Objective: This study aimed to use convolutional neural network (CNN), a deep learning software, to assist in cT1b diagnosis. Methods: This retrospective study used 190 colon lesion images from 41 cases of colon endoscopies performed between February 2015 and October 2016. Unenhanced colon endoscopy images (520 × 520 pixels) with white light were used. Images included 14 cTis cases with endoscopic resection and 14 cT1a and 13 cT1b cases with surgical resection. Protruding, flat, and recessed lesions were analyzed. AlexNet and Caffe were used for machine learning. Fine tuning of data to increase image numbers was performed. Oversampling for the training images was conducted to avoid impartiality in image numbers, and learning was carried out. The 3-fold cross-validation method was used. Sensitivity, specificity, accuracy, and area under the curve (AUC) values in the receiver operating characteristic curve were calculated for each group. Results: The results were the average of obtained values. With CNN learning, cT1b sensitivity, specificity, and accuracy were 67.5, 89.0, and 81.2%, respectively, and AUC was 0.871. Conclusion: Quantitative diagnosis is possible using an endoscopic diagnostic support system with machine learning, without relying on the skill and experience of endoscopists. Moreover, this system could be used to objectively evaluate endoscopic diagnoses.

Stewart BW, Wild CP: World cancer report 2014. http://publications.iarc.fr/Non-Series-Publications/World-Cancer-Reports/World-Cancer-Report-2014 (accessed February 10, 2018).
Winawer SJ, Zauber AG, Ho MN, O’Brien MJ, Gottlieb LS, Sternberg SS, et al: Prevention of colorectal cancer by colonoscopic polypectomy. The National Polyp Study Workgroup. N Engl J Med 1993; 329: 1977–1981.
Kronborg O: Colon polyps and cancer. Endoscopy 2004; 36: 3–7.
Gono K, Igarashi M, Obi T, Yamaguchi M, Ohyama N: Multiple-discriminant analysis for light-scattering spectroscopy and imaging of two-layered tissue phantoms. Opt Lett 2004; 29: 971.
Gono K, Obi T, Yamaguchi M, Ohyama N, Machida H, Sano Y, et al: Appearance of enhanced tissue features in narrow-band endoscopic imaging. J Biomed Opt 2004; 9: 568.
Machida H, Sano Y, Hamamoto Y, Muto M, Kozu T, Tajiri H, et al: Narrow-band imaging in the diagnosis of colorectal mucosal lesions: a pilot study. Endoscopy 2004; 36: 1094–1098.
Tanaka S, Haruma K, Teixeira CR, Tatsuta S, Ohtsu N, Hiraga Y, et al: Endoscopic treatment of submucosal invasive colorectal carcinoma with special reference to risk factors for lymph node metastasis. J Gastroenterol 1995; 30: 710–717.
Kitajima K, Fujimori T, Fujii S, Takeda J, Ohkura Y, Kawamata H, et al: Correlations between lymph node metastasis and depth of submucosal invasion in submucosal invasive colorectal carcinoma: a Japanese collaborative study. J Gastroenterol 2004; 39: 534–543.
Watanabe T, Muro K, Ajioka Y, Hashiguchi Y, Ito Y, Saito Y, et al: Japanese Society for Cancer of the Colon and Rectum (JSCCR) guidelines 2016 for the treatment of colorectal cancer. Int J Clin Oncol 2017, DOI: 10.1007/s10147-017-1101-6.
Ignjatovic A, East JE, Suzuki N, Vance M, Guenther T, Saunders BP: Optical diagnosis of small colorectal polyps at routine colonoscopy (Detect InSpect ChAracterise Resect and Discard; DISCARD trial): a prospective cohort study. Lancet Oncol 2009; 10: 1171–1178.
Deng L, Yu D: Deep learning: methods and applications. FnT Signal Processing 2013; 7: 197–387.
Løberg M, Kalager M, Holme Ø, Hoff G, Adami H-O, Bretthauer M: Long-term colorectal-cancer mortality after adenoma removal. N Engl J Med 2014; 371: 799–807.
Sano Y, Tanaka S, Kudo S, Saito S, Matsuda T, Wada Y, et al: Narrow-band imaging (NBI) magnifying endoscopic classification of colorectal tumors proposed by the Japan NBI Expert Team. Dig Endosc 2016; 28: 526–533.
Iwatate M, Sano Y, Hattori S, Sano W, Hasuike N, Ikumoto T, et al: The addition of high magnifying endoscopy improves rates of high confidence optical diagnosis of colorectal polyps. Endosc Int Open 2015; 3:E140–E145.
