scans and subsequently tested it on 685 scans. Of the architectures tested, the Swin Transformer outperformed the ResNet101 and vanilla Vision Transformer. It achieved an impressive AUC score of 0.934 ± 0.026 at a resolution of 384 384. Increasing the image resolution from 224 to 384 not only maintained vital image attributes but also led to a marked improvement in performance (p-value = 0.0003). The Mean Teacher algorithm, a semi-supervised method using both labeled and unlabeled DBT slices, showed no significant improvement over the supervised approach. Comprehensive analyses across different lesion types, sizes, and patient ages revealed consistent performance. The integration of attention mechanisms yielded a visual narrative of the model's decision-making process that highlighted the prioritized regions during assessments. These findings should significantly propel the methodologies employed in DBT image analysis by setting a new benchmark for breast cancer diagnostic precision.

Breast cancer prognosis through the use of multi-modal classifiers: current state of the art and the way forward.

We present a survey of the current state-of-the-art in breast cancer detection and prognosis. We analyze the evolution of Artificial Intelligence-based approaches from using just uni-modal information to multi-modality for detection and how such paradigm shift facilitates the efficacy of detection, consistent with clinical observations. We conclude that interpretable AI-based predictions and ability to handle class imbalance should be considered priority.

HI-Net: A novel histopathologic image segmentation model for metastatic breast cancer via lightweight dataset construction

Since 2020, breast cancer has remained the most prevalent cancer worldwide and the World Health Organisation projects significant increases by 2040, with new cases expected to exceed 3 million annually (a 40% increase) and deaths to surpass 1 million (a 50% increase), highlighting the urgent need for advancements in detection and treatment. Current detection of metastasis is highly dependent on labour-intensive and error-prone pathological examination of large-scale biotissue. Given the high-resolution (100,000 × 100,000 gigapixels) but limited quantity of open-source pathological slide datasets, existing deep learning models face preprocessing challenges. This paper introduces HI-Net, a high-speed panoramic feature-extraction pyramid network for rapid and accurate detection of metastatic breast cancer, balancing panoramic segmentation and local attention. Additionally, a lightweight pathological slide dataset optimised for 512 x 512-pixel resolution, derived from downsampled and reassembled competitive datasets, accelerates training and reduces computational costs. HI-Net demonstrates superior performance on existing medical imaging competition datasets and our lightweight dataset, evidencing its effectiveness across datasets and potential for contributing to the generalisation of intelligent diagnostics.

Feature-based detection of breast cancer using convolutional neural network and feature engineering.

Breast cancer (BC) is a prominent cause of female mortality on a global scale. Recently, there has been growing interest in utilizing blood and tissue-based biomarkers to detect and diagnose BC, as this method offers a non-invasive approach. To improve the classification and prediction of BC using large biomarker datasets, several machine-learning techniques have been proposed. In this paper, we present a multi-stage approach that consists of computing new features and then sorting them into an input image for the ResNet50 neural network. The method involves transforming the original values into normalized values based on their membership in the Gaussian distribution of healthy and BC samples of each feature. To test the effectiveness of our proposed approach, we employed the Coimbra and Wisconsin datasets. The results demonstrate efficient performance improvement, with an accuracy of 100% and 100% using the Coimbra and Wisconsin datasets, respectively. Furthermore, the comparison with existing literature validates the reliability and effectiveness of our methodology, where the normalized value can reduce the misclassified samples of ML techniques because of its generality.

