The value of LCI-based modified Kyoto classification risk scoring system in predicting the risk of early gastric cancer

Abstract

Objective

To study and compare the value of the Kyoto classification risk scoring system and the modified Kyoto classification risk scoring system based on linked color imaging (LCI) in predicting the risk of early gastric cancer.

Methods

One hundred and fifty patients with pathologically confirmed non-cardia early gastric cancer by endoscopic LCI and 150 non-gastric cancer patients matched for age and gender were included. Basic patient data and whole gastric endoscopic images under LCI were collected, and the images were scored according to the LCI-based Kyoto classification risk scoring system and the LCI-based modified Kyoto classification risk scoring system.

Results

Compared with the LCI-based Kyoto classification risk scoring system, the LCI-based modified Kyoto classification risk scoring system had a higher AUC for predicting the risk of early gastric cancer (0.723 vs. 0.784, p = 0.023), with a score of ≥3 being the best cutoff value for predicting the risk of early gastric cancer (sensitivity 61.33%, specificity 86.00%), and scores of 3 to 5 were significantly associated with early gastric carcinogenesis significantly (OR = 9.032, 95% CI: 4.995–16.330, p < 0.001).

Conclusions

Compared with the LCI-based Kyoto classification risk scoring system, the LCI-based Kyoto modified classification risk scoring system has a better value for predicting the risk of early gastric cancer, and the score of 3 to 5 is a high-risk factor for the risk of early gastric cancer development, which is more strongly correlated with the risk of early gastric cancer.

Keywords:

• Linked color imaging
• the Kyoto classification risk scoring system
• the modified kyoto classification risk scoring system
• early gastric cancer

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was supported by the Startup Fund for scientific research, Fujian Medical University [Grant number: 2022QH1425] and the guiding program of Fujian Provincial Science and Technology Department [Grant number: 2023Y0070].