Carotid plaque spaces relate to symptoms and ultrasound scattering

The Stenting to Internal Carotid Artery Stenosis (ICS) in Petrous Portion. The Evaluation of Plaque Figures in Magnetic Resonance Image

SUMMARY

In the safety stenting, it is important to get to know the characteristics of a plaque. In petrous carotid artery stenosis, it is difficult to know the characteristics of the plaque.We paid our attention to the MPRAGE (Magnetization Prepared Rapid Acquisition with Gradient Echo) method on high resolving power MRI. By the MPRAGE method, low intensity was observed in these lesions of all cases. This result suggested that the plaque in petrous portion was a fibrous plaque. This method is useful to get to know the characteristics of a plaque in petrous portion before endovascular treatment.

A tutorial on the use of ROC analysis for computer-aided diagnostic systems

The application of the receiver operating characteristic (ROC) curve for computer-aided diagnostic systems is reviewed. A statistical framework is presented and different methods of evaluating the classification performance of computer-aided diagnostic systems, and, in particular, systems for ultrasonic tissue characterization, are derived. Most classifiers that are used today are dependent on a separation threshold, which can be chosen freely in many cases. The separation threshold separates the range of output values of the classification system into different target groups, thus conducting the actual classification process. In the first part of this paper, threshold specific performance measures, e.g., sensitivity and specificity, are presented. In the second part, a threshold-independent performance measure, the area under the ROC curve, is reviewed. Only the use of separation threshold-independent performance measures provides classification results that are overall representative for computer-aided diagnostic systems. The following text was motivated by the lack of a complete and definite discussion of the underlying subject in available textbooks, references and publications. Most manuscripts published so far address the theme of performance evaluation using ROC analysis in a manner too general to be practical for everyday use in the development of computer-aided diagnostic systems. Nowadays, the user of computer-aided diagnostic systems typically handles huge amounts of numerical data, not always distributed normally. Many assumptions made in more or less theoretical works on ROC analysis are no longer valid for real-life data. The paper aims at closing the gap between theoretical works and real-life data. The review provides the interested scientist with information needed to conduct ROC analysis and to integrate algorithms performing ROC analysis into classification systems while understanding the basic principles of classification.