Hisabe T, Tsuda S, Hoashi T, Ishihara H, Yamasaki K, Yasaka T, et al: Validity of conventional endoscopy using “non-extension sign” for optical diagnosis of colorectal deep submucosal invasive cancer. Endosc Int Open 2018; 6:E156–E164.
Schachschal G, Mayr M, Treszl A, Balzer K, Wegscheider K, Aschenbeck J, et al: Endoscopic versus histological characterisation of polyps during screening colonoscopy. Gut 2014; 63: 458–465.
Itoh T, Kawahira H, Nakashima H, Yata N: Deep learning analyzes Helicobacter pylori infection by upper gastrointestinal endoscopy images. Endosc Int Open 2018; 6:E139–E144.
Krizhevsky A, Sutskever I, Hinton GE: ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 2012: 1–9.
Fukushima K, Miyake S: Neocognitron: a new algorithm for pattern recognition tolerant of deformations and shifts in position. Pattern Recognit 1982; 15: 455–469.
Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, et al: Caffe: Convolutional Architecture for Fast Feature Embedding. 2014. http://arxiv.org/abs/1408.5093.
Girshick R, Donahue J, Darrell T, Malik J: Rich feature hierarchies for accurate object detection and semantic segmentation. 2013. http://arxiv.org/abs/1311.2524.
Lachenbruch PA, Mickey MR: Estimation of error rates in discriminant analysis. Technometrics 1968; 10: 1–11.
Apel D, Jakobs R, Schilling D, Weickert U, Teichmann J, Bohrer MH, et al: Accuracy of high-resolution chromoendoscopy in prediction of histologic findings in diminutive lesions of the rectosigmoid. Gastrointest Endosc 2006; 63: 824–828.
Tischendorf JJW, Wasmuth HE, Koch A, Hecker H, Trautwein C, Winograd R: Value of magnifying chromoendoscopy and narrow band imaging (NBI) in classifying colorectal polyps: a prospective controlled study. Endoscopy 2007; 39: 1092–1096.
Fu K-I, Sano Y, Kato S, Fujii T, Nagashima F, Yoshino T, et al: Chromoendoscopy using indigo carmine dye spraying with magnifying observation is the most reliable method for differential diagnosis between non-neoplastic and neoplastic colorectal lesions: a prospective study. Endoscopy 2004; 36: 1089–1093.
Su M-Y, Hsu C-M, Ho Y-P, Chen P-C, Lin C-J, Chiu C-T: Comparative study of conventional colonoscopy, chromoendoscopy, and narrow-band imaging systems in differential diagnosis of neoplastic and nonneoplastic colonic polyps. Am J Gastroenterol 2006; 101: 2711–2716.
Haruki S, Kobayashi K, Yokoyama K, Sada M, Koizumi W: Comparison of diagnostic accuracies of various endoscopic examination techniques for evaluating the invasion depth of colorectal tumors. Gastroenterol Res Pract 2012; 2012: 1–7.
De Palma G-D, Rega M, Masone S, Persico M, Siciliano S, Addeo P, et al: Conventional colonoscopy and magnified chromoendoscopy for the endoscopic histological prediction of diminutive colorectal polyps: a single operator study. World J Gastroenterol 2006; 12: 2402–2405.
Sanomura M, Tanaka S, Sasaki Y, Fukunishi S, Higuchi K: Endoscopic diagnosis of the invasion depth of T1 colorectal carcinoma for endoscopic resection by using narrow-band imaging magnification as total excisional biopsy. Digestion 2016; 94: 106–113.
Komeda Y, Kashida H, Sakurai T, Asakuma Y, Tribonias G, Nagai T, et al: Magnifying narrow band imaging (NBI) for the diagnosis of localized colorectal lesions using the Japan NBI Expert Team (JNET) Classification. Oncology 2017; 93: 49–54.
Komeda Y, Handa H, Watanabe T, Nomura T, Kitahashi M, Sakurai T, et al: Computer-aided diagnosis based on convolutional neural network system for colorectal polyp classification: preliminary experience. Oncology 2017; 93(suppl 1): 30–34.
Shichijo S, Nomura S, Aoyama K, Nishikawa Y, Miura M, Shinagawa T, et al: Application of convolutional neural networks in the diagnosis of Helicobacter pylori infection based on endoscopic images. EBioMedicine 2017; 25: 106–111.
Korbar B, Olofson AM, Miraflor AP, Nicka CM, Suriawinata MA, Torresani L, et al: Deep learning for classification of colorectal polyps on whole-slide images. J Pathol Inform 2017; 8: 30.
Copyright / Drug Dosage / Disclaimer
Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.
You do not currently have access to this content.