Dynamic analysis of EUS used for the differentiation of benign and malignant lymph nodes

ORIGINAL ARTICLE: Clinical Endoscopy

Dynamic analysis of EUS used for the differentiation of benign and malignant lymph nodes Adrian S aftoiu, MD, PhD, Peter Vilmann, MD, PhD, Tudorel Ciurea, MD, PhD, Gabriel Lucian Popescu, Eng, MSc, Alexandru Iordache, Eng, Hazem Hassan, MD, Florin Gorunescu, PhD, Sevastit¸ a Iordache, MD Hellerup, Denmark, Craiova, Romania

Background: EUS elastography was reported to offer supplemental information that allows a better characterization of tissue, and that might enhance conventional EUS imaging. Objective: Our purpose was to apply real-time elastography during EUS examinations and to assess the accuracy of the differentiation of benign versus malignant lymph nodes. Design: Prospective cross-sectional feasibility study. Setting: Department of Surgical Gastroenterology, Gentofte University Hospital, Hellerup, Denmark. Patients: Patients diagnosed by EUS with cervical, mediastinal, or abdominal lymph nodes were included, with a total number of 78 lymph nodes examined. The final diagnosis of the type of lymph node was obtained by EUSFNA cytologic analysis or by surgical pathologic examination and by a minimum 6 months of follow-up. Interventions: Hue histogram analysis of the average images computed from EUS elastography movies was used to assess the color information inside the region of interest and to consequently differentiate benign and malignant lymph nodes. Main Outcome Measurements: Differentiate between malignant and benign lymph nodes. Results: By using mean hue histogram values, the sensitivity, specificity, and accuracy for the differential diagnosis were 85.4%, 91.9%, and 88.5%, respectively, on the basis of a cutoff level of 166 (middle of green-blue rainbow scale). The proposed method might be useful to avoid color perception errors, moving artifacts, or possible selection bias induced by analysis of still images. Limitations: Lack of the surgical standard in all cases. Conclusions: Computer-enhanced dynamic analysis based on hue histograms of the EUS elastography movies represents a promising method that allows the differential diagnosis of benign and malignant lymph nodes, offering complementary information added to conventional EUS imaging. (Gastrointest Endosc 2007;66:291-300.)

EUS elastography was recently reported to offer supplemental information that appears to obtain a better characterization of tissue, and that might enhance conventional EUS imaging.1-3 The method might be useful to visualize the elasticity distribution, because it shows differences in tissue hardness between various structures examined. EUS elastography was developed to analyze structures in real time, with the information being repre-

Copyright ª 2007 by the American Society for Gastrointestinal Endoscopy 0016-5107/$32.00 doi:10.1016/j.gie.2006.12.039

sented in transparent color superimposed on conventional gray-scale B-mode scans. The method has been used in preliminary reports for the characterization and differentiation of benign and malignant lymph nodes, with variable sensitivity, specificity, and accuracy, higher than the conventional EUS criteria.2,3 However, the number of published studies is low and the method still needs to be further refined because of potential pitfalls caused by possible perception errors and motion artifacts and the impossibility of controlling tissue compression.4 The application of EUS elastography for exclusion of malignancy or preferential targeting of the most suspicious lymph nodes should be regarded as

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complementary information rather than a replacement of tissue confirmation by EUS-guided FNA or trucut biopsy. Because the assessment of the risk of malignancy is very important for the clinical decision-making process and subsequent invasive staging procedures, the development of a noninvasive imaging procedure with acceptable positive predictive value (PPV) and negative predictive value (NPV) would be highly desirable. The aim of the study was to analyze whether computerenhanced dynamic analysis of EUS elastography images is able to better characterize and differentiate benign and malignant lymph nodes. Because we previously reported that color histograms on static EUS elastography images (still images) might be able to improve the sensitivity, specificity, and accuracy of the differential diagnosis,3 our approach was to use a dynamic hue histogram analysis that might reduce possible selection bias or artifacts.

PATIENTS AND METHODS

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Capsule Summary What is already known on this topic d

d

EUS elastography might be useful in the visualization of tissue elasticity distribution because it shows differences in tissue hardness between structures. EUS elastography was developed to analyze structures in real time, with the information represented in transparent color superimposed on conventional grayscale B-mode scans.