Recognition and comprehension of breast awareness among hospital staff: a questionnaire survey using the explanatory leaflet in Japan

BackgroundThis study aimed to assess the recognition and understanding of breast awareness (BA) among hospital staff, a group considered influential in disseminating information about health. Compared to the traditional approach of breast self-examination (BSE), BA has gained prominence as a concept focused on early detection. The study also explored the effectiveness of an informational leaflet in conveying BA concepts.MethodsWe conducted an online, voluntary, and anonymous questionnaire survey at St. Luke’s International Hospital in Japan, where approximately 1,000 breast cancer surgeries are performed annually. The survey comprised three sections: pre-leaflet questions, the informational leaflet, and post-leaflet questions.ResultsFrom a pool of 500 completed questionnaires, 499 were deemed suitable for the analysis. Notably, 78% of respondents were unfamiliar with “BA” before the survey. However, 89.1% expressed interest in adopting daily practices for early breast cancer detection. Following the leaflet exposure, 98.4% of respondents claimed to have understood BA, either completely or partially. The leaflet aided 93.2% of these individuals in differentiating between BA and the traditional BSE method. These outcomes remained consistent across various demographic segments such as occupation, age, and experience with breast cancer care.ConclusionsThe study underscores a concerning lack of awareness regarding BA among hospital staff within the surveyed institution. This highlights the need to engage medical professionals in promoting BA within the community. The informational leaflet proved effective in enhancing comprehension of BA across diverse groups, indicating its potential as a widely applicable educational tool. The leaflet facilitated the comprehension of BA among respondents across all demographic groups, indicating its potential for widespread utility.

Dielectric Signatures of Late Carcinoma Immune Cells Using MMTV-PyMT Mammary Carcinoma Models.

Peripheral blood mononuclear cells (PBMCs) are specialized immune cells produced from hematopoietic stem cells (HSC). They actively surveil for any signs of infection, foreign invaders, and abnormal or aberrant cells associated with diseases. Numerous inherent interactions between PBMCs and proliferating cancer cells facilitate cellular communication, inducing alterations in the composition of the PBMCs. These subtle alterations can be detected by using dielectrophoresis (DEP). The ultimate objective is to apply this knowledge in a clinical setting to achieve noninvasive early detection of breast cancer while minimizing the occurrence of false positives and negatives commonly associated with standard screening methods like mammography. To realize our long-term goal, we are probing the dielectric properties of the PBMCs from FVB/N MMTV-PyMT+ (late carcinoma, PyMT+ PBMC) and FVB/N (wild-type, WT-PBMC) age-matched mice at 14+ weeks using dielectrophoresis on a microfluidic platform. The central hypothesis of this research is that the changes triggered in the subcellular components, such as the cytoskeleton, lipid bilayer membrane, cytoplasm, focal adhesion proteins, and extracellular matrix (ECM) at the onset of carcinoma, regulate dielectric properties (conductivity, σ; and permittivity, ε), thus affecting the bioelectric signals that aid in the detection of breast cancer. The ANOVA results suggest a significant difference in PyMT+ PBMCs crossover frequencies at 0.01 and 0.05 S/m medium conductivity levels. Post hoc pairwise analysis of WT-PBMCs showed that the crossover frequencies are distinct across the medium conductivity ranges from 0.01 to 0.05 S/m. This study revealed that on average, PyMT+ PBMCs have increased crossover frequency, polarizability, higher membrane capacitance, and a folding factor compared with the age-matched wild-type PBMCs.

Mastalgia and Why It Should Be Evaluated With Imaging in Areas Where Use of Breast Cancer Screening Services are Unsatisfactory.

Mastalgia or breast pain is a very common symptom in women attending breast clinic. The aim of this study was to evaluate whether imaging for mastalgia leads to cancer detection in an area where routine breast cancer screening services are underutilized. This prospective study was performed between 1st March 2021 to 31st January 2023 at a tertiary care academic institution of central India. All patients underwent through clinical examination by a surgeon. Then patients were referred for ultrasound and/or X-ray mammography (MMG) depending on age. Cancer detection rate was calculated. The final cohort consisted of 176 patients with mastalgia and without any abnormality on clinical breast examination. Sixteen patients had mass lesion on radiology and core needle biopsy resulted as infiltrating duct carcinoma in 7 patients and benign phylloides tumor in one patient. Overall case detection rate for cancer was 4%. The breast cancer detection rate in patients presenting with mastalgia was low. However, in the absence of routine mammographic screening in the Indian general population, these would have been missed. Hence, diagnostic assessment for mastalgia is an appropriate strategy in countries where routine screening MMG is lacking.

