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Endoscopy 2022; 54(03): 251-261
DOI: 10.1055/a-1476-8931
Original article

An artificial intelligence system for distinguishing between gastrointestinal stromal tumors and leiomyomas using endoscopic ultrasonography

Xintian Yang  ‡   *
1   Department of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
2   Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
,
Han Wang  ‡   *
3   Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
,
Qian Dong
1   Department of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
2   Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
,
Yonghong Xu
4   Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China
,
Hua Liu
4   Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China
,
Xiaoying Ma
5   Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao, China
,
Jing Yan
4   Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China
,
Qian Li
4   Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China
,
Chenyu Yang
1   Department of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
2   Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
,
Xiaoyu Li
4   Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China
› Author Affiliations“Clinical medicine + X” scientific research project of Affiliated Hospital of Qingdao University http://dx.doi.org/10.13039/501100001809

Trial Registration: Chinese Clinical Trial Registry (http://www.chictr.org/) Registration number (trial ID): ChiCTR2000039322 Type of study: Prospective diagnostic test
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Abstract

Background Gastrointestinal stromal tumors (GISTs) and gastrointestinal leiomyomas (GILs) are the most common subepithelial lesions (SELs). All GISTs have malignant potential; however, GILs are considered benign. Current imaging cannot effectively distinguish GISTs from GILs. We aimed to develop an artificial intelligence (AI) system to differentiate these tumors using endoscopic ultrasonography (EUS).

Methods The AI system was based on EUS images of patients with histologically confirmed GISTs or GILs. Participants from four centers were collected to develop and retrospectively evaluate the AI-based system. The system was used when endosonographers considered SELs to be GISTs or GILs. It was then used in a multicenter prospective diagnostic test to clinically explore whether joint diagnoses by endosonographers and the AI system can distinguish between GISTs and GILs to improve the total diagnostic accuracy for SELs.

Results The AI system was developed using 10 439 EUS images from 752 participants with GISTs or GILs. In the prospective test, 132 participants were histologically diagnosed (36 GISTs, 44 GILs, and 52 other types of SELs) among 508 consecutive subjects. Through joint diagnoses, the total accuracy of endosonographers in diagnosing the 132 histologically confirmed participants increased from 69.7 % (95 % confidence interval [CI] 61.4 %–76.9 %) to 78.8 % (95 %CI 71.0 %–84.9 %; P = 0.01). The accuracy of endosonographers in diagnosing the 80 participants with GISTs or GILs increased from 73.8 % (95 %CI 63.1 %–82.2 %) to 88.8 % (95 %CI 79.8 %–94.2 %; P = 0.01).

Conclusions We developed an AI-based EUS diagnostic system that can effectively distinguish GISTs from GILs and improve the diagnostic accuracy of SELs.

