Endoscopic ultrasound (EUS) represents an advance in the diagnosis and staging of several diseases. EUS-guided fine-needle aspiration is useful, but technically demanding, and can be associated with complications. Elastography emerges as a useful tool that is based on the knowledge that some diseases, like cancer, lead to a modification in tissue stiffness. Elastography evaluates the elastic properties of tissues and compares images obtained before and after compression to target tissues; differentiating benign from malignant lesions. This article reviews theoretical aspects and the methodology of EUS elastography. Clinical applications, mainly in pancreatic diseases and lymph nodes, are analyzed.
Introduction
Endoscopic ultrasound (EUS) has evolved in recent years into a technique with a major clinical impact in digestive and mediastinal diseases. In fact, EUS has represented a major advance in the diagnosis and staging of several tumors, and can determine a change in diagnosis and management in 25% to 50% of cases. However, EUS is not only useful providing excellent images for detection and staging of several malignancies, it also provides guidance for fine-needle aspiration (FNA) and biopsies of almost all lesions detected during a standard procedure. Overall accuracy of EUS-guided FNA can be considered excellent, with sensitivities between 80% and 85%, and specificities close to 100%.
However, differential diagnosis of certain lesions, based only on B-mode image can be challenging and EUS-guided FNA and/or biopsy is technically demanding and multiple punctures of the lesions can be necessary to obtain sufficient tissue for cytohistologic assessment. EUS-guided FNA can also be associated with false-negative results, mainly in patients with solid pancreatic masses with the underlying diagnosis of chronic pancreatitis. Another limitation is related to the evaluation of lymph nodes. When several lymph nodes appear suspicious, the choice of which one to puncture is not always clear. Finally, EUS and EUS-guided FNA are associated with a small, but not insignificant, morbidity.
With this background, new methods allowing better characterization of lesions evaluated by EUS are essential to avoid the realization of unnecessary FNA and/or biopsies, to allow more accurate characterization of lesions before the puncture, and possibly to reduce complication rates. One of these new available methods is elastography.
It is well known that certain diseases, such as cancer, may induce changes in tissue stiffness. Elastography is a method for the real-time evaluation of tissue stiffness. This technique has been previously used for the analysis of superficial organ lesions, such as those of the breast and prostate. Elastographic images are an index of tissue elasticity, which may be related to histopathologic features. It has been considered virtual biopsy. Now, elastographic evaluation can be performed by EUS. Several studies have demonstrated that EUS-elastography is a promising technique with a high accuracy for the differential diagnosis of solid pancreatic tumors and lymph nodes.
This article analyzes the theoretical aspects and methodology of elastography, and reviews the actual indications and further development of this relatively novel method.
Theory and technical aspects of elastography
Elastography is based on the knowledge that some diseases (among them cancer) lead to a change in tissue hardness (elasticity modulus) and is an outgrowth of the well-known breast ultrasound fremitus technique, during which the patient is asked to hum while color or power Doppler is used to examine the breast. Softer portions of the breast vibrate more in response to the humming, whereas cancers and other solid lesions present a lower vibration rate and thus are seen as areas of decreased color, even if they are isoechoic on the ordinary B-mode ultrasound. Elastography examines the elastic properties of tissues by applying a slight compression to the tissue and comparing an image obtained before and after this compression. Both data obtained are compared by using a cross-correlation technique to determine the amount of displacement each small portion of tissue presented in response to the compression applied by the ultrasound transducer. The tissue elasticity distribution is calculated from the strain and the stress of the examined structures. Although the strain field can be estimated from the radio frequency signals returned from tissue structures before and after compression, it is impossible to measure the stress field directly within the tissue. Another problem is that the compression of harder tissue structures is often followed by a lateral displacement of these structures. It is nearly impossible to represent the volume of this sideslip with conventional two-dimensional methods, but its calculation is indispensable for an accurate determination of the tissue elasticity of the examined structures. To overcome these problems, the extended combined autocorrelation method has been developed, allowing the reconstruction of the tissue elasticity of the examined structures based on the three-dimensional finite element model. The new technique enables highly accurate estimation of the tissue elasticity distribution and adequate compensation of sideslips. The elasticity imaging can be performed in real time with the elastography module, which can be integrated into different HITACHI platforms (Hitachi Medical Systems Europe, Zug, Switzerland). Features of the elastographic patterns shown with the first generation of elastography, in terms of homogeneity or heterogeneity and predominant color, closely correlate with the histologic features of the lesion. New generations of elastography can also provide a quantitative elastographic evaluation.
