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Preliminary study of automatic gastric cancer risk classification from photofluorography

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Title: Preliminary study of automatic gastric cancer risk classification from photofluorography
Authors: Togo, Ren Browse this author
Ishihara, Kenta Browse this author
Mabe, Katsuhiro Browse this author
Oizumi, Harufumi Browse this author
Ogawa, Takahiro Browse this author →KAKEN DB
Kato, Mototsugu Browse this author
Sakamoto, Naoya Browse this author
Nakajima, Shigemi Browse this author
Asaka, Masahiro Browse this author
Haseyama, Miki Browse this author →KAKEN DB
Keywords: Gastric cancer
Helicobacter pylori
Mass screening
Photofluorography
Automatic data processing
Issue Date: 15-Feb-2018
Publisher: Baishideng Publishing Group
Journal Title: World Journal of Gastrointestinal Oncology
Volume: 10
Issue: 2
Start Page: 62
End Page: 70
Publisher DOI: 10.4251/wjgo.v10.i2.62
Abstract: 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.
Rights: https://creativecommons.org/licenses/by-nc/4.0/
Type: article
URI: http://hdl.handle.net/2115/71779
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 藤後 廉

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