A deep neural network improves endoscopic detection of early gastric cancer without blind spots

 

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Abstract

Background Gastric cancer is the third most lethal malignancy worldwide. A novel deep convolution neural network (DCNN) to perform visual tasks has been recently developed. The aim of this study was to build a system using the DCNN to detect early gastric cancer (EGC) without blind spots during esophagogastroduodenoscopy (EGD).

Methods 3170 gastric cancer and 5981 benign images were collected to train the DCNN to detect EGC. A total of 24549 images from different parts of stomach were collected to train the DCNN to monitor blind spots. Class activation maps were developed to automatically cover suspicious cancerous regions. A grid model for the stomach was used to indicate the existence of blind spots in unprocessed EGD videos.

Results The DCNN identified EGC from non-malignancy with an accuracy of 92.5 %, a sensitivity of 94.0 %, a specificity of 91.0 %, a positive predictive value of 91.3 %, and a negative predictive value of 93.8 %, outperforming all levels of endoscopists. In the task of classifying gastric locations into 10 or 26 parts, the DCNN achieved an accuracy of 90 % or 65.9 %, on a par with the performance of experts. In real-time unprocessed EGD videos, the DCNN achieved automated performance for detecting EGC and monitoring blind spots.

Conclusions We developed a system based on a DCNN to accurately detect EGC and recognize gastric locations better than endoscopists, and proactively track suspicious cancerous lesions and monitor blind spots during EGD.

