Annals of Vascular Surgery
Volume 20, Issue 2 , Pages 167-174, March 2006

Noninvasive Identification of the Unstable Carotid Plaque

  • Brajesh K. Lal, MD

      Affiliations

    • Division of Vascular Surgery, Department of Surgery, University of Medicine and Dentistry of New Jersey-New Jersey Medical School, Newark, NJ, USA
    • Department of Pharmacology and Physiology, University of Medicine and Dentistry of New Jersey-New Jersey Medical School, Newark, NJ, USA
    • Corresponding Author InformationCorrespondence to: Brajesh K. Lal, MD, Department of Surgery, University of Medicine and Dentistry of New Jersey-New Jersey Medical School, 185 South Orange Avenue, MSB-H570, Newark, NJ 07101-7190, USA
    • ,
  • Robert W. Hobson II, MD

      Affiliations

    • Division of Vascular Surgery, Department of Surgery, University of Medicine and Dentistry of New Jersey-New Jersey Medical School, Newark, NJ, USA
    • Department of Pharmacology and Physiology, University of Medicine and Dentistry of New Jersey-New Jersey Medical School, Newark, NJ, USA
    • ,
  • Meera Hameed, MD

      Affiliations

    • Department of Pathology, University of Medicine and Dentistry of New Jersey-New Jersey Medical School, Newark, NJ, USA
    • ,
  • Peter J. Pappas, MD

      Affiliations

    • Division of Vascular Surgery, Department of Surgery, University of Medicine and Dentistry of New Jersey-New Jersey Medical School, Newark, NJ, USA
    • Department of Pharmacology and Physiology, University of Medicine and Dentistry of New Jersey-New Jersey Medical School, Newark, NJ, USA
    • ,
  • Frank T. Padberg Jr., MD

      Affiliations

    • Division of Vascular Surgery, Department of Surgery, University of Medicine and Dentistry of New Jersey-New Jersey Medical School, Newark, NJ, USA
    • ,
  • Zafar Jamil, MD

      Affiliations

    • Division of Vascular Surgery, Department of Surgery, University of Medicine and Dentistry of New Jersey-New Jersey Medical School, Newark, NJ, USA
    • ,
  • Walter N. Durán, PhD

      Affiliations

    • Division of Vascular Surgery, Department of Surgery, University of Medicine and Dentistry of New Jersey-New Jersey Medical School, Newark, NJ, USA
    • Department of Pharmacology and Physiology, University of Medicine and Dentistry of New Jersey-New Jersey Medical School, Newark, NJ, USA

Article Outline

Intraplaque hemorrhage, enlarging lipid cores, and their proximity to the flow lumen are important determinants of carotid plaque rupture and neurological complications. We developed an image-analysis method for B-mode ultrasound, pixel distribution analysis (PDA), for pre-procedural identification of these high-risk features in carotid plaques. This technique may improve selection of patients for carotid endarterectomy and carotid artery stenting. Forty-two patients with high-grade carotid stenosis in 45 arteries, 18 symptomatic and 27 asymptomatic, underwent preoperative ultrasound. Intraplaque hemorrhage, lipid, fibromuscular tissue, calcium, lipid core area, and distance from the flow lumen were quantified using pixel intensities of tissues in control subjects. These findings were contrasted between symptomatic and asymptomatic plaques and correlated with histology. Inter- and intraobserver variabilities were determined for this technique. Pixel intensities of control tissues were discrete and significantly different from each other (median: blood 0, lipid 27, muscle 45.5, fibrous tissue 204, and calcium 245). There was more intraplaque hemorrhage (p < 0.001) and lipid (p = 0.002) but less calcium (p < 0.001) within symptomatic plaques. Lipid cores were larger (p = 0.005) and their distance from the flow lumen was lower (p = 0.01) in symptomatic plaques. Intraplaque hemorrhage, lipid, fibromuscular tissue, calcium, lipid core size, and distance from flow lumen measured by PDA correlated with histology. No significant inter- or intraobserver variabilities were observed in these measurements. PDA accurately identified more intraplaque hemorrhage and lipid, less calcium, and larger lipid cores located closer to the flow lumen in symptomatic patients with carotid stenosis. These data indicate that PDA may be used to identify high-risk carotid atherosclerotic plaques and thereby improve the selection of patients requiring treatment.

