Endoscopy 2017; 49(08): 798-802
DOI: 10.1055/s-0043-105486
Innovations and brief communications
© Georg Thieme Verlag KG Stuttgart · New York

Accuracy of diagnosing invasive colorectal cancer using computer-aided endocytoscopy

Kenichi Takeda
Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
,
Shin-ei Kudo
Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
,
Yuichi Mori
Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
,
Masashi Misawa
Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
,
Toyoki Kudo
Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
,
Kunihiko Wakamura
Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
,
Atsushi Katagiri
Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
,
Toshiyuki Baba
Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
,
Eiji Hidaka
Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
,
Fumio Ishida
Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
,
Haruhiro Inoue
Digestive Disease Center, Showa University Koto Toyosu Hospital, Tokyo, Japan
,
Masahiro Oda
Graduate School of Information Science, Nagoya University, Nagoya, Japan
,
Kensaku Mori
Information and Communications, Nagoya University, Nagoya, Japan
› Author Affiliations
Further Information

Publication History

submitted 01 November 2016

accepted after revision 15 February 2017

Publication Date:
04 May 2017 (eFirst)

Abstract

Background and study aims Invasive cancer carries the risk of metastasis, and therefore, the ability to distinguish between invasive cancerous lesions and less-aggressive lesions is important. We evaluated a computer-aided diagnosis system that uses ultra-high (approximately × 400) magnification endocytoscopy (EC-CAD).

Patients and methods We generated an image database from a consecutive series of 5843 endocytoscopy images of 375 lesions. For construction of a diagnostic algorithm, 5543 endocytoscopy images from 238 lesions were randomly extracted from the database for machine learning. We applied the obtained algorithm to 200 endocytoscopy images and calculated test characteristics for the diagnosis of invasive cancer. We defined a high-confidence diagnosis as having a ≥ 90 % probability of being correct.

Results Of the 200 test images, 188 (94.0 %) were assessable with the EC-CAD system. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were 89.4 %, 98.9 %, 94.1 %, 98.8 %, and 90.1 %, respectively. High-confidence diagnosis had a sensitivity, specificity, accuracy, PPV, and NPV of 98.1 %, 100 %, 99.3 %, 100 %, and 98.8 %, respectively.

Conclusion: EC-CAD may be a useful tool in diagnosing invasive colorectal cancer.