Background Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has always been a problem solver in troublesome breast lesions. Despite its many advantages, the encountered low specificity results in unnecessary biopsies. Diffusion-weighted MRI (DW-MRI) is a well-established technique that helps in characterizing breast lesions according to their water diffusivity. So this work aimed to assess the diagnostic performance of DW-MRI in troublesome breast lesions and see if it can replace DCE-MRI study. Results In our prospective study, we included 86 patients with mammography and/or ultrasound-detected 90 probably benign or probably malignant (BIRADS 3 or 4) breast lesions. Among the studied cases, 49/90 lesions were benign, and 41/90 were malignant. Combined analysis of morphological and kinetic findings in DCE-MRI had achieved the highest sensitivity of 95.1%. DW-MRI alone was less sensitive (73.2%) yet more specific (83.7%) than DCE-MRI (77.6%). Diagnostic accuracy of DCE-MRI was higher (85.6%) as compared to DW-MRI which was (78.9%). Conclusion DCE-MRI is the cornerstone in the workup of troublesome breast lesions. DW-MRI should not be used as supplementary tool unless contrast administration is contraindicated. Combining both DCE-MRI and DW-MRI is the ultimate technique for better lesion evaluation.

Background In women with diagnosed breast cancer, accurate loco-regional staging and preoperative examination are of utmost importance for optimal patient management decisions. MRI may be warranted for correct preoperative staging as recommended from international guidelines. DWI-MRI can be combined with CE-MRI to assess more functional data. So we aimed to evaluate the performance of CE-MRI and qualitative DWI-MRI in preoperative loco-regional staging of malignant breast lesions as regards the local extension of the disease and axillary lymph node status, beyond standard assessment with mammography and ultrasound. This prospective study included 50 female patients with pathologically proven malignant breast lesions (BIRADS VI) coming for preoperative staging. Full-field digital mammography (FFDM) and ultrasound, CE-MRI, and DWI-MRI findings were compared for all patients, and the findings were evaluated independently. Results were then correlated to postoperative histopathology. Results Fifty women with pathologically proven malignant breast lesions (BIRADS VI) were enrolled in this study; the mean age of this study population was 43.25 years. The 50 patients were divided into 2 groups: 37/50 (74%) underwent upfront surgery and 13/50 (26%) received neoadjuvant therapy before surgery. All patients performed DCE and DWI-MRI breast. Among patients who underwent upfront surgery, DCE-MRI showed the highest correlation with the postoperative pathology size and the overall sensitivity regarding multiplicity. Regarding patients who received neoadjuvant therapy, DCE-MRI was found to have the highest correlation with the postoperative pathology concerning lesion size and multiplicity after completion of the neoadjuvant chemotherapy cycles. Conclusion CE-MRI can accurately map lesion extension and detect multifocality/multicentricity, thus tailor surgical management options (either conservative surgery or mastectomy). Qualitative DWI can be combined with ultrasonography for better evaluation of the axillary nodal status.

Background: Automated three-dimensional (3D) breast ultrasound (US) systems and breast tomosynthesis are promising breast imaging modalities. The study aims to compare the diagnostic indices of the 3D imaging techniques: digital breast tomosynthesis (DBT) and automated breast ultrasound (ABUS) in the characterization of breast masses. This prospective study included 32 women with breast masses either detected by means of clinical examination or with the mammographic exam. All of them have been subjected to tomosynthesis and automatic breast ultrasound examinations. The images from both modalities were then analyzed one at a time by two experienced representative radiologists in consensus. Results were compared to each other and to pathology and follow-up of typically benign findings Results: The masses statistically evaluated in this study were 37 in number, among which 16 were benign and 21 were malignant. The sensitivity and specificity of tomosynthesis in the characterization of breast masses were 100% and 81.25%, respectively, while automated breast ultrasound were 100% and 75%, respectively. Conclusion: Tomosynthesis and automated breast ultrasound are two promising modalities in breast imaging. Their diagnostic indices in this study were very close to one another; therefore, they can be used as an adjunct modality to mammography for early diagnosis of breast cancer.