 

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INTRODUCTION 

Carotid atherosclerotic plaque histology has identified several features associated with atheroembolic neurological events. Plaques with large lipid cores1 located close to the flow lumen,2 fibrous cap disruption,3 surface ulceration4 and intraplaque hemorrhage5 have been classified as type VI (complex) plaques.6 These histological features have been associated with atheroembolic events in patients with carotid stenosis (CS). Some of these features may also predispose the plaque to atheroembolization during carotid artery stenting (CAS).7, 8 Specifically, large juxtaluminal lipid/necrotic cores may be vulnerable to endovascular catheter, guidewire, or balloon manipulation.7 Currently, the degree of CS is the only universally accepted clinical marker used to assign risk for atheroembolic neurological complications. Characterization of additional histological markers by noninvasive imaging may improve the identification of individuals at high risk for neurological complications and may facilitate improved selection of candidates for carotid endarterectomy (CEA) or CAS.9

A Duplex ultrasound (US) examination is an integral part of the evaluation of the extracranial carotid circulation. Doppler-derived velocity measurements are routinely used to estimate the degree of CS.10 However, duplex US also involves B-mode imaging that generates high-resolution images of carotid plaques. These B-mode images have been used to predict plaque histology using subjective visual comparisons.11, 12, 13, 14, 15 However, in the absence of a uniform and objective method of acquiring, analyzing, and interpreting the images, the results have been inconsistent. Different tissues reflect US differentially, resulting in B-mode images with variable brightness or pixel intensity.16, 17, 18, 19 Using this principle, we have developed a digital image-analysis algorithm (pixel distribution analysis, PDA) that combines standardized US image acquisition, image normalization, and pixel segmentation with tissue mapping for the noninvasive identification of carotid plaque constituents. In a recent study, we reported preliminary results on the accuracy of PDA by comparing it with histological analysis of carotid plaques.20 The purpose of the current investigation was to determine tissue components (intraplaque hemorrhage, lipid, fibromuscular tissue, and calcium) and architectural features (lipid/necrotic core size and location) of carotid plaques using PDA in patients prior to CEA. These observations were contrasted between symptomatic and asymptomatic patients to identify noninvasive high-risk tissue signatures with potential predictive value for acute neurological events. Finally, reproducibility of this technique was assessed by calculating inter- and intraobserver variabilities.

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METHODS 

Patients 

After institutional review board approval, informed consent was obtained from all participants. Twenty healthy subjects underwent US scanning to determine pixel intensity of individual tissues. Forty-two patients with 45 CSs were determined to require CEA based on North American Symptomatic Carotid Endarterectomy Trial (NASCET, ≥50% stenosis, symptomatic)21, 22 or Asymptomatic Carotid Atherosclerosis Study (ACAS, ≥60% stenosis, asymptomatic)23 recommendations. The degree of stenosis was determined based on peak systolic and end-diastolic velocities10 obtained at our Intersocietal Commission on Accreditation of Vascular Laboratories-approved vascular laboratory. These patients were entered into the study and underwent preoperative US with subsequent PDA. Patients did not undergo preprocedural angiography. Carotid plaques were explanted intact during CEA and subjected to histological analysis.

Standardized High-Resolution B-mode US 

All scans were performed by a registered vascular technologist according to a standardized protocol,20 with a Sequoia 512 US machine (Acuson, Mountain View, CA) using a 7–13 MHz linear array transducer. Briefly, to avoid variability in image acquisition between subjects, we set the maximum dynamic range at 60 dB, preprocessing at 0, persistence at 2, postprocessing at 0, and gain at −5 dB; depth gain compensation was linear. The probe was positioned directly over the area of interest with the angle of insonation at 90°. This standardization allowed collection of maximum data with least image modification by the scanner or operator. Vascular technologists were blinded to whether the patients were symptomatic or asymptomatic.

