Deep learning analysis for differential diagnosis and risk classification of gastrointestinal tumors

Abstract

Objectives

Recently, artificial intelligence (AI) has been applied to clinical diagnosis. Although AI has already been developed for gastrointestinal (GI) tract endoscopy, few studies have applied AI to endoscopic ultrasound (EUS) images. In this study, we used a computer-assisted diagnosis (CAD) system with deep learning analysis of EUS images (EUS-CAD) and assessed its ability to differentiate GI stromal tumors (GISTs) from other mesenchymal tumors and their risk classification performance.

Materials and methods

A total of 101 pathologically confirmed cases of subepithelial lesions (SELs) arising from the muscularis propria layer, including 69 GISTs, 17 leiomyomas and 15 schwannomas, were examined. A total of 3283 EUS images were used for training and five-fold-cross-validation, and 827 images were independently tested for diagnosing GISTs. For the risk classification of 69 GISTs, including very-low-, low-, intermediate- and high-risk GISTs, 2,784 EUS images were used for training and three-fold-cross-validation.

Results

For the differential diagnostic performance of GIST among all SELs, the accuracy, sensitivity, specificity and area under the receiver operating characteristic (ROC) curve were 80.4%, 82.9%, 75.3% and 0.865, respectively, whereas those for intermediate- and high-risk GISTs were 71.8%, 70.2%, 72.0% and 0.771, respectively.

Conclusions

The EUS-CAD system showed a good diagnostic yield in differentiating GISTs from other mesenchymal tumors and successfully demonstrated the GIST risk classification feasibility. This system can determine whether treatment is necessary based on EUS imaging alone without the need for additional invasive examinations.

Keywords:

• Subepithelial lesions (SELs)
• gastrointestinal stromal tumors (GISTs)
• artificial intelligence (AI)
• deep learning
• computer-aided diagnosis (CAD)

Acknowledgments

We would like to thank Junichi Ichikawa, Hironaka Mioka, Fumiyuki Shiratani, and Jun Ando (Olympus Medical Systems Corporation) for their support with deep-learning analysis. We would also like to thank Editage (www.editage.com) for the English language editing.

Author contributions

All authors critically revised the manuscript, commented on its drafts, and approved the final report.

Disclosure statement

The authors declare no conflicts of interest.

Data availability statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This research was conducted in collaboration with the Department of Gastroenterology, Kitasato University Hospital, and Olympus Medical System Corp. (Tokyo, Japan) based on a contract. No funding support was received from Olympus Medical Systems Corp., nor did it influence the study design or interpretation of results.