Development and validation of an in vivo analysis tool to identify changes in carotid plaque tissue types in serial 3-D ultrasound scans

Translational Radiomics: Defining the Strategy Pipeline and Considerations for Application-Part 2: From Clinical Implementation to Enterprise

Enterprise imaging has channeled various technological innovations to the field of clinical radiology, ranging from advanced imaging equipment and postacquisition iterative reconstruction tools to image analysis and computer-aided detection tools. More recently, the advancement in the field of quantitative image analysis coupled with machine learning-based data analytics, classification, and integration has ushered in the era of radiomics, a paradigm shift that holds tremendous potential in clinical decision support as well as drug discovery. However, there are important issues to consider to incorporate radiomics into a clinically applicable system and a commercially viable solution. In this two-part series, we offer insights into the development of the translational pipeline for radiomics from methodology to clinical implementation (Part 1) and from that point to enterprise development (Part 2). In Part 2 of this two-part series, we study the components of the strategy pipeline, from clinical implementation to building enterprise solutions.

Computerized Texture Analysis of Carotid Plaque Ultrasonic Images Can Identify Unstable Plaques Associated With Ipsilateral Neurological Symptoms

We estimated the value of objective, computerized texture analysis of ultrasonic images in distinguishing carotid plaques associated with neurological ipsilateral symptoms (amaurosis fugax [AmF; n = 30], transient ischemic attack [TIA; n = 52], and stroke [n = 55]) from asymptomatic plaques (n = 51). We performed 3 case-control studies (1/symptom with asymptomatic plaques as control). On logistic regression, AmF was independently associated with severity of stenosis, percentage of pixels with gray levels 0 to 10 (PPCS1; measure of echolucency), and spatial gray level dependence matrices (SGLDM) information measure of correlation (IMC-1; texture); TIAs with PPCS1 (echolucency), SGLDM correlation, and skewness (both texture); and stroke with PPCS1, SGLDM correlation, and percentage of pixels with gray levels 11 to 20 (PPCS2; echolucency). The area under the curve of the regression-derived predicted probability for AmF, TIA, and stroke was 0.92, 0.82, and 0.85, respectively (all P < .001). Texture analysis can identify carotid plaques associated with a neurological event, improving the diagnostic value of echolucency measures. Texture analyses could be applied to natural history studies.

Texture Analysis of Ultrasonic Images of Symptomatic Carotid Plaques can Identify Those Plaques Associated with Ipsilateral Embolic Brain Infarction

OBJECTIVES

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.

DESIGN

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

PATIENTS AND 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.

CONCLUSION

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.

Ultrasound imaging

Ultrasound imaging is now in very widespread clinical use. The most important underpinning technologies include transducers, beam forming, pulse compression, tissue harmonic imaging, contrast agents, techniques for measuring blood flow and tissue motion, and three-dimensional imaging. Specialized and emerging technologies include tissue characterization and image segmentation, microscanning and intravascular scanning, elasticity imaging, reflex transmission imaging, computed tomography, Doppler tomography, photoacoustics and thermoacoustics. Phantoms and quality assurance are necessary to maintain imaging performance. Contemporary ultrasonic imaging procedures seem to be safe but studies of bioeffects are continuing. It is concluded that advances in ultrasonic imaging have primarily been pushed by the application of physics and innovations in engineering, rather than being pulled by the identification of specific clinical objectives in need of scientific solutions. Moreover, the opportunities for innovation to continue into the future are both challenging and exciting.