What this study adds to our knowledge d

In a prospective feasibility study with mean hue histogram values, the sensitivity, specificity, and accuracy for determining benign versus malignant lymph nodes was 85.4%, 91.9%, and 88.5%, respectively, adding valuable information to conventional EUS imaging.

The following data were prospectively collected for all the patients: personal data (name, surname, sex, age, examination date, social security number, diagnosis at admission, clinical history), conventional EUS examination with EUS-guided FNA, EUS elastography results, and final diagnosis obtained by EUS-FNA cytology or surgical pathologic examination. A written informed consent was obtained for all the patients before EUS with FNA biopsy and EUS elastography. EUS, EUS-FNA, and EUS elastography of the lymph nodes were performed during the same EUS examination with a Hitachi 8500 US system with an embedded SonoElastography module (Hitachi Medical Systems Europe Holding AG, Zug, Switzerland), used in conjunction with a EG 3830 Pentax linear endoscope (Pentax, Hamburg,

Germany). All EUS-FNA procedures were performed with 22 G Sonotip 2, single-use biopsy needles (Medi-Globe Ltd, Achenmu ¨ hle, Germany). All the patients received sedation-analgesia with midazolam and fentanyl. Linear EUS with EUS-FNA included complete examinations of the cervical, mediastinal, and abdominal lymph nodes. Lymph node characteristics were carefully described for each lymph node: size (long axis), echogeneity (hypoechoic, hyperechoic, mixed), echostructure (homogenous, inhomogenous, calcifications), shape (round, oval, triangular), and border (regular, irregular). EUS-FNA was performed, with at least 2 passes per lymph node, with continuous suction, according to a technique described in detail elsewhere.6 The tip of the needle was placed in the lesion under real-time EUS guidance, with at least 10 toand-fro movements performed inside the lesion. The specimen was expelled onto glass slides, smeared, air dried, and stained for cytologic examination. Because a cytopathologist was not present in the examination room, we performed at least 2 passes, judging the quality of the specimen according to the macroscopic appearance of the aspirate.7 EUS elastography was performed during the EUS examinations, with 2 movies of 20 seconds recorded on the hard disk drive embedded in the US system to minimize variability and to increase repeatability of acquisition. A 2-panel image with the usual conventional gray-scale B-mode EUS image on the right side and with the elastography image on the left side was used (Figs. 1-3). The examination frequency during EUS elastography was always set at 7.5 MHz. The software includes a registration scale of the compression of the transducer against the tissue. This compression threshold was set to 3 to 4 to standardize the examination. The same conditions of brightness, contrast, intensity, and gain of the US system were used in all examinations. However, because the numeric elastography information is displayed using a rainbow colorcoded scale with values from 0 to 255, changes in the

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Patients The study design was prospective. A total of 54 consecutive patients with cervical, mediastinal, and abdominal lymph nodes were examined by EUS during a 6-month period at the Department of Gastrointestinal Surgery D, Gentofte University Hospital, Hellerup, Denmark. Patients were 34 to 79 years old (mean age 61.4 years), 32 men and 22 women. Cancer patients with various primary tumor locations staged by EUS were included: esophageal, gastric, pancreatic, lung, colon, breast, uterine cancer, and even melanoma. The following lymph node stations were carefully examined during the staging procedures: cervical, mediastinal (stations 2R, 2L, 4R, 4L, 5, 7, 8, and 9 according to the Mountain Dressler lung cancer classification5) and abdominal (celiac, splenic, perigastric, and peripancreatic). Patients with known benign lymph nodes identified during EUS examinations for acute and chronic pancreatitis or prepyloric ulcer were also included.

