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Efficacy of endoscopic ultrasound with artificial intelligence for the diagnosis of gastrointestinal stromal tumors

  • Original Article―Alimentary Tract
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Abstract

Background

Although endoscopic ultrasound (EUS) is reported to be suitable for determining the layer from which subepithelial lesions (SELs) originate, it is difficult to distinguish gastrointestinal stromal tumor (GIST) from non-GIST using only EUS images. If artificial intelligence (AI) can be used for the diagnosis of SELs, it should provide several benefits, including objectivity, simplicity, and quickness. In this pilot study, we propose an AI diagnostic system for SELs and evaluate its efficacy.

Methods

Thirty sets each of EUS images with SELs ≥ 20 mm or < 20 mm were prepared for diagnosis by an EUS diagnostic system with AI (EUS-AI) and three EUS experts. The EUS-AI and EUS experts diagnosed the SELs using solely the EUS images. The concordance rates of the EUS-AI and EUS experts’ diagnoses were compared with the pathological findings of the SELs.

Results

The accuracy, sensitivity, and specificity for SELs < 20 mm were 86.3, 86.3, and 62.5%, respectively for the EUS-AI, and 73.3, 68.2, and 87.5%, respectively, for the EUS experts. In contrast, accuracy, sensitivity, and specificity for SELs ≥ 20 mm were 90.0, 91.7, and 83.3%, respectively, for the EUS-AI, and 53.3, 50.0, and 83.3%, respectively, for the EUS experts. The area under the curve for the diagnostic yield of the EUS-AI for SELs ≥ 20 mm (0.965) was significantly higher than that (0.684) of the EUS experts (P = 0.007).

Conclusion

EUS-AI had a good diagnostic yield for SELs ≥ 20 mm. EUS-AI has potential as a good option for the diagnosis of SELs.

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Acknowledgements

We thank Dr. Junji Kishimoto in the Kyushu University Center for Clinical and Translational Research for assisting with the statistical analysis.

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Authors

Contributions

YM designed the study, analyzed the data, and wrote the manuscript. HO edited the manuscript. EI collected the data and critically reviewed the manuscript. The other authors contributed to collection of the data. All authors have read and approved the manuscript.

Corresponding author

Correspondence to Eikichi Ihara.

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Conflict of interest

E. Ihara received lecture fees from Takeda Pharmaceutical Co., Ltd., and belongs to an endowed course supported by companies including Ono Pharmaceutical Co., Ltd., Miyarisan Pharmaceutical Co., Ltd., Sanwa Kagaku Kenkyusho Co., Ltd., Otsuka Pharmaceutical Co., Ltd., Fujifilm Medical Co., Ltd., Terumo Corporation, Fancl Corporation, and Ohga Pharmacy. The authors declare no other conflicts of interest in association with this study.

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535_2020_1725_MOESM1_ESM.tif

Supplementary Figure 1. A pilot study to determine the optimal number of SEL images for the main analysis (Pilot study). The results of the pilot study to determine the optimal number of EUS images required to reliably determine the examinees’ diagnostic ability for the evaluation of SELs are shown. Five EUS experts who did not participate in the main analysis were asked to diagnose the SELs. They were shown five images in one session, and the sessions were repeated eight times using a different set of five images each, resulting in analysis of case numbers ranging from 5 to 40. (TIFF 2234 kb)

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Minoda, Y., Ihara, E., Komori, K. et al. Efficacy of endoscopic ultrasound with artificial intelligence for the diagnosis of gastrointestinal stromal tumors. J Gastroenterol 55, 1119–1126 (2020). https://doi.org/10.1007/s00535-020-01725-4

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  • DOI: https://doi.org/10.1007/s00535-020-01725-4

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