Image Normalization 

Images were recorded on super-VHS tape and digitized to a Gateway computer (P4 1.4 GHz 1,000 RAM; Gateway, San Diego, CA) with an analog-digital image converter, Dazzle DVC (Dazzle Multimedia, Fremont, CA). The digital images were processed in image-editing software, Adobe Photoshop 6.0 (Adobe, San Jose, CA). The pixel intensities of two constant reference points in each image adjacent to the plaque were identified (luminal blood and arterial adventitia). Each image was linearly scaled by setting the gray value of blood to 0 and that of adventitia to 190. This normalization process allowed valid comparisons to be made between patients.19, 20

Gray-Scale Range of Tissues in Control Subjects 

Twenty age-matched subjects (10 men, 10 women) who had no CS underwent duplex US scanning. B-mode images of subcutaneous fat (abdomen), muscle (biceps), fibrous tissue (iliotibial tract), and calcified structures (tibial head) were obtained using the standardized US protocol described above. Care was taken to image a blood vessel in the same frame to allow image normalization. Each image was recorded, digitized, and normalized. The tissue of interest was outlined, and the median and range of gray-scale pixel intensity representing blood, lipid, fibrous tissue, muscle, and calcium were obtained. Three images were obtained for each tissue from each patient.

PDA of Carotid Plaques 

Once a decision to perform CEA was made, patients were entered into the study and B-mode images were acquired using the same protocol utilized for control subjects. The carotid bifurcation was identified and the plaque was magnified to visualize the entire lesion and both the far and near walls of the artery in the same frame. Several images of the plaque were obtained and recorded live. After digitization and normalization, plaque images were analyzed in Image Pro-3.1 (Media Cybernetics, Silver Spring, MD). The best longitudinal view of the bifurcation was then selected and the plaque manually outlined. The gray-scale ranges of pixels defining blood, lipid, fibrous tissue, muscle, and calcium were identified through an image-segmentation algorithm and mapped within the plaque image. The area occupied by each gray-scale range was measured and the composition of blood, lipid, fibromuscular tissue, and calcium within the plaque was reported as a percent of the total area of the plaque. Coalescent regions of lipid representing the lipid/necrotic core were manually outlined, and the total area occupied was measured in millimeters squared. The least distance between the lipid core and the carotid blood flow lumen was also measured in millimeters. Image analysis was performed with the observer blinded to whether the patient was symptomatic or asymptomatic.

Inter- and Intraobserver Variabilities 

Variability was measured for the methods of determination of control tissue pixel intensity (image selection and digital processing/analysis of blood, lipid, muscle, fibrous tissue, and calcium) as well as for carotid plaque PDA (image selection and digital processing/analysis of intraplaque hemorrhage). Interobserver variability was determined between two observers blinded to each other's results, while intraobserver variability was measured for two sets of measurements performed by one observer at least 2 months apart.

Histology of Carotid Plaques 

Each carotid plaque was excised intact and sectioned at 3 mm intervals. The sections were examined by our collaborating pathologist, who was blinded to the patient's clinical history and US results. The specimens were processed, sectioned, and stained with hematoxylin and eosin and Go-mori trichrome. Digital images of each slide were used to identify, outline, and measure the area occupied by each tissue component (intraplaque hemorrhage, lipid/necrosis, fibromuscular tissue, and calcium) within the plaque as previously described.20 The amount of each tissue component was calculated (surface area in each section × slice thickness 3 mm) as a percentage of the total plaque. The lipid core was separately identified and measured, as was the least distance of the lipid/necrotic core from the flow lumen. This information was used to establish a correlation with PDA.

Statistics 

Control tissue pixel ranges were determined as median and interquartile ranges and compared using the Wilcoxon signed-rank test. Percent composition of individual tissues within plaques was expressed as mean ± standard error of the mean (SEM). Comparison of PDA measurements between symptomatic and asymptomatic plaques was performed using a two-tailed t-test. Inter- and intraobserver variabilities were determined according to the Bland-Altman statistics. Correlation between tissue composition determined by PDA and by histology was performed using the Spearman coefficient.