Protocol of examination

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Analysis of EUS elastography to differentiate benign and malignant lymph nodes

Figure 1. A, Typical appearance on EUS elastography of a benign mediastinal lymph node (scattered soft, mixed green-yellow-red). Instantaneous hue was 102.37 (47.68) (mean [SD]). B, Artifactual appearance on some of the EUS elastography frames of the same benign mediastinal lymph node (homogenous hard, predominantly blue). Instantaneous hue was 225.40 (32.27) (mean [SD]). The artifact might have been caused by the inclusion in the sample elastography window of a big vessel that might interfere with the sonoelastography software. C, Appearance of the blurred average image in the same benign mediastinal lymph node that excludes artifacts, wherein each individual pixel stores average intensity over all frames at a corresponding pixel location from a 10-s movie. Average hue was 117.60 (30.92) (mean [SD]).

system setting did not affect the subsequent postprocessing process. All examinations were done by 2 experienced EUS examiners (A. S. and P. V.) in a typical clinical setting with previous knowledge of the patient’s underlying disease. The EUS examination was immediately followed by a EUS elastography examination, whereas EUS-FNA was always performed at the end of the examination of each lymph node targeted. The EUS examiners were blinded to the cytopathologic results, which were obtained later after careful review of the slides. To obtain an unbiased opinion, all movies recorded during the EUS examinations were later reviewed and postprocessed by other examiners (T. C., G. L. P.), who were blinded to the clinical information and the EUS-FNA results. Because all the postprocessing analysis of the EUS elastography movies was done by using a predefined procedure with the same computer programs

(see below), the final information obtained was certainly reproducible and devoid of any observer or selection bias.

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Technical details The technical principle of the sonoelastography method was previously described in detail elsewhere.1,8 In brief, EUS elastography is a recent imaging procedure that allows the assessment of elasticity distribution and shows differences in hardness between diseased tissue and normal tissue.1-4 The principle of elastography assumes that tissue compression produces strain (displacement) within the tissue and that the strain is smaller in harder tissue compared with softer tissue.4 Consequently, by measuring the tissue strain induced by compression, it is possible to estimate the tissue hardness, which might be useful in diagnosing and differentiating malignant tumors. EUS elastography thus estimates the axial strain of the

Analysis of EUS elastography to differentiate benign and malignant lymph nodes

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Figure 2. A, Typical appearance on EUS elastography of a malignant lymph node (homogenous hard, predominantly blue). Instantaneous hue was 238.87 (15.78) (mean [SD]). B, Artifactual appearance on some of the EUS elastography frames of the same malignant lymph node (in-homogenous soft, predominantly green-yellow). Instantaneous hue was 124.51 (42.34) (mean [SD]). The artifact might have been caused by the inclusion in the sample elastography window of a vessel that might interfere with the sonoelastography software. C, Appearance of the blurred average image in the same malignant lymph node that excludes artifacts, wherein each individual pixel stores average intensity over all frames at a corresponding pixel location from a 10-s movie. Average hue was 187.64 (22.37) (mean [SD]).

tissues along the direction of insonification/compression. This is done by analyzing backscattered US signals returned if the tissue is slightly compressed and decompressed during the procedure and can be recently obtained in real time with standard US systems.1 For the real-time sonoelastography method used in our study, little additional compression is required because the pressure from respiratory movements or pulsation of surrounding vessels is normally sufficient. Real-time EUS elastography can be performed with the conventional EUS probes without any need for additional equipment that induces vibration or pressure. The method is similar with color Doppler examinations with a manually selected trapezoidal window used for the elasticity calculations. This should include the targeted lesion, as well as the soft surrounding tissues, because the elasticity values are displayed relative to the average strain inside

the trapezoidal window. Different elasticity values are marked with different colors (on a scale of 1 to 255) and the EUS elastography information is shown as a transparent color layout superimposed on the conventional gray-scale image. The system uses by default a rainbow color-coded map (red-green-blue) (RGB), where hard tissue areas are marked with dark blue, medium hard tissue areas with cyan, intermediate tissue areas with green, medium soft tissue areas with yellow, and soft tissue areas with red.