Theory and technical aspects of elastography
Elastography is based on the knowledge that some diseases (among them cancer) lead to a change in tissue hardness (elasticity modulus) and is an outgrowth of the well-known breast ultrasound fremitus technique, during which the patient is asked to hum while color or power Doppler is used to examine the breast. Softer portions of the breast vibrate more in response to the humming, whereas cancers and other solid lesions present a lower vibration rate and thus are seen as areas of decreased color, even if they are isoechoic on the ordinary B-mode ultrasound. Elastography examines the elastic properties of tissues by applying a slight compression to the tissue and comparing an image obtained before and after this compression. Both data obtained are compared by using a cross-correlation technique to determine the amount of displacement each small portion of tissue presented in response to the compression applied by the ultrasound transducer. The tissue elasticity distribution is calculated from the strain and the stress of the examined structures. Although the strain field can be estimated from the radio frequency signals returned from tissue structures before and after compression, it is impossible to measure the stress field directly within the tissue. Another problem is that the compression of harder tissue structures is often followed by a lateral displacement of these structures. It is nearly impossible to represent the volume of this sideslip with conventional two-dimensional methods, but its calculation is indispensable for an accurate determination of the tissue elasticity of the examined structures. To overcome these problems, the extended combined autocorrelation method has been developed, allowing the reconstruction of the tissue elasticity of the examined structures based on the three-dimensional finite element model. The new technique enables highly accurate estimation of the tissue elasticity distribution and adequate compensation of sideslips. The elasticity imaging can be performed in real time with the elastography module, which can be integrated into different HITACHI platforms (Hitachi Medical Systems Europe, Zug, Switzerland). Features of the elastographic patterns shown with the first generation of elastography, in terms of homogeneity or heterogeneity and predominant color, closely correlate with the histologic features of the lesion. New generations of elastography can also provide a quantitative elastographic evaluation.
Procedure technique and criteria
As with traditional color Doppler imaging, EUS tissue-elasticity imaging is performed with conventional EUS probes. The vibrations and compressions are provided physiologically by vascular pulsation and respiratory motion. The elastography module available for use with EUS scopes today provides two generations of this technique. The first generation allows a qualitative evaluation and the second generation allows a quantitative evaluation of tissue stiffness.
Qualitative EUS-Elastography
Elastography modules provided in the ultrasound devices enables real-time elastographic evaluation and recording. The technology is based on the detection of small structure deformations within the B-mode image caused by compression, so that the strain is smaller in hard tissue than in soft tissue. The degree of deformation is used as an indicator of the stiffness of the tissue. Different elasticity values (on a scale of 1–255) are marked with different colors resulting in different tissue elasticity patterns, represented in color superimposed over the conventional B-mode. The system is set-up to use a hue color map (red-green-blue), where hard tissue areas are shown in dark blue, medium-hard tissue areas in cyan, intermediate tissue areas in green, medium-soft tissue areas in yellow, and soft tissue areas in red. During the procedure, two-panel images are shown, with the usual conventional grayscale B-mode image on the right side and the elastographic image on the left side ( Fig. 1 ). To perform a correct elastographic evaluation, the probe needs to be attached to the wall just exerting the pressure needed for an optimal and stable B-mode image at 7.5 MHz. The region of interest (ROI) for the elastographic evaluation is manually selected and, when possible, the whole targeted lesion as well as surrounding tissues needs to be included. Maximal sensitivity for elastographic registration should be used to give the final elastographic evaluation and, because elastographic images tend to show rapid changing colors, a stable image for at least 5 s is required for the final color pattern definition.
