Si YT, Xiong XS, Wang JT, Yuan Q, Li YT, Tang JW, Li YN, Zhang XY, Li ZK, Lai JX, Umar Z, Yang WX, Li F, Wang L, and Gu B
The progression of gastric cancer involves a complex multi-stage process, with gastroscopy and biopsy being the standard procedures for diagnosing gastric diseases. This study introduces an innovative non-invasive approach to differentiate gastric disease stage using gastric fluid samples through machine-learning-assisted surface-enhanced Raman spectroscopy (SERS). This method effectively identifies different stages of gastric lesions. The XGBoost algorithm demonstrates the highest accuracy of 96.88% and 91.67%, respectively, in distinguishing chronic non-atrophic gastritis from intestinal metaplasia and different subtypes of gastritis (mild, moderate, and severe). Through blinded testing validation, the model can achieve more than 80% accuracy. These findings offer new possibilities for rapid, cost-effective, and minimally invasive diagnosis of gastric diseases., Competing Interests: Declaration of competing interest The authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)