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Computer-enhanced dynamic analysis Each acquired EUS elastography movie was subjected to a computer-enhanced dynamic analysis using a public domain Java-based image processing tool (Image J) developed at the National Institutes of Health, Bethesda, Maryland.9 To minimize the human bias, all the postprocessing and computer analysis of digital movies was performed within the

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Analysis of EUS elastography to differentiate benign and malignant lymph nodes

IT Center of the University of Medicine and Pharmacy of Craiova with all programmers and statisticians being blinded to the clinical and pathological information. In a previous pilot study,3 we tried to assess the EUS elastography characteristics of lymph nodes as a function of a qualitative pattern (with 5 types) or a semiquantitative histogram analysis performed separately on each of the basic RGB channels. Now we decided that the hue histogram, taking into account the rainbow color map used as a default setting in the sonoelastography software, would better describe the hardness or elasticity of the lymph node as a monotone and bijective function. Thus, 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 region of interest (ROI). Consequently, the mean value of the histogram corresponds to the global hardness or elasticity of the lymph node, whereas the SD gives an indication on how homogenous the lymph node is. The histogram analysis was previously used for the characterization of lymph nodes on the conventional gray-scale EUS images, and the method is described in detail elsewhere.10 We found that this method is hampered by the calculation of histogram on still (static) images, because of the arbitrary selection of frames from a dynamic sequence, as shown in Figs.1B and 2B, corresponding to Figs. 1A and 2A, respectively. To eliminate the possibility of inducing a high selection bias followed by a significant intraobserver and interobserver variability, average images were used by applying the ‘‘average intensity’’ facility of the Image J software.9 Each pixel in the output image stores intensity values averaged over all images (individual frames or stacks) in the movie at corresponding pixel position. The averaged images are usually blurred because of the inherent motion of the tissues during real-time examinations, but this effect could be eliminated by using edge detection algorithms bound to keep the ROI fixed to the lymph node. However, these algorithms have not yet been tested and are beyond the scope of this article. After obtaining average images from the movie, a single-hue histogram analysis of the ROI was performed by using a custom-designed JAVA plug-in called AverHue. This plug-in computes and displays the mean value and SD of the averaged hue histogram, as shown in Figs. 1C and 2C. The mean values taken from AverHue were afterward used for the calculation of the receiver-operating characteristic (ROC) curve and for other statistical analysis. On the basis of a similar approach we have developed another JAVA plug-in for ImageJ to filter the EUS elastography movies. Because stray frames (ie, frames that have the mean value of their histogram falling out of a trust interval around the mean of the global histogram) affect the mean and SD values and thus affect the classification and decision process, the filtering algorithm could crop them out. The www.giejournal.org

Figure 3. A, Small lymph node with a mixed appearance, difficult to be categorized as definitely ‘‘benign’’ or ‘‘malignant’’ on the basis of the EUS elastography appearance because of visual perception and quantification errors of the blue-green areas. B, Dynamic analysis based on a hue histogram display of each frame of the EUS elastography movie, allows the characterization of individual images and possible filtering of the artifacts as a function of the mean hue values. The mean hue value of the manually selected ROI (lymph node) allows the characterization of the lymph node as malignant on the basis of the cutoff value of 166. Instantaneous hue was 207.95 (25.34) (mean [SD]).

plug-in was used to compute and dynamically analyze the individual hue histograms of each image (frame) from an EUS elastography movie (Fig. 3). After a mean and SD of the hue histograms of the average images of the EUS elastography movies were computed, a narrow-band filter was applied to cut the images with hue histograms that were too dispersed (mean values of more than 10%), hence constituting possible sources of artifacts. The utility of the Volume 66, No. 2 : 2007 GASTROINTESTINAL ENDOSCOPY 295

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filter to eliminate artifacts will have to be further tested and validated in future studies.

Final diagnosis A malignant cytologic diagnosis was taken as a final proof of malignancy of the lymph nodes. The diagnoses obtained by EUS-FNA were further verified either by surgery or during the clinical follow-up. The diagnosis of benign lymph nodes in patients with cancer was always confirmed by surgery. Surgical pathologic study allowed a direct comparison with EUS elastography dynamic analysis of the lymph nodes, after the station number and location of the lymph nodes were identified according to vascular and other anatomic landmarks. A follow-up of at least 6 months was used to exclude malignancy in the patients with acute or chronic pancreatitis and benign prepyloric ulcer.

