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©The Author(s) 2018. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Feb 15, 2018; 10(2): 62-70
Published online Feb 15, 2018. doi: 10.4251/wjgo.v10.i2.62
Preliminary study of automatic gastric cancer risk classification from photofluorography
Ren Togo, Kenta Ishihara, Katsuhiro Mabe, Harufumi Oizumi, Takahiro Ogawa, Mototsugu Kato, Naoya Sakamoto, Shigemi Nakajima, Masahiro Asaka, Miki Haseyama
Ren Togo, Kenta Ishihara, Takahiro Ogawa, Miki Haseyama, Graduate School of Information Science and Technology, Hokkaido University, Hokkaido 060-0814, Japan
Katsuhiro Mabe, Mototsugu Kato, Department of Gastroenterology, National Hospital Organization Hakodate Hospital, Hokkaido 041-8512, Japan
Harufumi Oizumi, Medical Examination Center of the Yamagata City Medical Association, Yamagata 990-2473, Japan
Naoya Sakamoto, Department of Gastroenterology, Hokkaido University Graduate School of Medicine, Hokkaido 060-8648, Japan
Shigemi Nakajima, Department of General Medicine, Japan Community Healthcare Organization Shiga Hospital, Shiga 520-0846, Japan
Masahiro Asaka, Health Sciences University of Hokkaido, Hokkaido 061-0293, Japan
Author contributions: Togo R wrote the paper; Ishihara K performed the majority of experiments and analyzed the data; Togo R, Ishihara K, Ogawa T and Haseyama M took charge of the statistical analysis; Mabe K, Oizumi H, Ogawa T, Kato M, Sakamoto N, Nakajima S, Asaka M and Haseyama M designed and coordinated the research.
Supported by JSPS KAKENHI Grant, No. JP17H01744.
Institutional review board statement: The study was reviewed and approved by the Yamagata Medical Association Institutional Review Board.
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous data that were obtained after each patient agreed to inspections by written consent.
Conflict-of-interest statement: The authors have no conflict of interest.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Correspondence to: Dr. Katsuhiro Mabe, MD, PhD, Chief Doctor, Department of Gastroenterology, National Hospital Organization Hakodate Hospital, 18-16, Kawahara-cho, Hokkaido 041-8512, Japan.
kmabe@hnh.hosp.go.jp
Telephone: +81-0138-516281 Fax: +81-0138-516288
Received: November 19, 2017
Peer-review started: November 20, 2017
First decision: December 1, 2017
Revised: December 5, 2017
Accepted: December 13, 2017
Article in press: December 13, 2017
Published online: February 15, 2018
Processing time: 81 Days and 1.7 Hours
AIM
To perform automatic gastric cancer risk classification using photofluorography for realizing effective mass screening as a preliminary study.
METHODS
We used data for 2100 subjects including X-ray images, pepsinogen I and II levels, PGI/PGII ratio, Helicobacter pylori (H. pylori) antibody, H. pylori eradication history and interview sheets. We performed two-stage classification with our system. In the first stage, H. pylori infection status classification was performed, and H. pylori-infected subjects were automatically detected. In the second stage, we performed atrophic level classification to validate the effectiveness of our system.
RESULTS
Sensitivity, specificity and Youden index (YI) of H. pylori infection status classification were 0.884, 0.895 and 0.779, respectively, in the first stage. In the second stage, sensitivity, specificity and YI of atrophic level classification for H. pylori-infected subjects were 0.777, 0.824 and 0.601, respectively.
CONCLUSION
Although further improvements of the system are needed, experimental results indicated the effectiveness of machine learning techniques for estimation of gastric cancer risk.
Core tip: We developed an automatic gastric cancer risk classification system that analyzes X-ray images as a preliminary study. To evaluate the effectiveness of our system, we performed a retrospective analysis of patients who underwent photofluorography and ABC (D) stratification by blood inspection. From the experimental results, we found that machine learning techniques might have a potential for extracting additional gastric cancer risk information. The collaborative use of image-based risk information and ABC (D) stratification will provide more reliable gastric cancer risk information.
