392).\n\nThe aneurysm dome size showed a negative linear
correlation with intra-aneurysmal flow velocity and WSS. find more Wide-necked aneurysm geometry was associated with faster intra-aneurysmal flow velocity.”
“Penile precancerous and invasive lesions exhibit a variegated morphology. Although the diagnosis and classification of penile tumors is straightforward in most cases, a few entities are problematic, especially to pathologists from countries in which penile cancer is rarely encountered. The differential diagnosis of squamous hyperplasias from differentiated penile intraepithelial neoplasia or from extremely low-grade invasive neoplasms (eg, pseudohyperplastic and verrucous carcinomas) may be particularly check details difficult. Similarly, given the morphologic features shared by all verruciform tumors (ie, verrucous, warty, papillary, and cuniculatum carcinomas, along with giant condylomas), it is challenging at times to distinguish one from another. At the other end of the spectrum, because of their lack of differentiation, it is sometimes difficult to classify high-grade carcinomas, such as basaloid and sarcomatoid,
which may have etiologic/prognostic implications. Penile mixed tumors, harboring more than 1 histologic subtype and grade, constitute a frequent finding in routine pathology. The recognition of distinctive morphologic patterns and histologic grades in these tumors is important because these features could be related to etiologic factors, such as human papillomavirus infection, or they could influence outcome. Penile tumors with glandular features (eg,
adenosquamous and mucoepidermoid carcinomas), although rare, may be confused with HM781-36B nmr the more common pseudoglandular (adenoid, acantholytic) variant of squamous cell carcinomas, their main mimicker. In this review we provide clues that may help in the differential diagnosis of these lesions. (C) 2012 Elsevier Inc. All rights reserved.”
“Diagnostic accuracy can be improved considerably by combining multiple biomarkers. Although the likelihood ratio provides optimal solution to combination of biomarkers, the method is sensitive to distributional assumptions which are often difficult to justify. Alternatively simple linear combinations can be considered whose empirical solution may encounter intensive computation when the number of biomarkers is relatively large. Moreover, the optimal linear combinations derived under multivariate normality may suffer substantial loss of efficiency if the distributions are apart from normality. In this paper, we propose a new approach that linearly combines the minimum and maximum values of the biomarkers. Such combination only involves searching for a single combination coefficient that maximizes the area under the receiver operating characteristic (ROC) curves and is thus computation-effective.