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RESULTS 

Patient Population 

There were 45 carotid plaques from 42 patients included in the study (34 males and 8 females). The mean age was 68 years (range 51–91). The degree of stenosis (based on preoperative US) ranged 70–99%. Eighteen plaques were obtained from symptomatic patients (stroke n = 6, transient ischemic attack n = 9, and amaurosis fugax n = 3), and 27 were from asymptomatic patients. The mean interval between symptoms and CEA in symptomatic patients was 12 weeks. The control group comprised 10 male and 10 female volunteers with a mean age of 62 years (range 49–71).

Gray-Scale Pixel Range of Tissues in Control Subjects 

Three hundred images of normal tissues were analyzed from 20 healthy volunteers. The median [95% confidence interval (CI) lower, upper] gray-scale pixel intensity for each tissue was as follows: blood 0 (−0.2–1.1), lipid 27 (16.1–31.7), muscle 45.5 (42.2–58.6), fibrous tissue 204 (197.4–207.7), and calcium 245 (242.5–250.6). Figure 1 displays the median, interquartile range, and range of values for each tissue. There was no overlap between tissue pixel intensities. Pixel values for individual tissues were significantly different from each other (vs. blood: p = 0.002 for lipid, p = 0.002 for muscle, p = 0.0005 for fibrous tissue, and p = 0.0005 for calcium; Wilcoxon's signed rank test).

  • View full-size image.
  • Fig. 1. 

    Gray-scale pixel values of individual tissues as measured in control subjects. The box-and-whisker plot shows an interquartile range of 25–75% (box), median, and range (whiskers) of gray-scale values for blood, fat, muscle, fibrous tissue, and calcium.

PDA Differences in Symptomatic vs. Asymptomatic Carotid Plaque Tissue Composition 

In the 18 symptomatic plaques, PDA calculated the percent distribution (mean ± SEM) of tissue components to be as follows: blood, 10.85 ± 1.63; lipid, 29.43 ± 3.30; fibromuscular tissue, 39.83 ± 2.46; and calcium, 2.73 ± 0.46. In the 27 asymptomatic plaques, the tissue composition was as follows: blood, 2.30 ± 0.78; lipid, 18.68 ± 2.40; fibromuscular tissue, 43.50 ± 3.39; and calcium, 11.06 ± 0.74. There was a significantly higher amount of intraplaque hemorrhage and lipid within symptomatic plaques vs. asymptomatic ones (p ≤ 0.001 and p = 0.002, respectively). Conversely, there was a larger amount of calcium within asymptomatic plaques (p ≤ 0.001). There was no significant difference in the amount of fibromuscular tissue between the two groups (p = 0.35) (Fig. 2, Fig. 3).

  • View full-size image.
  • Fig. 2. 

    PDA for tissue composition demonstrates that symptomatic plaques had higher quantities of intraplaque hemorrhage (p < 0.001) and lipid (p = 0.002), while asymptomatic plaques had larger amounts of calcification (p < 0.001). There was no difference in fibromuscular tissue content between the two groups (p = 0.35).

  • View full-size image.
  • Fig. 3. 

    Appearance of representative atherosclerotic carotid bifurcation plaques on B-mode US images after digital PDA processing. L, internal carotid artery blood flow lumen; red, intraplaque hemorrhage; yellow, lipid; green, fibromuscular tissue; and blue, calcification. (A) Symptomatic patients, (B) asymptomatic patients.

PDA Differences in Symptomatic vs. Asymptomatic Carotid Plaque Lipid Core Size and Location 

In the 18 symptomatic plaques, according to PDA, the (mean ± SEM) area occupied by the lipid core was 16.6 ± 2.0 mm2 and the (mean ± SEM) distance of the core from the flow lumen was 0.5 ± 0.1 mm. However, in the 27 asymptomatic plaques, the area occupied by a lipid/necrotic core was 10.7 ± 2.1 mm2 (p = 0.005 vs. symptomatic) and the distance was 1.8 ± 0.4 mm (p = 0.01 vs. symptomatic) (Fig. 3, Fig. 4).

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  • Fig. 4. 

    PDA for tissue architecture demonstrating that (A) lipid cores in symptomatic plaques occupied larger areas than those in asymptomatic plaques (p = 0.005) and (B) lipid cores in symptomatic plaques were much closer to the flow lumen than those in asymptomatic plaques (p = 0.01).