Initial clinical research involving elastography was focused on the evaluation of breast masses and initial patterns were described in these lesions. Three different patterns have been identified in elastograms of breast cancers: a well-defined, very hard (dark) mass or nodule; a moderately hard mass or nodule containing much harder (darker) foci within it; and a very dark or hard central core surrounded by a somewhat softer or less dark peripheral component. Although with conventional ultrasound or EUS fibrosis generally appears as hyperechogenic regions with posterior acoustic shadowing (an appearance also seen in cancers), with elastography it generally appears as a uniform, moderately hard region with no distinct foci of increased hardness. Preliminary work in breast tissue elastography has shown that it can correctly classify most benign and malignant masses. For EUS-elastography, different patterns have been described (see later discussion).
Quantitative EUS-Elastography
There are two options to perform a quantitative elastographic evaluation: using the calculation of hue-histogram with specific software or using the new elastography module allowing the strain ratio calculation.
Hue histogram
Possibly one of the most useful tools available in digital medical images is the histogram—a graphical representation of the colors (hues) distribution. Specific software is freely available that allows performing this evaluation (ImageJ software, NIH, Bethesda, MD, USA).
Calculation of hue-histograms is based on the qualitative EUS elastographic image obtained during the standard procedure. Once the optimal elastographic image is selected, the lesion to study for the hue-histogram is manually selected. For the hue-histogram analysis, on the x-axis of the histogram the numeric values of the elasticity are displayed on a scale from 0 (softest) to 255 (hardest). On the y-axis, the height of the spikes displayed indicates the number of pixels of each elasticity level found in the ROI. In the new HITACHI machines, software is included that allows the calculation of the hue-histogram. However, there is a slight difference compared with the ImageJ software in which the scale is from 0 to 250, but 0 is the hardest and 255 the softest ( Fig. 2 ). Consequently, the mean value of the histogram corresponds to the global hardness or elasticity of the tumors.
Strain ratio
Strain ratio was developed to add quantitative diagnostic information to pattern recognition. Strain ratio is based on the assumption that the hardness of connective or fat tissue does not vary between individuals. Taking into account that colors obtained during the elastographic evaluation are relative to each ROI, the relationship between the color patterns of lesions and the surrounding tissue sometimes provides the most meaningful data. The software extracts various features from real-time images. It converts the color values inside of the ROI into a relative strain value and calculates other features of the elastographic image, such as the mean of the relative strain value, the standard deviation of the relative strain value, and the proportion of the blue region in the region analyzed. This software also allows evaluation of the uniformity of the target area and quantifies the number of objective parameters of the distribution of hardness.
Calculation of strain ratio is based on the qualitative EUS elastographic image obtained during the standard procedure. Two areas (A and B) from the ROI are selected for quantitative elastographic analysis. Area A represents the area of the target lesion, including the biggest possible area of the lesion. Area B refers to a soft (red) reference area outside the area under investigation, with the gut wall being the best option to select. The quotient B/A (strain ratio) is the measure of the quantitative elastographic evaluation ( Fig. 3 ).
Clinical applications of EUS-elastography
This article focuses on the accepted and potential indications of EUS-elastography. First, the usefulness of this new technique in the diagnosis and management of pancreatic diseases is reviewed. Second, the focus is on the management of lymph nodes. Finally, the future and theoretical indications of EUS-elastography are commented on.
