CESM is a recommended investigation in breast cancer to increase the accuracy of size measurement and the detection of multiple tumors. The addition of 3D ultrasound can enhance the detection of intraductal extension. Advances in knowledge: Choice of conservative breast surgery vs mastectomy is still a debate. We used an advanced, contrast-based, application of the mammogram: CESM and a non-invasive 3D breast ultrasound in the assessment of the local extension of the breast cancer regarding size, perifocal stromal infiltration and multiplicity to guide the selection of proper management in proved cases of breast cancer.

Background On mammography many cancers may be missed even in retrospect either due to the breast density, the small size of the tumor or the subtle signs of cancer that are imperceptible. We aimed to compare the sensitivity of artificial intelligence (AI) to that of digital mammography in the detection of different types of breast cancer. Also, the sensitivity of AI in picking up the different breast cancer morphologies namely mass, pathological calcifications, asymmetry, and distortion was assessed. Tissue biopsy and pathology were used as the standard reference. The study included 123 female patients with 134 proved carcinoma. All patients underwent digital mammogram (DM) examination scanned with artificial intelligence algorithm. Results AI achieved higher sensitivity than mammography in detecting malignant breast lesions. The sensitivity of AI was 96.6%, and false negative rate was 3.4%, while mammography sensitivity was 87.3% and false negative rate 12.7%. Our study showed AI performed better than mammography in detecting ductal carcinoma in situ and invasive lobular carcinoma with sensitivity (100% and 96.6%) vs (88.9% and 82.2%) respectively. AI was more sensitive to detect cancers presented with suspicious mass 95.2% vs 75%, suspicious calcifications 100% vs 86.5% and asymmetry and distortion 100% vs 84.6%, than mammography. Conclusions AI showed potential values to overcome mammographic limitations in the detection of breast cancer even those with challenging morphology as invasive lobular carcinoma, ductal carcinoma in situ, tubular carcinoma and micropapillary carcinoma.

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: Juvenile Idiopathic Arthritis (JIA) is defined as arthritis of unknown etiology beginning before the age of 16 years and persisting for at least 6 weeks, while excluding other known conditions. Aim of Study:The purpose of this study is to highlight the beneficial role of MRI & US in the evaluation of knee joint affection in patients with juvenile idiopathic arthritis, especially in early cases. Patients and Methods:The study was carried out on fourty patients (26 females and 14 males), referred to the Radiology Department of Kasr El-Ainy Hospital from Abo El-Rish Pediatric Hospital. Their age ranged from 2.5 years up to 13 years. All patients underwent examination of the more symptomatic knee joint using MRI with intravenous contrast (Gadolinium) and Ultrasound (US) examinations. The results of the ultrasound were compared to those of MRI, with the MRI being the gold standard of diagnosis.Results: Among the studied cases ultrasound was able to detect joint effusion in all cases as a compressible anechoic area. It was able to detect synovitis as synovial thickening and increased vascularity on power Doppler in all cases which is evident mainly in the suprapatellar recess. The accuracy of US regarding both effusion and synovitis was 100%. Ultrasound had great potential to identify the normal cartilage and allows for differentiation of the abnormal morphology such as loss of clarity, irregularity, and defects on the surface. In our study ultrasound was able to demonstrate most of the cases. The overall accuracy regarding cartilage changes was 90%. In this study six of the cases had bone erosions which were detected on MRI. US was able to detect four of these cases. The overall accuracy of US regarding erosions was 95%. Conclusion:Ultrasound has the ability to demonstrate knee joint pathology in early JIA which can help start early treatment or modify already existing one to prevent permanent joint damage. At this point in time, however, it is not possible to determine that ultrasound is superior to MRI, especially regarding bone erosions and the fact that it is operator dependent and needs experience.

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