Objective: This study aims to establish risk of breast cancer based on breast density among Saudi women and to compare cancer prediction using different breast density methods. Methods: 1140 pseudonymised screening mammograms from Saudi females were retrospectively collected. Breast density was assessed using Breast Imaging Reporting and Data System (BI-RADS) density categories and visual analogue scale (VAS) of 285 cases and 855 controls matched on age and body mass index. In a subset of 160 cases and 480 controls density was estimated by two automated methods, Volpara Density™ and predicted VAS (pVAS). Odds ratios (ORs) between the highest and second categories in BI-RADS and Volpara density grades, and highest vs lowest quartiles in VAS, pVAS and Volpara Density™, were estimated using conditional logistic regression. Results: For BI-RADS, the OR was 6.69 (95% CI 2.79–16.06) in the highest vs second category and OR = 4.78 (95% CI 3.01–7.58) in the highest vs lowest quartile for VAS. In the subset, VAS was the strongest predictor OR = 7.54 (95% CI 3.86–14.74), followed by pVAS using raw images OR = 5.38 (95% CI 2.68–10.77) and Volpara Density ™ OR = 3.55, (95% CI 1.86–6.75) for highest vs lowest quartiles. The matched concordance index for VAS was 0.70 (95% CI 0.65–0.75) demonstrating better discrimination between cases and controls than all other methods. Conclusion: Increased mammographic density was strongly associated with risk of breast cancer among Saudi women. Radiologists’ visual assessment of breast density is superior to automated methods. However, pVAS and Volpara Density ™ also significantly predicted breast cancer risk based on breast density. Advances in knowledge: Our study established an association between breast density and breast cancer in a Saudi population and compared the performance of automated methods. This provides a stepping-stone towards personalised screening using automated breast density methods.

Background: Breast density is an independent risk factor for breast cancer and affects the sensitivity of mammography screening. Therefore, new breast imaging approaches could benefit women with increased breast density in early cancer detection and diagnosis. Objectives: To assess the diagnostic performance of abbreviated breast MRI compared with mammography and other imaging modalities in screening and diagnosing breast cancer among Saudi women with dense breast tissue. Methods: A retrospective diagnostic study was conducted using anonymized medical images and histopathology information from 55 women, aged ≥30 years, who had dense breasts (Breast Imaging and Reporting Data System [BI-RADS] breast density categories C and D) and an abnormal mammogram. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated for mammography, digital breast tomosynthesis (DBT), synthetic mammography (SM) derived from DBT, ultrasound, and abbreviated breast MRI (ABMRI). Results: A total of 19 women had pathology-proven breast cancer. Among all methods, ABMRI showed the highest sensitivity (94.7%) and specificity (58.3%), while mammography showed the lowest (84.2% and 44.4%, respectively). AUC for ABMRI was higher than all the methods including mammography (0.751 vs. 0.643; P < 0.05). Conclusion: ABMRI appears to be more accurate in cancer diagnosis than mammography and other modalities for women with dense breast tissue. Further research is advised on a larger sample of Saudi women to confirm the benefit of ABMRI in breast cancer screening and diagnosis for women with increased breast density.

Background: Breast density is an important yet often overlooked factor in breast cancer screening. Limited awareness among women can hinder informed decision-making, especially in regions without standardized breast density notification, such as Saudi Arabia. Objective: This study aimed to assess awareness, knowledge, and educational needs related to breast density among women in Saudi Arabia. Methods: A nationwide, cross-sectional survey was conducted using a structured online questionnaire. Saudi women aged 30 years and older were recruited via convenience sampling through social media platforms and targeted mailing lists. The survey collected demographic data and assessed participants’ breast density (BD) awareness, knowledge, perceived risk, and preferred information sources. Data analysis included both descriptive and inferential statistical methods. Results: 1,021 Saudi women (30– 75 years old) participated in this study. Only 11.4% of the sample had heard of breast density, and less than half of those (5%) understood its association with breast cancer risk. Women with higher education and previous mammography experience had significantly higher awareness (OR: 5.00, 95% CI: 2.18– 11.45; p< 0.001 and 1.81, 95% CI: 1.15– 2.86; p=0.010, respectively). A vast majority (80%) expressed interest in being informed about their breast density, and more than half (56.3%) are willing to pay for additional imaging if they had dense breast tissue. Conclusion: Awareness of breast density among women in Saudi Arabia is remarkably low, likely due to the absence of structured education, lack of provider-patient communication, and no national notification policy. Despite this, women express a clear willingness to receive related information. There is a critical need for targeted education on breast density. National health strategies should prioritize breast density awareness and standardized communication in breast cancer screening programs.

