br In conclusion we constructed a CNN CAD system
In conclusion, we constructed a CNN-CAD system to determine the invasion depth of gastric cancer with high accuracy and specificity. This system distinguished EGC from tumors with deeper SM invasion and minimized over-estimation of invasion depth, which could reduce unneces-sary gastrectomy.
1. Isomoto H, Shikuwa S, Yamaguchi N, et al. Endoscopic submucosal dissection for early gastric cancer: a large-scale feasibility study. Gut 2009;58:331-6.
2. Chung IK, Lee JH, Lee SH, et al. Therapeutic outcomes in 1000 cases of endoscopic submucosal dissection for early gastric neoplasms: Korean ESD Study Group multicenter study. Gastrointest Endosc 2009;69: 1228-35.
3. Ono H, Yao K, Fujishiro M, et al. Guidelines for endoscopic submucosal dissection and endoscopic mucosal resection for early gastric cancer. Dig Endosc 2016;28:3-15.
Zhu et al Applying a CNN-CAD system to determine invasion depth for endoscopic resection
5. Choi J, Kim S, Im J, et al. Comparison of endoscopic ultrasonography and conventional endoscopy for prediction of depth of tumor invasion in early gastric cancer. Endoscopy 2010;42:705-13.
7. Philpotts LE. Can computer-aided detection be detrimental to mammographic interpretation? Radiology 2009;253:17-22.
8. Hornbrook MC, Goshen R, Choman E, et al. Early colorectal cancer de-tected by machine learning model using gender, age, and complete blood count data. Dig Dis Sci 2017;62:2719-27.
9. Gandomkar Z, Brennan PC, Mello-Thoms C. MuDeRN: Multi-category classification of breast histopathological image using deep residual networks. Artif Intell Med 2018;88:14-24.
11. Japanese Gastric Cancer Association. Japanese classification of gastric carcinoma: 3rd English edition. Gastric Cancer 2011;14:101-12. 12. Feng H, Wang Y, Cao L, et al. Lymph node 1462249-75-7 in differentiated-type early gastric cancer: a single-center retrospective analysis of sur-gically resected cases. Scand J Gastroenterol 2016;51:48-54. 13. Vasconcelos CN, Vasconcelos BN. Increasing deep learning melanoma classification by classical and expert knowledge based image trans-forms. CoRR, abs/170207025. 2017;1.
16. Zeiler MD, Fergus R. Visualizing and understanding convolutional net-works. European conference on computer vision, Zurich, Switzerland. Springer; 2014. p. 818-33.
17. Polkowski M, Palucki J, Wronska E, et al. Endosonography versus helical computed tomography for locoregional staging of gastric cancer. Endoscopy 2004;36:617-23.
18. Kim SJ, Choi CW, Kang DH, et al. Factors associated with the efficacy of miniprobe endoscopic ultrasonography after conventional endoscopy for the prediction of invasion depth of early gastric cancer. Scand J Gastroenterol 2017;52:864-9.
19. Yamamoto S, Nishida T, Kato M, et al. Evaluation of endoscopic ultra-sound image quality is Nontranscribed spacer necessary in endosonographic assessment of
20. Savides T. A blind comparison of the effectiveness of endoscopic ultra-sonography and endoscopy in staging early gastric cancer. Gastroint-est Endosc 2000;44:635-6.
21. Choi J, Kim SG, Im JP, et al. Comparison of endoscopic ultrasonography and conventional endoscopy for prediction of depth of tumor invasion in early gastric cancer. Endoscopy 2010;42:705.
22. Tsujii Y, Kato M, Inoue T, et al. Integrated diagnostic strategy for the invasion depth of early gastric cancer by conventional endoscopy and EUS. Gastrointest Endosc 2015;82:452-9.
24. Cheng J, Wu X, Yang A, et al. Model to identify early-stage gastric cancers with deep invasion of submucosa based on endoscopy and endoscopic ultrasonography findings. Surg Endosc 2018;32: 855-63.
25. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint, arXiv:14091556. 2014.
26. Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. J Machine Learn Res 2010;9:249-56.
27. He K, Zhang X, Ren S, et al. Deep residual learning for image recogni-tion. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. p. 770-8.
29. Hasuike N, Ono H, Boku N, et al. A non-randomized confirmatory trial of an expanded indication for endoscopic submucosal dissection for intestinal-type gastric cancer (cT1a): the Japan Clin-ical Oncology Group study (JCOG0607). Gastric Cancer 2018;21: 114-23.
30. Hu J, Zhao Y, Ren M, et al. The comparison between endoscopic submucosal dissection and surgery in gastric cancer: a systematic review and meta-analysis. Gastroenterol Res Pract 2018;2018: 4378945.
Applying a CNN-CAD system to determine invasion depth for endoscopic resection