Evaluation of Pancreatic Diseases
As previously noted, EUS provides high-resolution images of the pancreas and it is considered one of the most accurate methods for the diagnosis and staging of chronic inflammatory, cystic, and neoplastic pancreatic diseases. However, differentiation between pancreatic cancer and focal pancreatitis remains a challenge based only on B-mode imaging, particularly in cases of advanced chronic pancreatitis. Several studies have attempted to establish EUS-imaging criteria for the discrimination between benign inflammatory lesions and malignant tumors. However, despite the high resolution of EUS, overall accuracy in this setting is not higher than 75%. EUS-elastography seems to be a useful tool in the evaluation of pancreatic diseases.
Differential diagnosis of solid pancreatic lesions
Giovannini and colleagues published the first experience with elastography in pancreatic solid lesions. They analyzed 24 pancreatic masses using a subjective scoring system based on different color patterns of the images. Lesions appearing mostly blue (harder) were classified as malignant. Based on this classification, sensitivity and specificity for detecting malignancy was 100% and 67%, respectively. The investigators performed a more refined classification. Score 1 (normal pancreatic tissue) was given to a homogeneous low elastographic area (soft or green). Score 2 (fibrosis) was given to images with heterogeneity of the elastographic area in the soft-tissue range (green, yellow, and red). Score 3 (early pancreatic adenocarcinoma) was given to an elastographic image that was largely blue (hard) with minimal heterogeneity. Score 4 (hypervascular lesion, such as neuroendocrine tumor or small pancreatic metastasis) was given to an image with a hypoechoic region in the center of the tumor, with appearance of a small green area surrounded by blue or harder tissue. Finally, score 5 (advanced pancreatic adenocarcinoma) was assigned to lesions that were largely blue on elastography images but with heterogeneity of softer tissue colors representing necrosis. Subsequently, Giovannini and colleagues published the results from a multicenter study, including 121 pancreatic masses. Elastography showed a malignant aspect (blue color) for all pancreatic adenocarcinomas, endocrine tumors, pancreatic metastases, and pancreatic sarcomas. All inflammatory masses presented a benign aspect (mixed green and low intensity of blue). Using the previous classification, considering score 1 and 2 as benign and 3 to 5 as malignant, sensitivity, specificity, positive predictive value, and negative predictive value in the differentiation between benign and malignant pancreatic masses were 92.3%, 80.6%, 93.3%, and 78.1%, with an overall accuracy of 89.2%. An interobserver agreement evaluation on 30 cases showed a kappa score of 0.785 for determination of malignancy. Iglesias-Garcia and colleagues have published their experience with qualitative EUS-elastography in 130 patients with solid pancreatic masses and 20 controls. The investigators detected four different patterns, similar to those described by Giovannini and colleagues. These include a homogeneous green pattern present only in normal pancreas; a heterogeneous, green-predominant pattern, with slight yellow and red lines, present only in inflammatory pancreatic masses; a heterogeneous, blue-predominant pattern, with slight green areas and red lines and a geographic appearance, present mainly in pancreatic malignant tumors (among them pancreatic adenocarcinoma); and a homogeneous blue pattern, present only in pancreatic neuroendocrine malignant lesions. By using this classification, sensitivity, specificity, positive and negative predictive values, and overall accuracy of EUS-elastography for malignancy were 100%, 85.5%, 90.7%, 100%, and 94.0%, respectively. An interobserver agreement analysis was also conducted in which both endosonographers agreed in 121 cases and the 20 controls, with a kappa value of 0.772. Fig. 4 shows the different patterns previously described for solid pancreatic lesions.
Related posts:
Endoscopic Ultrasonography/Fine-Needle Aspiration and Endobronchial Ultrasonography/Fine-Needle Aspiration for Lung Cancer Staging
Endoscopic Ultrasonography-Guided Biliary Drainage: Rendezvous Technique
Endoscopic Ultrasound-Guided Fiducial Markers and Brachytherapy
Endoscopic Ultrasound: Contrast Enhancement
Endoscopic Ultrasonography-Guided Tumor Ablation
Endoscopic Ultrasonography-Guided Pancreatic Drainage
Stay updated, free articles. Join our Telegram channel
Full access? Get Clinical Tree