An Artificial Intelligence-Based Tool for Enhancing Pectoral Muscle Segmentation in Mammograms: Addressing Class Imbalance and Validation Challenges in Automated Breast Cancer Diagnosis.

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

Hybrid ensemble deep learning model for advancing breast cancer detection and classification in clinical applications

Being the most common type of cancer worldwide, and affecting over 2.3 million women, breast cancer poses a significant health threat. Although survival rates have improved around the world due to advances in screening, diagnosis, and treatment, early detection remains crucial for effective management. This study seeks to introduce a novel hybrid model that makes use of image-preprocessing techniques and deep-learning algorithms on mammograms to enhance the detection and classification accuracy of breast cancer lesions. The model was tested on a dataset comprising 20,000 mammograms. First, image-processing techniques, such as Contrast-Limited Adaptive Histogram Equalization, Gaussian Blur, and sharpening methods were used to optimize the images for enhanced feature extraction. In addition, the Ensemble Deep Random Vector-Functional Link Neural Network algorithm, YOLOv5, and MedSAM segmentation models were utilized for robust deep learning-based extraction, classification, and visualization of lesions. Finally, the model was clinically validated on 800 patients. The study found a notable enhancement in both accuracy and processing time for benign and malignant diagnoses using the hybrid model. The model achieves an impressive accuracy of 99.7 % and demonstrates a remarkable processing time of 0.75 s. In clinical applications, the hybrid model exhibits high proficiency, reporting 97.2 % accuracy for benign cases and 98.6 % for malignant scenarios. These results highlight the effectiveness of the hybrid model in improving diagnostic accuracy, offering a promising tool for early breast cancer detection.

An Efficient Gradient Based Modified Cuckoo Search Optimization with Dynamic Levy Flight (MCSO-DLF) Algorithm for Breast Cancer Detection

Purpose of Study: This study evaluates the Gradient-Based Dynamic Levy Flight Cuckoo Search Optimization (MCSO-DLF) for breast cancer detection and compares it to six established optimization algorithms. The goal is to determine if MCSO-DLF offers higher accuracy and computational efficiency for optimizing diagnostic models. Methodology: The study compares MCSO-DLF with PSO, GA, ACO, SA, DE, and ABC using the Breast Cancer Wisconsin dataset. The algorithms are evaluated based on accuracy, convergence speed, and efficiency. MCSO-DLF uses dynamic Levy flight and gradient-based optimization to improve solution quality. Main Findings: MCSO-DLF achieved the highest accuracy (0.9912) and fastest computation time (65.94 seconds), outperforming all other algorithms. This demonstrates its effective balance between exploration and local solution refinement. Implications: MCSO-DLF significantly improves accuracy and speed, offering potential advancements in breast cancer detection systems, leading to better patient outcomes and more efficient healthcare. Novelty of Study: This study introduces MCSO-DLF as a novel hybrid optimization method that combines gradient-based optimization with dynamic Levy flight, outperforming traditional algorithms in complex medical diagnostics.

An Efficient Gradient Based Modified Cuckoo Search Optimization with Dynamic Levy Flight (MCSO-DLF) Algorithm for Breast Cancer Detection

Purpose of Study: This study evaluates the Gradient-Based Dynamic Levy Flight Cuckoo Search Optimization (MCSO-DLF) for breast cancer detection and compares it to six established optimization algorithms. The goal is to determine if MCSO-DLF offers higher accuracy and computational efficiency for optimizing diagnostic models. Methodology: The study compares MCSO-DLF with PSO, GA, ACO, SA, DE, and ABC using the Breast Cancer Wisconsin dataset. The algorithms are evaluated based on accuracy, convergence speed, and efficiency. MCSO-DLF uses dynamic Levy flight and gradient-based optimization to improve solution quality. Main Findings: MCSO-DLF achieved the highest accuracy (0.9912) and fastest computation time (65.94 seconds), outperforming all other algorithms. This demonstrates its effective balance between exploration and local solution refinement. Implications: MCSO-DLF significantly improves accuracy and speed, offering potential advancements in breast cancer detection systems, leading to better patient outcomes and more efficient healthcare. Novelty of Study: This study introduces MCSO-DLF as a novel hybrid optimization method that combines gradient-based optimization with dynamic Levy flight, outperforming traditional algorithms in complex medical diagnostics.