Inter- and Intraobserver Variabilities 

No significant interobserver variability was noted in the measurements of control tissue pixel intensity for blood, lipid, muscle, fibrous tissue, or calcium (Fig. 5A). Similarly, no significant inter- or intraobserver variability was noted in measurements for intraplaque hemorrhage as determined by carotid plaque PDA (Fig. 5B and C).

  • View full-size image.
  • Fig. 5. 

    Bland-Altman statistics demonstrating no significant inter- or intraobserver variability in the determination of pixel intensity of individual tissues as well as in PDA. The horizontal lines are drawn at the mean difference and at the mean difference ± 2 standard deviations. (A) Differences between pixel intensities of individual tissues measured by two different observers from the same images plotted against their respective mean differences. (B) Differences in measured intraplaque hemorrhage by two different observers in the same carotid plaque images plotted against their respective mean differences. (C) PDA predictions for blood content were repeated by one observer in the same carotid plaque images and plotted against their respective mean differences.

Correlation between PDA and Histology 

Forty-five carotid plaques were explanted intact during CEA and sequentially cut at 3 mm intervals to obtain four to eight rings per plaque. Histological sections from each plaque ring were analyzed for a total of 315 carotid plaque sections. Percent volume occupied by intraplaque hemorrhage, lipid/necrosis, fibromuscular tissue, and calcium with respect to total plaque volume was calculated. Using the Spearman coefficient of correlation, PDA assessments of percent composition of individual tissues correlated significantly with histological estimates of each tissue (blood, p = 0.0001, r = 0.6; lipid, p = 0.0001, r = 0.82; fibromuscular tissue, p = 0.007, r = 0.49; and calcium, p ≤ 0.0001, r = 0.82). PDA predictions of lipid/necrotic core size (p = 0.031, r = 0.58) and the least distance of the core from the flow lumen (p = 0.021, r = 0.60) also correlated with histological measurements made on the explanted carotid plaques.

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DISCUSSION 

Randomized controlled trials demonstrating the efficacy of CEA at preventing stroke have utilized the degree of internal carotid artery stenosis as the only criterion for selecting patients at high risk for stroke.21, 22, 23 However, these trials also noted that the majority of patients with high-grade stenoses remained stroke-free even when receiving medical therapy alone.24 It has therefore been proposed that factors in addition to the degree of stenosis may be responsible for determining stroke risk.9 Recent studies have improved our understanding of the pathogenesis of atherosclerotic plaques and their progression from fatty streaks (type I)25 through organized plaques (type IV)25 to complicated plaques (type VI).6 Histological analyses of explanted carotid plaques have demonstrated a higher incidence of fibrous cap thinning and lipid core rupture in symptomatic patients.3 Bassiouny et al.2 reported that proximity of the lipid core to the arterial flow lumen predisposed to neurological complications. Others have noted a correlation between intraplaque hemorrhage and neurological symptoms.4, 5, 26, 27 This indicates that compositional and regional architectural changes within the plaque in the form of hemorrhage, lipid core expansion, and/or fibrous cap thinning may predispose to rupture and atheroembolic neurological complications. Therefore, there is a need to identify these changes noninvasively to afford improved patient selection for treatment with CEA or CAS. The ideal noninvasive imaging modality to identify these changes would have to assess compositional and architectural variations in the plaque in an objective manner and be readily available, inexpensive, and associated with low observer variability. Our results demonstrate that PDA satisfies these requirements.

We have previously reported that PDA of B-mode US images provides an accurate means to discriminate the different tissue components that make up a plaque.20 As a logical extension of this work, we demonstrate in this study that PDA can also accurately discriminate plaque architecture (lipid core size and distance from flow lumen). Furthermore, we provide evidence that PDA can differentiate between symptomatic and asymptomatic plaques. Our findings indicate that an optimized combination of high-resolution B-mode imaging and computer-assisted digital image analysis, as applied in PDA, provides discrete, identifiable, and characteristic pixel distribution (gray scale) ranges for human blood, lipid, fibromuscular tissue, and calcium. Thus, we propose that PDA may be a significant adjuvant method to identify noninvasive high-risk tissue signatures potentially predictive of acute neurological events in patients with CS.

