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Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of artificial intelligence: a pilot study
Background The accurate differentiation between T1a and T1b Barrett’s-related cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an artificial intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett’s cancer on white-light images.
Methods Endoscopic images from three tertiary care centers in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) were evaluated using the AI system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett’s cancer.
Results The sensitivity, specificity, F1 score, and accuracy of the AI system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.74, and 0.71, respectively. There was no statistically significant difference between the performance of the AI system and that of experts, who showed sensitivity, specificity, F1, and accuracy of 0.63, 0.78, 0.67, and 0.70, respectively.
Conclusion This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett’s cancer. AI scored equally to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and real-life settings. Nevertheless, the correct prediction of submucosal invasion in Barrett’s cancer remains challenging for both experts and AI.
* These authors contributed equally to this work.
Received: 01 June 2020
Accepted: 16 November 2020
16 November 2020 (online)
© 2020. Thieme. All rights reserved.
Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany
- 1 Coleman HG, Xie SH, Lagergren J. The epidemiology of esophageal adenocarcinoma. Gastroenterology 2018; 154: 390-405
- 2 Drahos J, Ricker W, Parsons R. et al. Metabolic syndrome increases risk of Barrett esophagus in the absence of gastroesophageal reflux: an analysis of SEER-Medicare Data. J Clin Gastroenterol 2015; 49: 282-288
- 3 Sharma P, Bergman JJ, Goda K. et al. Development and validation of a classification system to identify high-grade dysplasia and esophageal adenocarcinoma in Barrett’s esophagus using narrow-band imaging. Gastroenterology 2016; 150: 591-598
- 4 de Groof AJ, Struyvenberg MR, Fockens KN. et al. Deep learning algorithm detection of Barrett’s neoplasia with high accuracy during live endoscopic procedures: a pilot study (with video). Gastrointest Endosc 2020; 91: 1242-1250
- 5 de Groof AJ, Struyvenberg MR, van der Putten J. et al. Deep-learning system detects neoplasia in patients with Barrett’s esophagus with higher accuracy than endoscopists in a multistep training and validation study with benchmarking. Gastroenterology 2020; 158: 915-929
- 6 Hashimoto R, Requa J, Tyler D. et al. Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett’s esophagus (with video). Gastrointest Endosc 2020; 91: 1264-1271
- 7 Ebigbo A, Mendel R, Probst A. et al. Real-time use of artificial intelligence in the evaluation of cancer in Barrett’s oesophagus. Gut 2020; 69: 615-616
- 8 Ebigbo A, Mendel R, Probst A. et al. Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma. Gut 2019; 68: 1143-1145
- 9 Weusten B, Bisschops R, Coron E. et al. Endoscopic management of Barrett’s esophagus: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy 2017; 49: 191-198
- 10 Pimentel-Nunes P, Dinis-Ribeiro M, Ponchon T. et al. Endoscopic submucosal dissection: European Society of Gastrointestinal Endoscopy (ESGE) Guideline. Endoscopy 2015; 47: 829-854
- 11 He K, Zhang X, Ren S. et al. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016; 770-778
- 12 Russakovsky O, Deng J, Su H. et al. ImageNet large scale visual recognition challenge. Int J Comput Vis 2015; 115: 211-252
- 13 Kingma DP, Ba J. Adam: a method for stochastic optimization. Presented at the International Conference on Learning Representations; 2015 May 7–9; San Diego, California. arXiv preprint arXiv:14126980 2014.
- 14 Liu W, Rabinovich A, Berg AC. Parsenet: looking wider to see better. Presented at the International Conference on Learning Representations; 2016 May 2–4, 2016; San Juan, Puerto Rico. arXiv preprint arXiv:150604579 2015.
- 15 Forman G, Scholz M. Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement. ACM SIGKDD Explorations Newsletter 2010; 12: 49-57
- 16 Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977; 33: 159-174
- 17 Thosani N, Singh H, Kapadia A. et al. Diagnostic accuracy of EUS in differentiating mucosal versus submucosal invasion of superficial esophageal cancers: a systematic review and meta-analysis. Gastrointest Endosc 2012; 75: 242-253
- 18 Qumseya BJ, Brown J, Abraham M. et al. Diagnostic performance of EUS in predicting advanced cancer among patients with Barrett’s esophagus and high-grade dysplasia/early adenocarcinoma: systematic review and meta-analysis. Gastrointest Endosc 2015; 81: 865-874
- 19 Zhu Y, Wang QC, Xu MD. et al. Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy. Gastrointest Endosc 2019; 89: 806-815
- 20 Lui TKL, Wong KKY, Mak LLY. et al. Endoscopic prediction of deeply submucosal invasive carcinoma with use of artificial intelligence. Endosc Int Open 2019; 7: E514-e520
- 21 Horie Y, Yoshio T, Aoyama K. et al. Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointest Endosc 2019; 89: 25-32