* Joint first authors


Supplementary material



Publication History

Received: 12 October 2020

Accepted: 07 April 2021

Accepted Manuscript online:
07 April 2021

Article published online:
09 June 2021

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Faulx AL, Kothari S. Standards of Practice C. et al. The role of endoscopy in subepithelial lesions of the GI tract. Gastrointest Endosc 2017; 85: 1117-1132
    CrossrefPubMedGoogle Scholar
  • 2 Miettinen M, Lasota J. Gastrointestinal stromal tumors: Pathology and prognosis at different sites. Semin Diagn Pathol 2006; 23: 70-83
    CrossrefPubMedGoogle Scholar
  • 3 Chandrasekhara V, Ginsberg GG. Endoscopic management of gastrointestinal stromal tumors. Curr Gastroenterol Rep 2011; 13: 532-539
    CrossrefPubMedGoogle Scholar
  • 4 Nishida T, Blay JY, Hirota S. et al. The standard diagnosis, treatment, and follow-up of gastrointestinal stromal tumors based on guidelines. Gastric Cancer 2016; 19: 3-14
    CrossrefPubMedGoogle Scholar
  • 5 Zhou P, Zhong Y, Li Q. Chinese consensus on endoscopic diagnosis and management of gastrointestinal submucosal tumor (version 2018) [In Chinese]. Zhonghua Wei Chang Wai Ke Za Zhi 2018; 21: 841-852
    PubMedGoogle Scholar
  • 6 Nishida T, Kawai N, Yamaguchi S. et al. Submucosal tumors: comprehensive guide for the diagnosis and therapy of gastrointestinal submucosal tumors. Dig Endosc 2013; 25: 479-489
    CrossrefPubMedGoogle Scholar
  • 7 Landi B, Palazzo L. The role of endosonography in submucosal tumours. Best Pract Res Clin Gastroenterol 2009; 23: 679-701
    CrossrefPubMedGoogle Scholar
  • 8 Dumonceau JM, Deprez PH, Jenssen C. et al. Indications, results, and clinical impact of endoscopic ultrasound (EUS)-guided sampling in gastroenterology: European Society of Gastrointestinal Endoscopy (ESGE) Clinical Guideline – Updated January 2017. Endoscopy 2017; 49: 695-714
    Article in Thieme ConnectPubMedGoogle Scholar
  • 9 Luo H, Xu G, Li C. et al. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study. Lancet Oncol 2019; 20: 1645-1654
    CrossrefPubMedGoogle Scholar
  • 10 Byrne MF, Chapados N, Soudan F. et al. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut 2019; 68: 94-100
    CrossrefPubMedGoogle Scholar
  • 11 Kather JN, Pearson AT, Halama N. et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med 2019; 25: 1054-1056
    CrossrefPubMedGoogle Scholar
  • 12 Bi WL, Hosny A, Schabath MB. et al. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin 2019; 69: 127-157
    CrossrefPubMedGoogle Scholar
  • 13 Song Z, Zou S, Zhou W. et al. Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning. Nat Commun 2020; 11: 4294
    CrossrefPubMedGoogle Scholar
  • 14 Ikenoyama Y, Yoshio T, Tokura J. et al. Artificial intelligence diagnostic system predicts multiple Lugol-voiding lesions in the esophagus and patients at high risk for esophageal squamous cell carcinoma. Endoscopy 2021; DOI: 10.1055/a-1334-4053.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 15 Wu L, He X, Liu M. et al. Evaluating the effects of an artificial intelligence system on endoscopy quality and preliminarily testing its performance on detecting early gastric cancer: a randomized controlled trial. Endoscopy 2021; DOI: 10.1055/a-1350-5583.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 16 Trister AD, Buist DSM, Lee CI. Will machine learning tip the balance in breast cancer screening?. JAMA Oncol 2017; 3: 1463-1464
    CrossrefPubMedGoogle Scholar
  • 17 Ding Z, Shi H, Zhang H. et al. Gastroenterologist-level identification of small-bowel diseases and normal variants by capsule endoscopy using a deep-learning model. Gastroenterology 2019; 157: 1044-1054
    CrossrefPubMedGoogle Scholar
  • 18 Nagtegaal ID, Odze RD, Klimstra D. et al. The 2019 WHO classification of tumours of the digestive system. Histopathology 2020; 76: 182-188
    CrossrefPubMedGoogle Scholar
  • 19 He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision & Pattern Recognition; 2016: 770-778
    PubMedGoogle Scholar
  • 20 Hajian-Tilaki K. Sample size estimation in diagnostic test studies of biomedical informatics. J Biomed Inform 2014; 48: 193-204
    CrossrefPubMedGoogle Scholar
  • 21 Leisenring W, Alonzo T, Pepe MS. Comparisons of predictive values of binary medical diagnostic tests for paired designs. Biometrics 2000; 56: 345-351
    CrossrefPubMedGoogle Scholar
  • 22 Hunt GC, Smith PP, Faigel DO. Yield of tissue sampling for submucosal lesions evaluated by EUS. Gastrointest Endosc 2003; 57: 68-72
    CrossrefPubMedGoogle Scholar
  • 23 Menon L, Buscaglia JM. Endoscopic approach to subepithelial lesions. Therap Adv Gastroenterol 2014; 7: 123-130
    CrossrefPubMedGoogle Scholar
  • 24 Antonini F, Delconte G, Fuccio L. et al. EUS-guided tissue sampling with a 20-gauge core biopsy needle for the characterization of gastrointestinal subepithelial lesions: A multicenter study. Endosc Ultrasound 2019; 8: 105-110
    CrossrefPubMedGoogle Scholar
  • 25 Akahoshi K, Oya M, Koga T. et al. Clinical usefulness of endoscopic ultrasound-guided fine needle aspiration for gastric subepithelial lesions smaller than 2 cm. J Gastrointest Liver Dis 2014; 23: 405-412
    CrossrefPubMedGoogle Scholar
  • 26 Nagpal K, Foote D, Tan F. et al. Development and validation of a deep learning algorithm for Gleason grading of prostate cancer from biopsy specimens. JAMA Oncol 2020; 6: 1372-1380
    CrossrefPubMedGoogle Scholar
  • 27 Ling T, Wu L, Fu Y. et al. A deep learning-based system for identifying differentiation status and delineating margins of early gastric cancer in magnifying narrow-band imaging endoscopy. Endoscopy 2020; DOI: 10.1055/a-1229–0920.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 28 Gong D, Wu L, Zhang J. et al. Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. Lancet Gastroenterol Hepatol 2020; 5: 352-361
    CrossrefPubMedGoogle Scholar
  • 29 O'Sullivan JW, Banerjee A, Heneghan C. et al. Verification bias. BMJ Evid Based Med 2018; 23: 54-55
    CrossrefPubMedGoogle Scholar
  • 30 Schmidt RL, Jedrzkiewicz JD, Allred RJ. et al. Verification bias in diagnostic accuracy studies for fine- and core needle biopsy of salivary gland lesions in otolaryngology journals: a systematic review and analysis. Head Neck 2014; 36: 1654-1661
    CrossrefPubMedGoogle Scholar