Association between gastric cancer and the Kyoto classification of gastritis

Helicobacter pylori infection in subjects negative for high titer serum antibody

AIM

To investigate the clinicopathological features of the patients testing negative for high titer serum anti-Helicobacter pylori (H. pylori) antibody.

METHODS

The antibody titers were measured using antigens derived from Japanese individuals. 13C-urea breath test-positive individuals were defined as having H. pylori infection. We investigated the demographic characteristics, laboratory data, endoscopic findings including Kyoto classification of gastritis, and histology in negative-high titer patients without H. pylori eradication therapy. Kyoto classification consisted of scores for gastric atrophy, intestinal metaplasia, enlarged folds, nodularity, and redness.

RESULTS

Of the 136 subjects enrolled, 23 (17%) had H. pylori infection. Kyoto classification had an excellent area under the receiver operating characteristics curve (0.886, 95% confidence interval: 0.803-0.968, P = 3.7 × 10-20) for predicting H. pylori infection with a cut-off value of 2. Further, Kyoto classification, H. pylori density, and neutrophil activity had high accuracies (89.7%, 96.3%, and 94.1%, respectively). Kyoto classification was independent of the demographic and laboratory parameters in multivariate analysis.

CONCLUSION

Endoscopic Kyoto classification of gastritis is a useful predictor of H. pylori infection in negative-high titer antibody patients.

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.