Saftoiu et al

Accuracy is measured by the area under the ROC curve. The area measures discrimination, that is, the ability of the test to correctly classify those with and without the disease. An area of 1 represents a perfect test; an area of 0.5 represents a worthless test. Accordingly, the area under the ROC curve was analyzed for its discriminatory ability by testing against the null hypothesis that the area under the curve was 0.5 (meaning no discriminating ability).13 It is recognized that there was multiple testing of outcome data arising from individual observations; however, it is noted that correction by the Bonferroni method would not have removed statistical significance from any of the findings.14

RESULTS

For the purposes of statistical analysis, it was assumed that data from different lymph nodes were statistically independent observations, even if some may have arisen from the same patient. Continuous variables were reported as mean and SD or median and range, if data were not normally distributed. SD was also used to denote SD in the figure legends (Figs. 1A-C, 2A-C, and 3B). Differences between the patients with benign and malignant lymph nodes were compared with the 2-sample t test (2 independent samples). Because this parametric method makes assumptions about normality and similar variances, both the Kolmogorov-Smirnov and Shapiro-Wilk W normality tests were performed. The equality of variances assumption was verified with the Levene test using F Fisher statistics. In the case of the 2-sample t test, the nonparametric alternative given by the Mann-Whitney U test was also used because in some instances it may even offer greater power to reject the null hypothesis than the t test. The 1-way analysis of variance (ANOVA) method was used to look at all the data simultaneously. A P value less than .05 was considered statistically significant.11 All statistical calculations were performed with Statistica ’99 Edition, kernel release 5.5 A (StatSoft Inc, Tulsa, Okla). ROC analysis was done in order to display the range of trade-offs between true-positive and false-positive rates possible with EUS elastography. A quantitative ROC analysis for mean values of the hue histogram of the region of interest examined from an average image of the EUS elastography movies was thus performed. The sensitivity, specificity, PPV, NPV, and accuracy for the differential diagnosis between benign and malignant lymph nodes were calculated for EUS elastography by comparing the results of dynamic analysis (mean hue histogram values of the region of interest) with the final diagnosis, as a function of the ROC analysis cutoff values. For ROC analysis we constructed ROC curves by plotting sensitivity against 1 – Specificity, for different values of the cutoff level.12

A total of 85 lymph nodes were examined in 54 patients by linear EUS. The final diagnosis was confirmed in 78 of these lymph nodes, and this was our final study group. The following locations were examined: cervical (n Z 10), mediastinal (n Z 51), celiac (n Z 9), perisplenic (n Z 2), peripancreatic (n Z 4), and para-aortic (n Z 2). The mean (SD) size of the lymph nodes was 17.4 (9.3) mm. From the total number of 78 lymph nodes included, 37 were benign and 41 malignant, with the final diagnosis based on a combination of EUS-FNA results (39 lymph nodes), surgery with lymph node dissection and pathology results (23 lymph nodes), and follow-up for at least 6 months (16 lymph nodes). The difference between the mean (SD) size of benign (16.4 [9.2] mm) and malignant (18.3 [9.4] mm) lymph nodes was not statistically significant (P Z .36). On the basis of EUS characteristics suggestive of malignancy (hypoechoic structure, sharp border, round contour, and size O1 cm), it was not possible to distinguish between benign and malignant lymph nodes. A combination of all 4 criteria used for differentiation of malignant lymph nodes was observed only in 11 malignant lymph nodes (14.1%), but also in 7 benign lymph nodes (9%). The sensitivity, specificity, and accuracy of the conventional EUS criteria of malignancy were consequently 26.8%, 81.1%, and 52.6%. The PPV and NPV were 61.1% and 50.0%, respectively. ROC analysis for the mean hue obtained through histogram analysis of the region of interest (lymph node) after averaging individual pixels over a 10-second EUS elastography recording yielded an area under the curve of 0.928 (P ! .0001, 95% CI [0.868-0.989]) (Fig. 4). The area under the ROC curve and its P value are considered versus the null hypothesis that the area under the curve was 0.5 (meaning no discriminating ability). The results of ROC analysis with a cutoff value of 166 were used to differentiate between ‘‘benign’’ and ‘‘malignant’’ lymph nodes on the EUS elastography movies. The cutoff value of 166 was exactly in the mid of hues of the green-blue spectrum. Thus from the beginning of green hues (77, brilliant

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Statistical analysis

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Analysis of EUS elastography to differentiate benign and malignant lymph nodes

TABLE 1. Conventional EUS versus EUS elastography and EUS-FNA for differentiation of malignant lymph nodes. Conventional EUS %

EUS elastography

EUS-FNA

No.