From Biosensors to Robotics: Pioneering Advances in Breast Cancer Management.

Breast cancer stands as the most prevalent form of cancer amongst females, constituting more than one-third of all cancer cases affecting women. It causes aberrant cell development, which can assault or spread to other sections of the body, perhaps leading to the patient's death. Based on research findings, timely detection can diminish the likelihood of mortality and enhance the quality of healthcare provided for the illness. However, current technologies can only identify cancer at an advanced stage. Consequently, there is a substantial demand for rapid and productive approaches to detecting breast cancer. Researchers are actively pursuing precise and timely methods for the diagnosis of breast cancer, aiming to achieve enhanced accuracy and early detection. Biosensor technology can allow for the speedy and accurate diagnosis of cancer-related cells, as well as a more sensitive and specialized technique for generating them. Additionally, numerous treatments for breast cancer are depicted such as herbal therapy, nanomaterial-based drug delivery, miRNA targeting, CRISPR technology, immunotherapy, and precision medicine. Early detection and efficient therapy are necessary to manage such a severe illness properly.

Abstract A119: Disparities in mammography screening adherence throughout phases of the COVID-19 Pandemic: Results from the 2018, 2020, and 2022 Behavioral Risk Factor Surveillance System

Abstract Background Early detection of breast cancer through mammography screening reduces mortality risk. Disruptions during the COVID-19 pandemic in the United States worsened disparities in mammography use, and it is unclear if the impact persisted after the rollout of COVID-19 vaccine in 2021. We aim to describe disparities among minority and underserved women in breast cancer screening before and during the initial phase of the COVID-19 pandemic and assess any changes in screening patterns following the rollout of COVID-19 vaccines in the United States. Methods We used Behavioral Risk Factor Surveillance System (BRFSS) data for years before (2018), during (2020), and after (2022) the COVID-19 vaccine rollout in the United States. We estimated the proportion of women aged 50–74 years who were non-adherent to the United States Preventive Services Taskforce screening guidelines (no mammography in the past two years) by select sociodemographic and behavioral factors. Weighted frequencies, Chi-square tests with Rao-Scott correction, and log-binomial regression models with a Poisson family were used to estimate prevalence ratios (PR) for non-adherence by selected factors to identify sub-populations more likely to be non-adherent. Results A total of 116,756, 102,778, and 110,456 women were included in the analytic samples from 2018, 2020, and 2022, respectively. The prevalence of non-adherence to breast cancer screening guidelines was 21.1%, 20.6%, and 21.6%, in 2018, 2020, and 2022, respectively. Comparing 2018 to 2022, the proportion of non-adherent women significantly increased among those who did not graduate high school (26.7% vs 34.7%), those without health insurance (45.3% vs 61.9%), and women with incomes below $15,000 (28.1% vs 35.1%). Insurance status had the largest impact on screening adherence, with uninsured women more than twice as likely to be non-adherent compared to insured women both before (PR = 2.15, 95% CI: 1.94, 2.38) and after the COVID-19 vaccine rollout (2.24, 95% CI: 2.05, 2.45). Prevalence ratios of nonadherence significantly increased between 2018 and 2020, then returned back to pre-pandemic levels in 2022 among those living in non-metropolitan versus metropolitan areas, never married versus married, self-employed versus employed, and those reporting poor versus excellent health. Hispanics were more likely than non-Hispanics to be non-adherent in 2020 and 2022, but no difference was seen in 2018. Conclusions Persistent disparities in breast cancer screening adherence among eligible women in the United States were observed throughout the phases of the COVID-19 pandemic. Although overall adherence remained stable, certain subgroups, such as those with lower education, no health insurance, Hispanic, rural residence, and lower income, experienced worsening adherence. Health insurance status was the most significant factor associated with non-adherence, highlighting the need for targeted interventions among those who are uninsured. Citation Format: Gregory Chang C Chang, James L. Fisher, Electra D. Paskett. Disparities in mammography screening adherence throughout phases of the COVID-19 Pandemic: Results from the 2018, 2020, and 2022 Behavioral Risk Factor Surveillance System [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A119.

