Identification of chronic non-atrophic gastritis and intestinal metaplasia stages in the Correa's cascade through machine learning analyses of SERS spectral signature of non-invasively-collected human gastric fluid samples

Document Type

Journal Article

Publication Title

Biosensors and Bioelectronics

Volume

262

Publisher

Elsevier

School

Centre for Precision Health / School of Medical and Health Sciences

RAS ID

71168

Funders

Basic and Applied Basic Research Foundation of Guangdong Province / Research Foundation for Advanced Talents of Guangdong Provincial People's Hospital / Fifth People's Hospital of Huai'an Collaboration Foundation / National Natural Science Foundation of China / National Key Research and Development Program of China

Grant Number

2022A151522002, KY012023293, KJ012021097, HWY-YL-20230072, 82272423, 82072380, 2023YFC2606200

Comments

Si, Y. T., Xiong, X. S., Wang, J. T., Yuan, Q., Li, Y. T., Tang, J. W., ... & Gu, B. (2024). Identification of chronic non-atrophic gastritis and intestinal metaplasia stages in the Correa's cascade through machine learning analyses of SERS spectral signature of non-invasively-collected human gastric fluid samples. Biosensors and Bioelectronics, 262, 116530. https://doi.org/10.1016/j.bios.2024.116530

Abstract

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.

DOI

10.1016/j.bios.2024.116530

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