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<p>Subepithelial tumors (SET) of the upper gastrointestinal (GI) tract are rare incidental findings, usually benign but with potential malignant degeneration, especially gastrointestinal stromal tumors (GIST) and neuroendocrine neoplasms (NEN). Smaller tumors (&lt; 2 cm) are usually asymptomatic but larger ones can become symptomatic due to ulceration, bleeding or obstruction. Endoscopic imaging and endosonography (endoscopic ultrasound, EUS) provide only limited diagnostic certainty and also a biopsy does not always result in a&#xa0;certain diagnosis. The aim of this review is to present the current diagnostic and therapeutic strategies for SET of the upper GI tract, with particular emphasis on endoscopic resection procedures as an alternative to surgical treatment. The decision between surveillance and intervention should be made individually based on tumor characteristics, imaging and patient preference. A standardized risk stratification using algorithmic classification supports the clinical decision-making.</p>

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Subepitheliale Tumoren des oberen Gastrointestinaltrakts

  • 期刊论文

  • 发表日期：

    2025年11月18日

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  • Selina Günther,
  • Florian Alexander Michael,
  • Mireen Friedrich-Rust

摘要

Subepithelial tumors (SET) of the upper gastrointestinal (GI) tract are rare incidental findings, usually benign but with potential malignant degeneration, especially gastrointestinal stromal tumors (GIST) and neuroendocrine neoplasms (NEN). Smaller tumors (< 2 cm) are usually asymptomatic but larger ones can become symptomatic due to ulceration, bleeding or obstruction. Endoscopic imaging and endosonography (endoscopic ultrasound, EUS) provide only limited diagnostic certainty and also a biopsy does not always result in a certain diagnosis. The aim of this review is to present the current diagnostic and therapeutic strategies for SET of the upper GI tract, with particular emphasis on endoscopic resection procedures as an alternative to surgical treatment. The decision between surveillance and intervention should be made individually based on tumor characteristics, imaging and patient preference. A standardized risk stratification using algorithmic classification supports the clinical decision-making.