%

No.

%

No.

Sensitivity 26.8

11/41

85.4

35/41

95.1

39/41

Specificity

81.1

30/37

91.9

34/37

Accuracy

52.6

41/78

88.5

69/78

96.4

53/55

NPV

50.0

30/60

85.0

34/40

87.5

14/16

PPV

61.1

11/18

92.1

35/38

100

100

14/14

39/39

The utility of elasticity imaging and older palpation techniques is based on the hypothesis that the range of

elastic moduli varies between normal and pathologic human tissues.1-3 The mechanical properties of normal and diseased tissues are of pathologic relevance, whereas the development of a direct measure of tissue elasticity might be very important for the characterization of lesions.1 However, it is rather difficult to postulate that a specific level of hardness (compressibility) would be equivalent with the diagnosis of malignancy because some of the benign lesions are very hard (focal chronic pancreatitis), whereas some of the tumors are very soft (mucinous or cystic adenocarcinomas and tumors with extensive necrosis).15 Nevertheless, a correlation between the elasticity imaging results and histologic study would be highly desirable, either to represent a ‘‘virtual’’ biopsy used for noninvasive characterization of lesions or for the real-time guidance of biopsies in stiffer areas of the lesions.2,3 Different types of elasticity imaging procedures were already described in the literature, with clinical applications already developed for the diagnosis of breast lesions and prostate cancer.16-25 Static compression elastography was initially described, with data recorded before and after controlled compression of the tissues with an external device (up to 2% applied strain).17 Quasistatic (dynamic) compression elastography is a variant in which the probe is moving, whereas cross-correlation analysis is used to track tissue displacements and consequently to measure the strain field.18 Transient elastography uses a device based on 1-dimensional collection of data, being useful for assessing the stiffness of liver tissue and characterizing liver fibrosis.26-30 Translation of the color-coded information obtained through a recently described procedure such as US sono-elastography into clinical relevant information related to the elasticity (compressibility) of the tissues might represent a major breakthrough into the clinical practice.4 However, the procedure is still hampered by certain methodologic flaws and by the lack of adequately powered studies, backed by histologic diagnosis and in vitro validation of the results. Because the stiffness of normal and pathologic tissue is usually different, it may be assessed by EUS elastography,

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Figure 4. ROC curve of the mean hue values used for the discrimination of benign and malignant lymph nodes (sensitivity of 85.4% and specificity 91.9 % for a cutoff value of 166). The area under the ROC curve was 0.928 (P ! .0001). The area under the ROC curve was analyzed for its discriminatory ability by testing against the null hypothesis that the area under the curve was 0.5 (meaning no discriminating ability).

lime) to the end of the rainbow scale (255, vivid blue), the value of 166 is situated exactly in the middle (166, vivid aqua). This value is very difficult to be categorized by the human eye as either blue or green, whereas consequent perception errors might certainly induce possible artifacts or misinterpretations. Quantification of the hues inside the region of interest through histogram analysis of EUS elastography movies helped to obtain a better classification of ‘‘benign’’ and ‘‘malignant’’ lymph nodes, as opposed to a simple ‘‘blue’’ versus ‘‘green’’ categorization of lymph nodes. Consequently, the sensitivity, specificity, and accuracy of the hue histogram classification for the differential diagnosis of benign and malignant lymph nodes were 85.4%, 91.9%, and 88.5%, respectively. The PPV and NPV were 92.1% and 85.0%, respectively. The prevalence of the underlying condition of interest (malignancy) represented approximately half the total number of lymph nodes (41/78 Z 52.6%). EUS-FNA was done in 55 (70.5%) lymph nodes and was positive in 39 cases. EUS-FNA cytology was negative in 2 cases that were later confirmed as malignant after surgery. There were no false-positive results of EUS-FNA cytology our cases. Consequently, the sensitivity, specificity, and accuracy of EUS-FNA were 95.1%, 100%, and 96.4%. The PPV and NPV were 100% and 87.5%, respectively. The comparative accuracies of conventional EUS criteria, EUS elastography, and EUS-FNA are shown in Table 1.

