Texture analysis of ultrasonic images of symptomatic carotid plaques can identify those plaques associated with ipsilateral embolic brain infarction.
OBJECTIVE
The aim of our study was to determine the association between objective, computerised texture analysis of carotid plaque ultrasonic images and embolic CT-brain infarction in patients presenting with hemispheric neurological symptoms.


METHODS
Cross-sectional study in patients with 50%-99% (ECST) carotid stenosis.


METHODS
Carotid plaque ultrasonic images (n=54, 26 with TIAs and 28 with stroke) obtained during carotid ultrasound were normalised and standardised for resolution and subsequently assessed visually for the presence of discrete echogenic or juxtaluminal echolucent components and overall echogenicity (plaque type). Using computer software, 51 histogram/textural features of the plaque outlines were calculated. Factor analysis was subsequently applied to eliminate redundant variables. Small cortical, large cortical and discrete subcortical infarcts on CT-brain scan were considered as being embolic.


RESULTS
Twenty-five cases (46%) had embolic infarcts. On logistic regression, grey-scale median (GSM), a measure of echolucency, spatial grey level dependence matrices (SGLDM) correlation and SGLDM information measure of correlation-1, measures of homogeneity were significant (p<0.05), but not grey level runlength statistics (RUNL) Run Percentage (RP), stenosis severity, type of symptoms or echolucent juxtaluminal components. Using ROC curves methodology, SGLDM information measure of correlation-1 improved the value of GSM in distinguishing embolic from non-embolic CT-brain infarction.


CONCLUSIONS
Computerised texture analysis of ultrasonic images of symptomatic carotid plaques can identify those that are associated with brain infarction, improving the results achieved by GSM alone. This methodology could be applied to prospective natural history studies of symptomatic patients not operated on or randomised trials of patients undergoing carotid angioplasty and stenting in order to identify high-risk subgroups for cerebral infarction.
Kakkos
S K
SK
Department of Vascular Surgery, Imperial College, London, UK.
Stevens
J M
JM
Nicolaides
A N
AN
Kyriacou
E
E
Pattichis
C S
CS
Geroulakos
G
G
Thomas
D
D
D000465:Algorithms; D020520:Brain Infarction / Q000175:diagnosis / Q000000981:diagnostic imaging* / Q000209:etiology; D016893:Carotid Stenosis / Q000150:complications / Q000000981:diagnostic imaging*; D003430:Cross-Sectional Studies; D005163:Factor Analysis, Statistical; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D020766:Intracranial Embolism / Q000150:complications / Q000175:diagnosis / Q000000981:diagnostic imaging* / Q000209:etiology; D016015:Logistic Models; D011237:Predictive Value of Tests; D012372:ROC Curve; D015203:Reproducibility of Results; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D012984:Software; D014057:Tomography, X-Ray Computed; D018616:Ultrasonography, Doppler, Duplex