Reilly et al.13 noted that echo patterns in B-mode images of carotid plaques could be related to their tissue composition. They qualitatively defined plaque echogenicity as the degree of acoustic brightness seen on B-mode US. Goes et al.11 subsequently proposed that brightness (echogenicity) of plaques increased when fibrous tissue or calcium content increased. Gray-Weale et al.12 reported that most plaques with predominantly hypoechoic components were associated with neurological symptoms, whereas predominantly hyperechoic plaques were asymptomatic. However, no histo-logical comparisons were provided to corroborate this proposal. Since the classification was based on subjective visual impressions, variability in image acquisition and an inability to objectively quantify echogenicity prevented accurate, clinically relevant comparisons.28

Digital image processing allows the objective measurement of pixel intensity (brightness) of US images using a gray-scale range from 0 (black) to 255 (white). Using this technique, el Barghouty et al.18 quantified the gray-scale intensity of the entire plaque (gray-scale median, GSM). Several investigators subsequently reported that low GSM values may be associated with a higher incidence of neurological symptoms.16, 17, 19, 29, 30 However, GSM measures the median brightness of the entire plaque; regional instability, such as hemorrhage, may exist within a plaque even with a high GSM value. This observation explains why these authors have reported widely varying GSM values that characterize symptomatic and asymptomatic plaques.

Our method relies on the ability to obtain high-resolution images of carotid plaques consistently by using a protocol of standardized B-mode US image acquisition and digital normalization. We find that the process of image normalization using the pixel intensity of adventitia and blood adjacent to the carotid plaque allows for objective and accurate comparisons between patients. Because the gray-scale pixel ranges of human blood, lipid, fibro-muscular tissue, and calcium obtained by PDA are discrete and quantifiable (Fig. 1); the subsequent pixel segmentation algorithm allows the mapping of individual tissue constituents in the carotid plaque image.

Using PDA of B-mode US images, we noted significant differences in tissue composition and architecture between symptomatic and asymptomatic plaques (Fig. 2, Fig. 3, Fig. 4). The 18 symptomatic plaques demonstrated larger quantities of intraplaque hemorrhage (p ≤ 0.001) and lipid (p = 0.002) and larger lipid cores (p = 0.005) located closer to the flow lumen (p = 0.01) but had smaller amounts of calcium (p ≤ 0.001) compared to the 27 asymptomatic plaques. At least one histological study has noted that asymptomatic plaques contained significantly higher amounts of fibrocollagenous material;1 however, this has not been corroborated by others. There was no significant difference in the amount of fibromuscular tissue between the two groups (p = 0.35) in this study. Therefore, based on B-mode US and PDA, intraplaque hemorrhage, large superficially located lipid cores, and low intraplaque calcification are potential markers for high risk of stroke in carotid atherosclerotic plaques (Fig. 3).

Our preoperative assessment of tissue composition using PDA correlated very well with postoperative histological analysis of the same carotid plaques. Additionally, measurements of lipid core size and location with respect to the blood flow lumen also correlated with corresponding measurements made on the histological specimens. Prior US-based classifications were limited by a lack of confirmatory histological correlative data. Our study provides evidence that PDA accurately determines specific compositional (intraplaque hemorrhage, lipid, fibromuscular tissue, and calcium) and architectural (lipid core size and least distance from the flow lumen) features of carotid atherosclerotic plaques.

The utility of PDA is further supported by the low inter- and intraobserver variabilities in the determination of control tissue pixel intensities and in determining carotid plaque pixel distribution (Fig. 5). This indicates that once the data acquisition protocol is firmly established in the noninvasive vascular laboratory, the measurements become highly operator-independent. Another advantage of PDA is that it utilizes B-mode imaging, a capability that is readily available in all US machines in vascular laboratories throughout the country. Furthermore, image processing requires only basic image-analysis programs such as Adobe Photoshop, which are commercially available. Since duplex US is the current standard of care for evaluating extracranial CS, no additional testing or patient visits will be required to perform PDA. Ultimately, a simplified version of the image-analysis software can be incorporated in standard US machines to allow real-time PDA while the test is being conducted. Once incorporated into US software, PDA could be performed by technologists or physicians with an additional 10–15 min added to a routine carotid protocol.

