Preliminary study of automatic gastric cancer risk classification from photofluorography

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.