Breast density is a recognized risk factor for breast cancer and can affect the sensitivity of mammography. Consequently, magnetic resonance imaging (MRI) is recommended as a screening modality for women with increased breast density. However, mammography remains the primary method for assessing a woman's breast density classification. magnetic resonance elastography (MRE) is a new technique to evaluate tissue stiffness characteristics. This study aims to evaluate the ability of MRE to distinguish between fibroglandular and fatty tissues in normal women with different breast densities, potentially aiding in the classification of breast density using MRI. Forty‐three women aged 40–79 years with normal screening mammograms were included in this prospective study. MRE was performed using a 1.5‐T MRI scanner, and an in‐house passive driver was used to obtain an MRE‐capable gradient echo (GRE) sequence, which was integrated into a noncontrast‐enhanced breast MRI protocol. MRE images were analyzed to measure stiffness values for fibroglandular and fatty tissue based on regions of interest (ROIs) in both breasts. Differences in mean stiffness between tissue types were assessed; a p‐value < 0.05 was considered significant. Fibroglandular tissue exhibited significantly higher stiffness than fatty tissue in both breasts (right breast: 1.55 ± 0.31 kPa vs. 0.82 ± 0.13 kPa, p < 0.001; left breast: 1.46 ± 0.23 kPa vs. 0.81 ± 0.11 kPa, p < 0.001). Comparison between dense and nondense groups on mammograms revealed no significant difference in stiffness for the same tissue types in both breasts. MRE can potentially differentiate between fibroglandular and fatty breast tissues based on shear stiffness, independent of mammographic density. Future research with larger cohorts, including cancer cases, is needed to further establish MRE's role in breast cancer screening.

Background: Breast density is increasingly recognized as a vital risk factor that affects early breast cancer detection. Therefore, this study was aimed at evaluating the agreement between different breast density measurements across multiple imaging modalities and identifying the best breast cancer predictor among these methods. Methods: Data for women over 30 years old who underwent mammography, synthetic mammography of digital breast tomosynthesis (SM‐DBT), ultrasound (US), and magnetic resonance imaging (MRI) were collected. Breast density was assessed by two breast radiologists using the American College of Radiology (ACR) categories and the visual analog scale (VAS) for percentage density (%PD). The agreement was calculated using the kappa coefficient ( k ) and Spearman correlation coefficient ( ρ ). Logistic regression odds ratios (ORs) were used to assess the best predictor of breast cancer based on breast density. Results: Among 77 women (mean age 47.34 years), 25 had breast cancer. Categorical breast density assessments showed the highest agreement between mammography and SM‐DBT ( k = 0.535) and moderate agreement between mammography and MRI ( k = 0.452). VAS analysis revealed moderate positive correlations between mammography and SM‐DBT ( ρ = 0.49), mammography and MRI ( ρ = 0.56), and SM‐DBT and MRI ( ρ = 0.56), p < 0.05. Ultrasound showed the lowest correlation with all breast imaging modalities. Breast cancer risk prediction based on breast density showed significant associations for mammography (OR = 3.09) and MRI (OR = 4.16), p < 0.05. Conclusion: The results suggest notable variability in radiologists′ breast density assessment across different imaging modalities. MRI showed a greater ability to identify dense breast tissue and demonstrated potential value in breast cancer risk prediction, although these findings should be interpreted cautiously given the limited sample size. Establishing standardized approaches to breast density assessment remains important to improve the accuracy of breast cancer screening and risk prediction, and further research with larger cohorts is warranted.

Background/Objectives: This study aimed to evaluate the diagnostic performance of simplified intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) parameters in distinguishing malignant from benign breast lesions, and to explore their association with clinicopathological features. Methods: This retrospective study included 108 women who underwent breast MRI with multi-b-value DWI (0, 20, 200, 500, 800 s/mm²). Of those 108 women, 73 had pathologically confirmed malignant lesions. IVIM maps (ADC_map, D, D*, and perfusion fraction f) were generated using IB-Diffusion™ software version 21.12. Lesions were manually segmented by radiologists, and clinicopathological data including receptor status, Ki-67 index, cancer type, histologic grade, and molecular subtype were extracted from medical records. Nonparametric tests and ROC analysis were used to assess group differences and diagnostic performance. Additionally, a binary logistic regression model combining D, D*, and f was developed to evaluate their joint diagnostic utility, with ROC analysis applied to the model’s predicted probabilities. Results: Malignant lesions demonstrated significantly lower diffusion parameters compared to benign lesions, including ADC_map (p = 0.004), D (p = 0.009), and D* (p = 0.016), indicating restricted diffusion in cancerous tissue. In contrast, the perfusion fraction (f) did not show a significant difference (p = 0.202). ROC analysis revealed moderate diagnostic accuracy for ADC_map (AUC = 0.671), D (AUC = 0.657), and D* (AUC = 0.644), while f showed poor discrimination (AUC = 0.576, p = 0.186). A combined logistic regression model using D, D*, and f significantly improved diagnostic performance, achieving an AUC of 0.725 (p < 0.001), with 67.1% sensitivity and 74.3% specificity. ADC_map achieved the highest sensitivity (100%) but had low specificity (11.4%). Among clinicopathological features, only histologic grade was significantly associated with IVIM metrics, with higher-grade tumors showing lower ADC_map and D* values (p = 0.042 and p = 0.046, respectively). No significant associations were found between IVIM parameters and ER, PR, HER2 status, Ki-67 index, cancer type, or molecular subtype. Conclusions: Simplified IVIM DWI offers moderate accuracy in distinguishing malignant from benign breast lesions, with diffusion-related parameters (ADC_map, D, D*) showing the strongest diagnostic value. Incorporating D, D*, and f into a combined model enhanced diagnostic performance compared to individual IVIM metrics, supporting the potential of multivariate IVIM analysis in breast lesion characterization. Tumor grade was the only clinicopathological feature consistently associated with diffusion metrics, suggesting that IVIM may reflect underlying tumor differentiation but has limited utility for molecular subtype classification.