Although currently an investigative method, PDA has the potential for being automated and integrated into routine US B-mode imaging. It can provide an inexpensive but rapid real-time assessment of carotid plaque composition and architecture. Histological comparisons confirm the accuracy of PDA-predicted tissue composition. These data also describe differences in the appearance of symptomatic and asymptomatic carotid plaques. These differences in composition and architecture are potential PDA markers for high risk of stroke. Further evaluation in large longitudinal studies will determine their efficacy in predicting future atheroembolic complications in asymptomatic patients with CS. Computer-assisted image-analysis systems have the potential to revolutionize the selection of treatment for asymptomatic and symptomatic carotid artery disease by either CEA or CAS.

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We appreciate the efforts of our registered vascular technologists (Jessenia Pineda, Regina Bayla, and Brenda Velez-Eng) at Saint Michael's Medical Center for the US scans. Support for this research was provided through grants from the American College of Surgeons, University of Medicine and Dentistry of New Jersey-New Jersey Medical School Foundation, and NINDS/NIH (RO1-NS38384-01).

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REFERENCES 

  1. Feeley TM , Leen EJ , Colgan MP , Moore DJ , Hourihane DO , Shanik GD . Histologic characteristics of carotid artery plaque . J Vase Surg . 1991;13:719–724
  2. Bassiouny HS , Sakaguchi Y , Mikucki SA , et al.   Juxtalumenal location of plaque necrosis and neoformation in symptomatic carotid stenosis . J Vasc Surg . 1997;26:585–594
  3. Carr S , Farb A , Pearce WH , Virmani R , Yao JS . Atherosclerotic plaque rupture in symptomatic carotid artery stenosis . J Vase Surg . 1996;23:755–756
  4. Park AE , McCarthy WJ , Pearce WH , Matsumura JS , Yao JS . Carotid plaque morphology correlates with presenting symptomatology . J Vase Surg . 1998;27:872–879
  5. Avril G , Batt M , Guidoin R , et al.   Carotid endarterectomy plaques: correlations of clinical and anatomic findings . Ann Vase Surg . 1991;5:50–54
  6. Stary HC , Chandler AB , Dinsmore RE , et al.   A definition of advanced types of atherosclerotic lesions and a histological classification of atherosclerosis. A report from the Committee on Vascular Lesions of the Council on Arteriosclerosis, American Heart Association . Arterioscler Thromb Vase Biol . 1995;15:1512–1531
  7. Carr SC , Cheanvechai V , Virmani R , Pearce WH . Histology and clinical significance of the carotid atherosclerotic plaque: implications for endovascular treatment . J Endovasc Surg . 1997;4:321–325
  8. Ohki T , Marin ML , Lyon RT , et al.   Ex vivo human carotid artery bifurcation stenting: correlation of lesion characteristics with embolic potential . J Vasc Surg . 1998;27:463–471
  9. Barnett HJ , Eliasziw M , Meldrum H . Plaque morphology as a risk factor for stroke . JAMA . 2000;284:177
  10. Faught WE , Mattos MA , van Bemmelen PS , et al.   Color-flow duplex scanning of carotid arteries: new velocity criteria based on receiver operator characteristic analysis for threshold stenoses used in the symptomatic and asymptomatic carotid trials . J Vasc Surg . 1994;19:818–827
  11. Goes E , Janssens W , Maillet B , Freson M , Steyaert L , Osteaux M . Tissue characterization of atheromatous plaques: correlation between ultrasound image and histological findings . J Clin Ultrasound . 1990;18:611–617
  12. Gray-Weale AC , Graham JC , Burnett JR , Byrne K , Lusby RJ . Carotid artery atheroma: comparison of preoperative B-mode ultrasound appearance with carotid endarterectomy specimen pathology . J Cardiovasc Surg (Torino) . 1988;29:676–681
