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. 2014 Jan 8:14:7.
doi: 10.1186/1471-230X-14-7.

Digital image analysis of endoscopic ultrasonography is helpful in diagnosing gastric mesenchymal tumors

Gwang Ha Kim  1 , Kwang Baek Kim, Seung Hyun Lee, Hye Kyung Jeon, Do Youn Park, Tae Yong Jeon, Dae Hwan Kim, Geun Am Song
Affiliations

Digital image analysis of endoscopic ultrasonography is helpful in diagnosing gastric mesenchymal tumors

Gwang Ha Kim et al. BMC Gastroenterol. .

Abstract

Background: Endoscopic ultrasonography (EUS) is a valuable imaging tool for evaluating subepithelial lesions in the stomach. However, there are few studies on differentiation between gastrointestinal stromal tumors (GISTs) and benign mesenchymal tumors, such as leiomyoma or schwannoma, with the use of EUS. In addition, there are limitations in the analysis of the characteristic features of such tumors due to poor interobserver agreement as a result of subjective interpretation of EUS images. Therefore, the aim of this study was to evaluate the role of digital image analysis in distinguishing the features of GISTs from those of benign mesenchymal tumors on EUS.

Methods: We enrolled 65 patients with histopathologically proven gastric GIST, leiomyoma or schwannoma on surgically resected specimens who underwent EUS examination at our endoscopic unit from January 2007 to September 2010. After standardization of the EUS images, brightness values including the mean (Tmean), indicative of echogenicity, and the standard deviation (TSD), indicative of heterogeneity, in the tumors were analyzed.

Results: The Tmean and TSD were significantly higher in GIST than in leiomyoma and schwannoma (p < 0.001). However, there was no significant difference in the Tmean or TSD between benign and malignant GISTs. The sensitivity and specificity were almost optimized for differentiating GIST from leiomyoma or schwannoma when the critical values of Tmean and TSD were 65 and 75, respectively. The presence of at least 1 of these 2 findings in a given tumor resulted in a sensitivity of 94%, specificity of 80%, positive predictive value of 94%, negative predictive value of 80%, and accuracy of 90.8% for predicting GIST.

Conclusions: Digital image analysis provides objective information on EUS images; thus, it can be useful in diagnosing gastric mesenchymal tumors.

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Figures

Figure 1
Figure 1
Flow diagram illustrating the standardization process of the EUS image.
Figure 2
Figure 2
Extraction process of the anechoic center and outer hyperechoic rim of the EUS scope. (a) Gray image. (b) Smoothing method. (c) Edge-linking method. (d) Binarization. (e) Labeling with Grassfire algorithm. (f) Removal of noise using morphologic information. (g) Extraction of anechoic center of the scope. (h) Extraction of outer hyperechoic rim of the scope.
Figure 3
Figure 3
An example of digital image analysis. From the standardized image, a region of interest (ROI) is selected by an experienced endosonographer for tumor analysis. The final results for the ROI are expressed in the bottom histogram. The mean (Tmean) and standard deviation (TSD) of the brightness values are 81.53 and 180.50, respectively.
Figure 4
Figure 4
Receiver operating characteristic (ROC) curve for differentiating gastrointestinal stromal tumor (GIST) from non-GIST mesenchymal tumors. ROC curve of the (A) mean (Tmean) and (B) standard deviation (TSD) of the brightness values that differentiate GIST from non-GIST mesenchymal tumors in the stomach.
See this image and copyright information in PMC

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