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Purpose <p>Deep learning may be helpful to differentiate gastrointestinal stromal tumors and risk stratification using endoscopic images.</p> Methods <p>A total of 494 patients with gastrointestinal stromal tumors and 1010 patients with gastric cancer from author’s hospital were trained and validated. Another 99 patients with gastrointestinal stromal tumors and 100 patients with gastric cancer from second hospital were enrolled as external validation. Two deep learning networks, Swin Transformer and ConvNeXt were adapted for the differentiation, tumor size, mitotic index and risk stratification prediction with image level, patient level and combined level.</p> Results <p>In the internal validation dataset, ConvNeXt achieved area under curve of 0.985 in the differentiation gastrointestinal stromal tumors from gastric cancer. Swin Transformer achieved a best area under curve of 0.927, 0.806 and 0.765 in the prediction of tumor size, mitotic index and risk stratification for gastrointestinal stromal tumors, respectively. In the external validation dataset, ConvNeXt achieved a best area under curve of 0.999 in the differentiation gastrointestinal stromal tumors from gastric cancer. Swin Transformer achieved a best area under curve of 0.872, 0.856 and 0.693 in the prediction of tumor size, mitotic index and risk stratification for gastrointestinal stromal tumors.</p> Conclusions <p>Endoscopic images-based deep learning models demonstrated excellent performance in the differentiation gastrointestinal stromal tumors from gastric cancer, and in the prediction the tumor size, mitotic index and risk stratification of gastrointestinal stromal tumors. They are promising in the differentiation and risk stratification to improve the management of gastrointestinal stromal tumors.</p>

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Differentiation and risk stratification for gastrointestinal stromal tumors with endoscopic images using deep learning

  • 期刊论文

  • 发表日期：

    2026年7月2日

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  • Qiao Zheng,
  • Wenhao Guo,
  • Sunjun Li,
  • Long Zhang,
  • Jiayue Sun,
  • Jiaye Cui,
  • Xiance Jin,
  • Zhen Sun

摘要

Purpose

Deep learning may be helpful to differentiate gastrointestinal stromal tumors and risk stratification using endoscopic images.

Methods

A total of 494 patients with gastrointestinal stromal tumors and 1010 patients with gastric cancer from author’s hospital were trained and validated. Another 99 patients with gastrointestinal stromal tumors and 100 patients with gastric cancer from second hospital were enrolled as external validation. Two deep learning networks, Swin Transformer and ConvNeXt were adapted for the differentiation, tumor size, mitotic index and risk stratification prediction with image level, patient level and combined level.

Results

In the internal validation dataset, ConvNeXt achieved area under curve of 0.985 in the differentiation gastrointestinal stromal tumors from gastric cancer. Swin Transformer achieved a best area under curve of 0.927, 0.806 and 0.765 in the prediction of tumor size, mitotic index and risk stratification for gastrointestinal stromal tumors, respectively. In the external validation dataset, ConvNeXt achieved a best area under curve of 0.999 in the differentiation gastrointestinal stromal tumors from gastric cancer. Swin Transformer achieved a best area under curve of 0.872, 0.856 and 0.693 in the prediction of tumor size, mitotic index and risk stratification for gastrointestinal stromal tumors.

Conclusions

Endoscopic images-based deep learning models demonstrated excellent performance in the differentiation gastrointestinal stromal tumors from gastric cancer, and in the prediction the tumor size, mitotic index and risk stratification of gastrointestinal stromal tumors. They are promising in the differentiation and risk stratification to improve the management of gastrointestinal stromal tumors.