  13. Reilly LM , Lusby RJ , Hughes L , Ferrell LD , Stoney RJ , Ehrenfeld WK . Carotid plaque histology using real-time ultrasonography. Clinical and therapeutic implications . Am J Surg . 1983;146:188–193
  14. Urbani MP , Picano E , Parenti G , et al.   In vivo radiofre-quency-based ultrasonic tissue characterization of the atherosclerotic plaque . Stroke . 1993;24:1507–1512
  15. Iannuzzi A , Wilcosky T , Mercuri M , Rubba P , Bryan FA , Bond MG . Ultrasonographic correlates of carotid atherosclerosis in transient ischemic attack and stroke . Stroke . 1995;26:614–619
  16. Belcaro G , Laurora G , Cesarone MR , et al.   Ultrasonic classification of carotid plaques causing less than 60% stenosis according to ultrasound morphology and events . J Cardiovasc Surg (Torino) . 1993;34:287–294
  17. Biasi GM , Mingazzini PM , Baronio L , et al.   Carotid plaque characterization using digital image processing and its potential in future studies of carotid endarterectomy and angioplasty . J Endovasc Surg . 1998;5:240–246
  18. el-Barghouty N , Nicolaides A , Bahal V , Geroulakos G , Androulakis A . The identification of the high risk carotid plaque . Eur J Vasc Endovasc Surg . 1996;11:470–478
  19. Sabetai MM , Tegos TJ , Clifford C , et al.   Carotid plaque echogenicity and types of silent CT brain infarcts. Is there an association in patients with asymptomatic carotid stenosis? . Int Angiol . 2001;20:51–57
  20. Lal BK , Hobson RW , Pappas PJ , et al.   Pixel distribution analysis of B-mode ultrasound scan images predicts histologic features of atherosclerotic carotid plaques . J Vasc Surg . 2002;35:1210–1217
  21. Beneficial effect of carotid endarterectomy in symptomatic patients with high-grade carotid stenosis. North American Symptomatic Carotid Endarterectomy Trial Collaborators . N Engl J Med . 1991;325:445–453
  22. Barnett HJ , Taylor DW , Eliasziw M , et al.   Benefit of carotid endarterectomy in patients with symptomatic moderate or severe stenosis. North American Symptomatic Carotid Endarterectomy Trial Collaborators . N Engl J Med . 1998;339:1415–1425
  23. Executive Committee for the Asymptomatic Carotid Atherosclerosis Study Endarterectomy for asymptomatic carotid artery stenosis . JAMA . 1995;273:1421–1428
  24. Barnett HJ , Meldrum HE , Eliasziw M . The dilemma of surgical treatment for patients with asymptomatic carotid disease . Ann Intern Med . 1995;123:723–725
  25. Stary HC , Chandler AB , Glagov S , et al.   A definition of initial, fatty streak, and intermediate lesions of atherosclerosis. A report from the Committee on Vascular Lesions of the Council on Arteriosclerosis, American Heart Association . Circulation . 1994;89:2462–2478
  26. AbuRahma AF , Kyer PD , Robinson PA , Hannay RS . The correlation of ultrasonic carotid plaque morphology and carotid plaque hemorrhage: clinical implications . Surgery . 1998;124:721–728
  27. Lusby RJ , Ferrell LD , Ehrenfeld WK , Stoney RJ , Wylie EJ . Carotid plaque hemorrhage. Its role in production of cerebral ischemia . Arch Surg . 1982;117:1479–1488
  28. Geroulakos G , Hobson RW , Nicolaides A . Ultrasonographic carotid plaque morphology in predicting stroke risk . Br J Surg . 1996;83:582–587
  29. Biasi GM , Froio A , Diethrich EB , et al.   Carotid plaque echolucency increases the risk of stroke in carotid stenting: the Imaging in Carotid Angioplasty and Risk of Stroke (ICAROS) study . Circulation . 2004;110:756–762
  30. Tegos TJ , Kalomiris KJ , Sabetai MM , Kalodiki E , Nicolaides AN . Significance of sonographic tissue and surface characteristics of carotid plaques . AJNR Am J Neuroradiol . 2001;22:1605–1612

PII: S0890-5096(06)60027-7

doi:10.1007/s10016-006-9000-8

Annals of Vascular Surgery
Volume 20, Issue 2 , Pages 167-174, March 2006

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