Pulmonary artery aneurysm is the best-defined type of pulmonary disease in Behçet's disease (BD) with an important morbidity and mortality. The objective of this study was to assess the contribution of high-resolution dynamic chest CT imaging for one of the most serious aspects of BD: pulmonary artery aneurysm and other pulmonary parenchymal involvement. Sixteen BD patients were recruited for this study, (14 men, 87.5%, and 2 women, 12.5%). All patients fulfilled the 1990 American College of Rheumatology criteria for classification of BD [International Study Group for Behçet's Disease, Lancet 335:1078-1080, (1990)]. All patients underwent thorough history taking, full clinical examination, and routine laboratory investigations. Plain chest X-rays and pulmonary CT angiography were performed on all patients in an attempt to assess the pulmonary vasculature and lung parenchyma. Pulmonary vascular abnormalities were as follows: pulmonary artery aneurysms of varying sizes in nine patients (56.3%), main pulmonary artery ectasia in two patients (12.5%), pulmonary artery embolism in two patients (12.5%), venacaval thrombosis in seven patients (43.8%), and pulmonary venous varices in four patients (25%). Pulmonary parenchymal abnormalities were as follows: three patients (18.8%) with mild central bronchiectasis, one patient (6.3%) with atelectasis, one patient (6.3%) with subpleural nodule, and four patients (25%) with interstitial lung disease. Eight of the male patients were smokers. Multislice CT is useful in demonstrating the entire spectrum of thoracic manifestations of BD. Multislice CT is noninvasive and provides excellent delineation of the vessel lumen and wall and perivascular tissues, as well as detailed information concerning the lung parenchyma, pleura, and mediastinal structures.

Fine-needle aspiration cytology (FNAC) of breast masses has been replaced by ultrasound-guided core-needle biopsy (USG-CNB) in many countries. However, in Egypt, breast FNAC continues to play the major role in diagnosing breast masses. In this prospective study, we evaluated the efficacy of USG-FNACs performed at a breast cancer screening center by comparing the FNAC results with the corresponding definitive histological examination outcome. We also investigated the role that CNB can play as a complementary diagnostic tool for FNAC in selected cases. A total of 229 consecutive nonpalpable breast masses were included in this study. Each FNAC was placed into one of four categories: 3.5% nondiagnostic, 13.5% benign, 12.3% atypical/suspicious (indeterminate), and 70.7% malignant. The overall diagnostic accuracy was 98.9%, with a specificity and sensitivity of 99.3 and 96.7%, respectively. The overall positive predictive values and negative predictive values were 99.3 and 96.7%, respectively. Only 37 masses (16%) were converted to CNB, with the indeterminate cytology being the most common cause (54%) for this conversion. Two cases demonstrating the superior benefit of FNAC over CNB are illustrated. Although we started the study by reserving CNB as a first choice to assess microcalcifications without architectural distortion, we ended the study by deciding to perform combined FNAC and CNB for this type of lesions. In conclusion, aiming to maximize the preoperative diagnosis of cancer, it would be cost efficient and time saving to use FNAC as a first-line investigation to benefit from the wealth of cytological information yielded, followed by CNB in selected cases.

Breast cancer is the most common cancer in many countries all over the world. Early detection of cancer, in either diagnosis or screening programs, decreases the mortality rates. Computer Aided Detection (CAD) is software that aids radiologists in detecting abnormalities in medical images. In this article we present our approach in detecting abnormalities in mammograms using digital mammography. Each mammogram in our dataset is manually processed-using software specially developed for that purpose-by a radiologist to mark and label different types of abnormalities. Once marked, processing henceforth is applied using computer algorithms. The majority of existing detection techniques relies on image processing (IP) to extract Regions of Interests (ROI) then extract features from those ROIs to be the input of a statistical learning machine (classifier). Detection, in this approach, is basically done at the IP phase; while the ultimate role of classifiers is to reduce the number of False Positives (FP) detected in the IP phase. In contrast, processing algorithms and classifiers, in pixel-based approach, work directly at the pixel level. We demonstrate the performance of some methods belonging to this approach and suggest an assessment metric in terms of the Mann Whitney statistic.