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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
ARTICLE HIGHLIGHTS
Research background
Gastric cancer is one of the most common malignancies, and has the highest mortality rates in East Asian countries. Although ABC (D) stratification is effective method for evaluating gastric cancer risk, photofluorography still plays an important role in gastric cancer mass screening since image-based evaluation is mandatory.
Research motivation
If gastric cancer risk information can be provided automatically by analyzing X-ray images, it would be helpful for the future of gastric cancer mass screening.
Research objectives
The aim of this study was investigation of potential of machine learning techniques using photofluorography.
Research methods
We developed an automatic gastric cancer risk classification system for identification of Helicobacter pylori infection status and atrophic level from photofluorography. All of 2100 patients’ data were acquired at the Medical Examination Center of Yamagata City Medical Association in Japan, from April 2012 to March 2013. From DICOM data, we extracted the image data while securing anonymity.
Research results
Experimental results suggested that image-based risk information can be calculated by our system.
Research conclusions
Although further investigation and improvement of the system are needed, this retrospective study indicated that machine learning techniques analyzing X-ray images can provide effective gastric cancer risk information. Also, we discussed the potential of machine learning techniques and the future of gastric cancer mass screening.
Research perspectives
In the field of breast cancer, computer-aided supporting systems have already become a part of routine clinical work for detection of breast cancer or abnormalities. Gastric cancer as well as breast cancer requires effective and highly accurate mass screening. We believe that this preliminary study will contribute the next step of the future of gastric cancer mass screening.