WHO-recommended levels of physical activity in relation to mammographic breast density, mammographic tumor appearance, and mode of detection of breast cancer

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

Digital Breast Tomosynthesis Screening Improves Early Breast Cancer Detection and Survival in Taiwan

Digital Breast Tomosynthesis Screening Improves Early Breast Cancer Detection and Survival in Taiwan

Correction to: Deep learning approaches to detect breast cancer: a comprehensive review

Correction to: Deep learning approaches to detect breast cancer: a comprehensive review

Flamingo Search Sailfish Optimizer Based SqueezeNet for Detection of Breast Cancer Using MRI Images

Breast cancer with increased risk in women is identified with Breast Magnetic Resonance Imaging (Breast MRI) and this helps in evaluating treatment therapies. Breast MRI is time time-consuming process that involves the assessment of current imaging. This research work depends on the detection of breast cancer at the earlier stages. Among various cancers, breast cancer in women occurs in larger accounts for almost 30% of estimated cancer cases. In this research, many steps are followed for breast cancer detection like pre-processing, segmentation, augmentation, extraction of features, and cancer detection. Here, the median filter is utilized for pre-processing, as well as segmentation is followed after pre-processing, which is done by Psi-Net. Moreover, the process of augmentation like shearing, translation, and cropping are followed after segmentation. Also, the segmented image tends to process feature extraction, where features like shape features, Completed Local Binary Pattern (CLBP), Pyramid Histogram of Oriented Gradients (PHOG), and statistical features are extracted. Finally, breast cancer is detected using the DL model, SqueezeNet. Here, the newly devised Flamingo Search SailFish Optimizer (FSSFO) is used in training Psi-Net as well as SqueezeNet. Furthermore, FSSFO is the combination of both the Flamingo Search Algorithm (FSA) and SailFish Optimizer (SFO).

Detection of invasive ductal carcinoma by electrical impedance spectroscopy implementing gaussian relaxation-time distribution (EIS-GRTD)

Breast cancer detection and differentiation of breast tissues are critical for accurate diagnosis and treatment planning. This study addresses the challenge of distinguishing between invasive ductal carcinoma (IDC), normal glandular breast tissues (nGBT), and adipose tissue using electrical impedance spectroscopy combined with Gaussian relaxation-time distribution (EIS-GRTD). The primary objective is to investigate the relaxation-time characteristics of these tissues and their potential to differentiate between normal and abnormal breast tissues. We applied a single-point EIS-GRTD measurement to ten mastectomy specimens across a frequency range f = 4 Hz to 5 MHz. The method calculates the differential ratio of the relaxation-time distribution function Δγ between IDC and nGBT, which is denoted by ΔγIDC−nGBT, and Δγ between IDC and adipose tissues, which is denoted by ΔγIDC−adipose. As a result, the differential ratio of Δγ between IDC and nGBT ΔγIDC−nGBT is 0.36, and between IDC and adipose ΔγIDC−adipose is 0.27, which included in the α -dispersion at τpeak1=0.033±0.001s. In all specimens, the relaxation-time distribution function γ of IDC γIDC is higher, and there is no intersection with γ of nGBT γnGBT and adipose γadipose. The difference in γ suggests potential variations in relaxation properties at the molecular or structural level within each breast tissue that contribute to the overall relaxation response. The average mean percentage error δ for IDC, nGBT, and adipose tissues are 5.90%, 6.33%, and 8.07%, respectively, demonstrating the model’s accuracy and reliability. This study provides novel insights into the use of relaxation-time characteristic for differentiating breast tissue types, offering potential advancements in diagnosis methods. Future research will focus on correlating EIS-GRTD finding with pathological results from the same test sites to further validate the method’s efficacy.

