UNASSIGNED: Non-invasive ultrasound imaging of carotid plaques can provide information on the characteristics of the arterial wall including the size, morphology and texture of the @entity731 . Several studies were carried out that demonstrated the usefulness of these feature sets for differentiating between asymptomatic and symptomatic plaques and their corresponding cerebrovascular risk stratification. The aim of this study was to develop predictive modelling for estimating the time period of a @entity64 event by determining the risk for short term (less or equal to three years) or long term (more than three years) events. Data from 108 @entity1 that had a @entity64 event have been used. The information collected included clinical and ultrasound imaging data. The prediction was performed at base line where @entity1 were still asymptomatic. Several image texture analysis and clinical features were used in order to create a classification model. The different features were statistically analyzed and we conclude that image texture analysis features extracted using Spatial Gray Level Dependencies method had the best statistical significance. Several predictive models were derived based on Binary Logistic Regression (BLR) and @entity4002 ( @entity4002 ) modelling. The best results were obtained with the @entity4002 modelling models with an average correct classifications score of 77  7% for differentiating between @entity64 event occurrences within 3 years versus more than 3 years. Further work is needed in investigating additional multiscale texture analysis features as well as more modelling techniques on more subjects.

Prediction of the time period of XXXX based on ultrasound image analysis of initially asymptomatic carotid plaques.

@entity1 :: ('9606', 'Species') :: ['patients'] @entity731 :: ('MESH:D050197', 'Disease') :: ['atherosclerotic plaques'] @entity4002 :: ('MESH:D011778', 'Disease') :: ['Support Vector Machines', 'SVM'] @entity64 :: ('MESH:D020521', 'Disease') :: ['stroke']
@entity64 :: (MESH:D020521,Disease) :: ['stroke']
