Published online Feb 15, 2018. doi: 10.4251/wjgo.v10.i2.62
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
To perform automatic gastric cancer risk classiﬁcation using photoﬂuorography for realizing effective mass screening as a preliminary study.
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.
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.
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.