Parametric optimization and comparative study of machine learning and deep learning algorithms for breast cancer diagnosis.

Breast Cancer is the leading form of cancer found in women and a major cause of increased mortality rates among them. However, manual diagnosis of the disease is time-consuming and often limited by the availability of screening systems. Thus, there is a pressing need for an automatic diagnosis system that can quickly detect cancer in its early stages. Data mining and machine learning techniques have emerged as valuable tools in developing such a system. In this study we investigated the performance of several machine learning models on the Wisconsin Breast Cancer (original) dataset with a particular emphasis on finding which models perform the best for breast cancer diagnosis. The study also explores the contrast between the proposed ANN methodology and conventional machine learning techniques. The comparison between the methods employed in the current study and those utilized in earlier research on the Wisconsin Breast Cancer dataset is also compared. The findings of this study are in line with those of previous studies which also highlighted the efficacy of SVM, Decision Tree, CART, ANN, and ELM ANN for breast cancer detection. Several classifiers achieved high accuracy, precision and F1 scores for benign and malignant tumours, respectively. It is also found that models with hyperparameter adjustment performed better than those without and boosting methods like as XGBoost, Adaboost, and Gradient Boost consistently performed well across benign and malignant tumours. The study emphasizes the significance of hyperparameter tuning and the efficacy of boosting algorithms in addressing the complexity and nonlinearity of data. Using the Wisconsin Breast Cancer (original) dataset, a detailed summary of the current status of research on breast cancer diagnosis is provided.

Evaluating Real World Health System Resource Utilization and Costs for a Risk-Based Breast Cancer Screening Approach in the Canadian PERSPECTIVE Integration and Implementation Project.

A prospective cohort study was undertaken within the PERSPECTIVE I&I project to evaluate healthcare resource utilization and costs associated with breast cancer risk assessment and screening and overall costs stratified by risk level, in Ontario, Canada. From July 2019 to December 2022, 1997 females aged 50 to 70 years consented to risk assessment and received their breast cancer risk level and personalized screening action plan in Ontario. The mean costs for risk-stratified screening-related activities included risk assessment, screening and diagnostic costs. The GETCOST macro from the Institute of Clinical Evaluative Sciences (ICES) assessed the mean overall healthcare system costs. For the 1997 participants, 83.3%, 14.4% and 2.3% were estimated to be average, higher than average, and high risk, respectively (median age (IQR): 60 [56-64] years). Stratification into the three risk levels was determined using the validated multifactorial CanRisk prediction tool that includes family history information, a polygenic risk score (PRS), breast density and established lifestyle/hormonal risk factors. The mean number of genetic counseling visits, mammograms and MRIs per individual increased with risk level. High-risk participants incurred the highest overall mean risk-stratified screening-related costs in 2022 CAD (±SD) at CAD 905 (±269) followed by CAD 580 (±192) and CAD 521 (±163) for higher-than-average and average-risk participants, respectively. Among the breast screening-related costs, the greatest cost burden across all risk groups was the risk assessment cost, followed by total diagnostic and screening costs. The mean overall healthcare cost per participant (±SD) was the highest for the average risk participants with CAD 6311 (±19,641), followed by higher than average risk with CAD 5391 (±8325) and high risk with CAD 5169 (±7676). Although high-risk participants incurred the highest risk-stratified screening-related costs, their costs for overall healthcare utilization costs were similar to other risk levels. Our study underscored the importance of integrating risk stratification as part of the screening pathway to support breast cancer detection at an earlier and more treatable stage, thereby reducing costs and the overall burden on the healthcare system.