^* Contributed equally to this work

• References

• 1 Torre LA, Bray F, Siegel RL. et al. Global cancer statistics, 2012. CA Cancer J Clin 2015; 65: 87-108
  CrossrefPubMedGoogle Scholar
• 2 Soetikno R, Kaltenbach T, Yeh R. et al. Endoscopic mucosal resection for early cancers of the upper gastrointestinal tract. J Clin Oncol 2005; 23: 4490-4498
  CrossrefPubMedGoogle Scholar
• 3 Laks S, Meyers MO, Kim HJ. Surveillance for gastric cancer. Surg Clin 2017; 97: 317-331
  PubMedGoogle Scholar
• 4 Pasechnikov V, Chukov S, Fedorov E. et al. Gastric cancer: prevention, screening and early diagnosis. World J Gastroenterol 2014; 20: 13842-13862
  CrossrefPubMedGoogle Scholar
• 5 Yalamarthi S, Witherspoon P, McCole D. et al. Missed diagnoses in patients with upper gastrointestinal cancers. Endoscopy 2004; 36: 874-879
  Article in Thieme ConnectPubMedGoogle Scholar
• 6 Rutter MD, Senore C, Bisschops R. et al. The European Society of Gastrointestinal Endoscopy quality improvement initiative: developing performance measures. United European Gastroenterol J 2016; 4: 30-41
  CrossrefPubMedGoogle Scholar
• 7 Yao K, Uedo N, Muto M. et al. Development of an e-learning system for teaching endoscopists how to diagnose early gastric cancer: basic principles for improving early detection. Gastric Cancer 2017; 20: S28-S38
  CrossrefPubMedGoogle Scholar
• 8 Scaffidi MA, Grover SC, Carnahan H. et al. Impact of experience on self-assessment accuracy of clinical colonoscopy competence. Gastrointest Endosc 2018; 87: 827-836.e2
  CrossrefPubMedGoogle Scholar
• 9 Kim GH, Bang SJ, Ende AR. et al. Is screening and surveillance for early detection of gastric cancer needed in Korean Americans?. Korean J Int Med 2015; 30: 747
  PubMedGoogle Scholar
• 10 O'Mahony S, Naylor G, Axon A. Quality assurance in gastrointestinal endoscopy. Endoscopy 2000; 32: 483-488
  Article in Thieme ConnectPubMedGoogle Scholar
• 11 Torkamani A, Andersen KG, Steinhubl SR. et al. High-definition medicine. Cell 2017; 170: 828-843
  CrossrefPubMedGoogle Scholar
• 12 LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521: 436-444
  Google Scholar
• 13 Chen PJ, Lin MC, Lai MJ. et al. Accurate classification of diminutive colorectal polyps using computer-aided analysis. Gastroenterology 2018; 154: 568-575
  CrossrefPubMedGoogle Scholar
• 14 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 2018; DOI: 10.1136/gutjnl-2017-314547.
  CrossrefPubMedGoogle Scholar
• 15 Bisschops R, Areia M, Coron E. et al. Performance measures for upper gastrointestinal endoscopy: a European Society of Gastrointestinal Endoscopy (ESGE) quality improvement initiative. Endoscopy 2016; 48: 843-864
  Article in Thieme ConnectPubMedGoogle Scholar
• 16 Yao K. The endoscopic diagnosis of early gastric cancer. Ann Gastroenterol 2013; 26: 11-22
  PubMedGoogle Scholar
• 17 Russakovsky O, Deng J, Su H. et al. Imagenet large scale visual recognition challenge. Int J Comput Vision 2015; 115: 211-252
  CrossrefPubMedGoogle Scholar
• 18 Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. CoRR arXiv: 1409.1556 https://arxiv.org/abs/1508.06576
  PubMedGoogle Scholar
• 19 He K, Zhang X, Ren S. et al. Deep residual learning for image recognition. Proc IEEE Conf Comput Vision Pattern Recogn 2016; 770-778
  PubMedGoogle Scholar
• 20 Pan SJ, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng 2010; 22: 1345-1359
  CrossrefPubMedGoogle Scholar
• 21 Abadi M, Agarwal A, Barham P. et al. Tensorflow: A system for large-scale machine learning. 12th Symposium on Operating Systems Design and Implementation. 2016 265 – 283 https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf
  PubMedGoogle Scholar
• 22 Tanner MA, Wong WH. The calculation of posterior distributions by data augmentation. J Am Stat Assoc 1987; 82: 528-540
  CrossrefPubMedGoogle Scholar
• 23 Wen Z, Li B, Ramamohanarao K. et al. Improving efficiency of SVM k-fold cross-validation by alpha seeding. AAAI 2017; 2768-2774
  PubMedGoogle Scholar
• 24 Prechelt L. Automatic early stopping using cross validation: quantifying the criteria. Neural Netw 1998; 11: 761-767
  CrossrefPubMedGoogle Scholar
• 25 Li S, Liu G, Tang X. et al. An ensemble deep convolutional neural network model with improved DS evidence fusion for bearing fault diagnosis. Sensors 2017; 17: 1729
  CrossrefPubMedGoogle Scholar
• 26 Jung Y, Hu J. AK-fold averaging cross-validation procedure. J Nonparametr Stat 2015; 27: 167-179
  CrossrefPubMedGoogle Scholar
• 27 Zhou B, Khosla A, Lapedriza A. et al. Learning deep features for discriminative localization. Proc IEEE Conf Comput Vision Pattern Recogn 2016; 2921-2929
  PubMedGoogle Scholar
• 28 O'Hailey T. Hybrid animation: integrating 2D and 3D assets. Abingdon: OXON: Taylor and Francis; 2010
  Google Scholar
• 29 Liaw A, Wiener M. Classification and regression by randomForest. R news 2002; 2: 18-22
  PubMedGoogle Scholar
• 30 Hirasawa T, Aoyama K, Tanimoto T. et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer 2018; 21: 653-660
  CrossrefPubMedGoogle Scholar
• 31 Teh JL, Hartman M, Lau L. et al. Mo1579 duration of endoscopic examination significantly impacts detection rates of neoplastic lesions during diagnostic upper endoscopy. Gastrointest Endosc 2011; 73: AB393
  PubMedGoogle Scholar
• 32 Zhang Q, Wang F, Chen ZY. et al. Comparison of the diagnostic efficacy of white light endoscopy and magnifying endoscopy with narrow band imaging for early gastric cancer: a meta-analysis. Gastric Cancer 2016; 19: 543-552
  CrossrefPubMedGoogle Scholar
• 33 Ezoe Y, Muto M, Uedo N. et al. Magnifying narrowband imaging is more accurate than conventional white-light imaging in diagnosis of gastric mucosal cancer. Gastroenterology 2011; 141: 2017-2025
  CrossrefPubMedGoogle Scholar
• 34 Song M, Ang TL. Early detection of early gastric cancer using image-enhanced endoscopy: Current trends. Gastrointest Intervent 2014; 3: 1-7
  PubMedGoogle Scholar
• 35 Tsai TH, Leggett CL, Trindade AJ. et al. Optical coherence tomography in gastroenterology: a review and future outlook. J Biomed Opt 2017; 22: 1-17
  CrossrefPubMedGoogle Scholar

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