DISCUSSION

Analysis of EUS elastography to differentiate benign and malignant lymph nodes

Saftoiu et al

which is based on B-mode scanning during compressions of the tissues. The technique of sonoelastography evaluated in this article is based on the analysis of the backscattered US signals returned while tissue is slightly compressed and decompressed during the procedure. The method uses the natural compressions of the tissues induced by respiratory movements and vessel pulsations rather than the compression induced by the EUS transducer. Tissue dynamic behavior is displayed in real time, whereas the signal processing is very similar to Doppler methods, with the examination results represented as color images over the conventional gray-scale B-mode images (Figs. 1-3).1-3 We agree with others that a simplistic correspondence between color-coded information and histologic diagnosis (green equivalent to ‘‘benign’’ and blue equivalent to ‘‘malignant’’) seems rather difficult to justify.15 Even the possibility of using scoring systems2 or pattern classifications3 represents a subjective method of interpretation of the results because it is usually accompanied by high intraobserver and interobserver variability. Interpretation of the images is also complicated by the dynamic nature of the procedure, with color-coded information that changes rapidly and is exposed to motion and perceptual artifacts. Computer-enhanced dynamic analysis of the images and movies was the objective of our current study, with a consequent classification of the benign and malignant lymph nodes on the basis of hue histograms analysis with semiquantification of the basic elastography information. We consider that a simplistic approach of categorizing benign and malignant lymph nodes as a function of basic colors (green vs blue) is highly subjective, whereas a dynamic hue histogram analysis of the region of interest would better describe the elasticity values inside the lymph node. This approach would certainly avoid the perception artifacts induced by the merging of blue into green, and it would also provide an answer to the question of ‘‘how much green is allowed in a supposedly blue area to be considered malignant?’’15 On the basis of our hypothesis of dividing the green-blue rainbow scale into half (with a cutoff value of 166), we categorized the lymph nodes as either ‘‘benign’’ or ‘‘malignant’’ on the basis of a dynamic analysis of the EUS elastography movies. This would also eliminate the selection bias induced by analysis of static images3 because it takes into account the information contained in several frames of an EUS elastography movie. However, artifacts induced by the nearby presence of very low or very hard density and stiffness structures cannot be excluded because of the sonoelastography method, which assumes computations relative to the average strain inside the EUS elastography region of interest.1 The differential diagnosis between benign and malignant lymph nodes in the abdomen or mediastinum is usually difficult, wheras most authors agree that the classic criteria (homogenous, hypoechoic appearance, sharp