Objectives: to study the impact of artificial intelligence (AI) on the performance of mammogram with regard to the classification of the detected breast lesions in correlation to ultrasound aided mammograms. Methods: Ethics committee approval was obtained in this prospective analysis. The study included 2000 mammograms. The mammograms were interpreted by the radiologists and breast ultrasound was performed for all cases. The Breast Imaging Reporting and Data System (BI-RADS) score was applied regarding the combined evaluation of the mammogram and the ultrasound modalities. Each breast side-was individually assessed with the aid of AI scanning in the form of targeted heat-map and then, a probability of malignancy (abnormality scoring percentage) was obtained. Operative and the histopathology data were the standard of reference. Results: Normal assigned cases (BI-RADS 1) with no lesions were excluded from the statistical evaluation. The study included 538 benign and 642 malignant breast lesions (n = 1180, 59%). BI-RADS categories for the breast lesions with regard to the combined evaluation of the digital mammogram and ultrasound were assigned BI-RADS 2 (Benign) in 385 lesions with AI median value of the abnormality scoring percentage of 10, (n = 385/1180, 32.6%), and BI-RADS 5 (malignant) in 471, that had showed median percentage AI value of 88 (n = 471/1180, 39.9%). AI abnormality scoring of 59% yielded a sensitivity of 96.8% and specificity of 90.1% in the discrimination of the breast lesions detected on the included mammograms. Conclusions: AI could be considered as an optional primary reliable complementary tool to the digital mammogram for the evaluation of the breast lesions. The color hue and the abnormality scoring percentage presented a credible method for the detection and discrimination of breast cancer of near accuracy to the breast ultrasound. So consequently, AI- mammogram combination could be used as a one setting method to discriminate between cases that require further imaging or biopsy from those that need only time interval follows up. Advances in knowledge: Recently, the indulgence of AI in the work up of breast cancer was concerned. AI noted as a screening strategy for the detection of breast cancer. In the current work, the performance of AI was studied with regard to the diagnosis not just the detection of breast cancer in the mammographic-detected breast lesions. The evaluation was concerned with AI as a possible complementary reading tool to mammogram and included the qualitative assessment of the color hue and the quantitative integration of the abnormality scoring percentage.

Background Contrast-enhanced mammography (CEM) has been discovered to be more sensitive and specific than two-dimensional full-field digital mammography (FFDM) in both screening and diagnostic settings. The aim of the study was to assess the additive role of CEM in the detection and characterization of breast lesions in women with increased risk of developing breast cancer. This prospective study included 283 female patients with increased risk of developing breast cancer (i.e., positive family history of breast cancer, personal history of breast cancer, and heterogeneously dense mammary parenchyma) coming for either screening (n = 127/283 (49.1%)) or diagnostic (n = 156/283 (55.1%)) purpose. All patients had FFDM and CEM done, and the findings were evaluated independently; final Breast Imaging Reporting And Data System (BIRADS) classification was given for each modality. Results were then compared with histopathology or ultrasound findings with routine follow-up for normal and typically benign findings. Results In this study, 283 women with mean age of 48 were enrolled. Among the studied cases regardless to a specific risk factor, 15/283 (5.3%) were diagnosed as normal, 13/283 (4.6%) as inflammatory lesions, 72/283(25.4%) as benign lesions, 6/283 (2.1%) as benign precancerous lesions, and 177/283 (62.5%) as malignant. The overall sensitivity and specificity of the CEM were 92.7 and 71.43 %, respectively, while FFDM were 80.90 and 59.05%, respectively. Conclusion Contrast-enhanced mammography is a valuable screening and diagnostic imaging modality in patients with increased risk of developing breast cancer with diagnostic indices higher than mammography resulting in a significantly higher cancer detection rate.