Diagnostic Modalities of Endoscopic Ultrasound without Biopsy: History
Please note this is an old version of this entry, which may differ significantly from the current revision.

Gastric subepithelial lesions (SELs) are intramural lesions that arise underneath the gastric mucosa. SELs can be benign, but can also be malignant or have malignant potential. Their correct identification is of vital importance for a successful management. Due to their location, standard luminal endoscopy cannot determine the exact nature of these lesions. Therefore, endosonography (EUS) alone or EUS with fine needle aspiration (FNA) or fine needle biopsy (FNB) have been established as the next step in the diagnostic algorithm.

  • gastric subepithelial lesions
  • endoscopic ultrasound
  • EUS elastography

1. Introduction

Gastric subepithelial lesions (SELs) are intramural lesions that arise from the layers underneath the gastric mucosa (muscularis mucosae, submucosa, muscularis propria, and rarely from the serosa). They are discovered mainly during upper GI endoscopy performed for other indications with an incidence of 0.36% of upper GI endoscopies, as showed in 1991 [1]. To our knowledge, no study assessing their incidence has been published since then.
SELs are mostly incidental findings, as most of them are asymptomatic. Bigger lesions, however, can cause dysphagia, overt or occult gastrointestinal (GI) bleeding, and chronic anemia [2]. SELs may be nonneoplastic, neoplastic but benign, neoplastic with malignant potential, or malignant [2]. Only 15% of the SELs are malignant at the time of diagnosis [3]. Therefore, their correct identification is of vital importance for a successful management. Additionally, these lesions need to be differentiated from masses that cause extrinsic compression of the gastric wall and from epithelial lesions that mimic SEL.
Due to their location, standard luminal endoscopy cannot determine the exact nature of these lesions. Therefore, endosonography (EUS) alone or EUS with fine needle aspiration (FNA) or fine needle biopsy (FNB) have been established as the next step in the diagnostic algorithm [2][4]. Although these methods have a high level of accuracy, sensitivity, and specificity for lesions > 2 cm [5][6], the correct diagnosis of SELs, especially those of ≤2 cm, still poses a challenge.

2. New Diagnostic Modalities of EUS without Biopsy

2.1. Contrast-Enhanced EUS

In recent years, the utilization of contrast-enhanced EUS (CH-EUS) in the differential diagnosis of SELs has been gathering attention. With the use of contrast agents that contain microbubbles (SonoVue Bracco SpA., Milan, Italy or Sonazoid Daiichi-Sankyo, Tokyo, Japan), CH-EUS is able to provide a detailed view in the microvascularization and the perfusion of the lesions. The examined lesion can be characterized based on the enhancement level. Some studies revealed that CE-EUS is useful for distinguishing between GISTs and benign SELs. Hyperenhancement is suggestive of GISTs with 78–100% sensitivity, 60–100% specificity, and 60–100% accuracy, whereas hypoenhancement is associated with leiomyomas [7][8][9][10]. In addition, a meta-analysis showed that CE-EUS discriminated GISTs from benign SELs with pooled sensitivity and specificity 89% (95% CI 0.82–0.93) and 82% (95% CI 0.66–0.92), respectively [11]. CE-EUS can also be utilized as a method for the estimation of the malignant potential of GISTs. Findings on CE-EUS that are related to high malignant risk include irregular vessels, heterogeneous perfusion pattern, and the presence of nonenhancing spots [12][13][14]. In a meta-analysis of five studies, the pooled sensitivity and specificity of CE-EUS in predicting the malignant risk of GISTs were 96% (95% CI 90–99%) and 53% (95% CI 40–66%), respectively [11]. Consequently, CE-EUS provides useful insights for the nature of the lesion and could be used as an additional, less invasive diagnostic tool to EUS. Thus, lesions that need further evaluation with histological examination can be determined. If a GIST is diagnosed, CE-EUS is also useful for predicting its malignant potential.

2.2. EUS-Elastography

Elastography assesses the stiffness of a certain lesion, which is then reflected as a color spectrum; blue color represents hard lesions, while red color represents soft ones [15]. Elastography is a common procedure for the diagnosis of hepatic, thyroid, and lymph nodes diseases and was recently used as a supplementary tool to EUS for the differentiation of pancreatic lesions and SELs [15]. EUS elastography (EUS-E) can be performed in real time using a conventional EUS probe attached to a processor with specific software installed [16].
In the first pilot study on the effectiveness of EUS-E in the differential diagnosis of gastric SELs, GISTs were harder than other SELs with regard to quality by measuring the amount of stiffness regarding the majority and the distribution of the color [17]. In another study using a strain ratio as an objective marker οf stiffness, EUS-E distinguished GIST from leiomyoma with sensitivity and specificity of 100% and 94.1%, respectively. However, the distinction of GIST from schwannoma was difficult [18]. On the contrary, Guo et al. reported that the utilization of EUS-E in the discrimination of GISTs from leiomyomas could not be supported based on current evidence [19].

2.3. Artificial Intelligence in Endoscopic Ultrasound

In recent years, the use of artificial intelligence (AI) has increased in endoscopy. Starting with colonic polyp detection, AI systems now are being tested to determine if they can increase diagnostic accuracy of several endoscopic procedures [20]. So far, 22 articles have been published on the use of AI in endosonography; six deal with gastric SELs. Minoda et al. demonstrated that AI has a better accuracy, sensitivity, and specificity in recognizing GIST tumors from non-GIST lesions independent of the lesion size in comparison to senior endoscopists. In comparison to histology, the AI system had an accuracy, sensitivity, and specificity of 86.3, 86.3, and 62.5%, respectively, for lesions < 2 cm, and 90.0, 91.7, and 83.3% for lesions > 2 cm [21]. These results are comparable with the results of FNB. Therefore, the authors of this study concluded that EUS-AI can be an alternative to tissue sampling when differentiating GIST lesions from non-GIST. Hirai et al. and Kim et al. found similar results [22][23]. Seven at al. demonstrated that AI has a high accuracy in predicting the malignant potential of GIST tumors (low vs. high risk) up to 99% [24]. Additionally, Kim et al. were able to differentiate in non-GIST lesions leiomyomas from schwannomas in 75% [23]. Consequently, AI systems seem to be a promising additional tool when differentiating gastric SELs, but more studies are required for its validation.

2.4. Critical Appraisal of the Evidence

So far, these new diagnostic advances in EUS (CE-EUS, EUS-E, and EUS-AI) are at a research level and are not broadly clinically available. Currently, the European guidelines suggest to use CE-EUS for lesion characterization and determination of its malignant potential, if available. Nevertheless, tissue acquisition is still necessary [4]. As far as EUS-E is concerned, contradictory results have been published so far. Therefore, at the moment, there are not enough data yet to recommend or to reject this method for the diagnosis of gastric SELs [4]. Similarly, more studies are needed for the use of AI in EUS-guided diagnosis of gastric SELs. Interestingly, the American guidelines did not make any reference to the first two methods in their latest version, apart from a short reference to the use of AI [2].

This entry is adapted from the peer-reviewed paper 10.3390/diagnostics13132176


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