borders, round shape, size more than 1 cm) are not very useful.31-33 Problems appear to be due to the presence of large reactive, inflammatory lymph nodes observed in the vicinity of a primary tumor or in the mediastinum of the smokers and to the presence of small lymph nodes that may harbor micrometastases.34 Thus, EUS has a high sensitivity for the detection of lymph nodes, but the specificity of morphologic criteria for differentiation between benign and malignant lymph nodes is still rather small.35,36 Several recent studies found a low accuracy of EUS, with none of the previously described imaging characteristics being reliable for diagnosing or excluding malignancy in lymph nodes.37-40 EUS-guided FNA increases the accuracy for the detection of malignant regional or distant lymph nodes,41-43 with a specificity of EUS-FNA close to 100%.38 However, in the cases with multiple lymph nodes a selection has to be made, and the choice of lymph node sampling remains difficult.42 EUS-FNA seems justified only in the situations where a positive result would have a definitive clinical impact, being most useful for confirmation of distant metastases.43-45 Consequently, an imaging method that could accurately differentiate benign from malignant lymph nodes is still needed for a minimal invasive staging of cancers because of the significant impact on treatment decisions.3 By using a dynamic hue histogram analysis we were able to differentiate benign and malignant lymph nodes with a high sensitivity, specificity, and accuracy (85.4%, 91.9%, and 88.5%, respectively). The area under the ROC curve of the mean hue values used for the discrimination of benign and malignant lymph nodes was 0.928 (P ! .0001). Moreover, half the lymph nodes included in our study were malignant and the fact that the prevalence of the underlying condition of interest was approximately 50% was certainly one of the strengths of this study. We previously stated that EUS elastography might be used as an add-on to EUS for the characterization and differentiation of benign versus malignant lymph nodes, but we do agree with other authors that this method should be an adjunct and not a replacement for tissue confirmation. Although the accuracy was smaller compared with an initial pilot study previously published by our group,3 the PPV and NPV were still 92.1% and 85%, respectively. On the basis of a high PPV, the most probable lymph nodes to harbor malignancy could be targeted preferentially by EUS-FNA, whereas the high NPV could be used for exclusion of EUS-FNA in some of the lymph nodes that are considered most probably benign. EUS elastography also offers an alternative for the differential diagnosis in the situations of negative EUS-FNA biopsy or in the case of impossibility of puncture because of technical problems or interposed malignant tissue and so forth. The same approach of selective use of EUS-FNA as a function of modified EUS criteria for lymph node staging was previously proposed to enhance the differential diagnosis of benign and malignant lymph nodes.46 The authors included the

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Analysis of EUS elastography to differentiate benign and malignant lymph nodes

lymph node location, tumor stage, and number of nodes identified by EUS as additional diagnostic criteria for lymph node staging. Most important, the study also showed a reduction in the cost of preoperative staging by using this selective approach for EUS-FNA during lymph node staging. Our study has several limitations. Because consecutive patients examined by EUS were included, lymph nodes with different locations (abdominal, mediastinal, and cervical) and diseases were examined. The different physical characteristics of the lymph nodes could change the elastography information and might affect the results obtained. Because of the limited number of patients, subgroup analysis was not performed to test the discriminatory ability of EUS elastography separately for cervical (n Z 10), mediastinal (n Z 51), or abdominal (n Z 17) lymph nodes. Another important limitation of our study was that the sonoelastography method used computes elasticity values inside the ROI relative to the average strain inside the trapezoidal EUS elastography window. Surrounding tissues have to be included in the region examined by EUS elastography, and different physical characteristics of the tissues might also influence the results. However, the current technology does not yield absolute values of the elasticity inside the lymph node because of the technical principle of the sonoelastography and the use of the autocorrelation method. The specific cutoff value used in the study (166) was determined with knowledge of the observations analyzed to compute the diagnostic test statistics and should be again tested in a new cohort of patients with a prospective design that includes blinded analysis of the EUS elastography movies. Nevertheless, because we have used a computer-enhanced dynamic analysis of the EUS elastography movies by computing hue histograms, we have tried to reduce the intrinsic operator dependence of a complex imaging method like EUS elastography, and consequently we have obtained excellent performance characteristics of the test. In conclusion, elastography imaging thus offers complementary information added to conventional EUS imaging. However, future randomized studies with adequate power will have to establish the clinical impact of this procedure. Improvements of the software used and quantification of the elastography information through in vitro measurements of tissue explants might also add to the quality of the results. Future cost-effectiveness studies might also determine whether the use of EUS elastography will add a significant improvement of the current staging procedures, which are time consuming, cost inefficient, and resource draining.

DISCLOSURE The authors have no conflicts of interest to disclose. www.giejournal.org

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Received August 22, 2006. Accepted December 18, 2006. Current affiliations: Department of Surgical Gastroenterology (A.S., P.V., H.H.), Gentofte University Hospital, Hellerup, Denmark, Department of Gastroenterology (T.C., S.I.), IT Center (G.L.P., A.I.), Department of Biostatistics (F.G.), University of Medicine and Pharmacy, Craiova, Romania. Presented in part as a poster during Digestive Disease Week 2006, May 2025, 2006, Los Angeles, California. Reprint requests: Adrian Saftoiu, MD, PhD, Department of Gastroenterology, University of Medicine and Pharmacy, Str Horia nr 11, Craiova, Dolj, 200490, Romania.