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

Biosens Bioelectron. 2024 Oct 15:262:116530. doi: 10.1016/j.bios.2024.116530. Epub 2024 Jun 26.

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.

Keywords: Chronic non-atrophic gastritis; Gastric fluid; Intestinal metaplasia; Machine learning algorithms; String test; Surface-enhanced Raman spectroscopy.

MeSH terms

  • Algorithms
  • Biosensing Techniques / methods
  • Chronic Disease
  • Gastric Juice / chemistry
  • Gastritis* / diagnosis
  • Gastritis* / pathology
  • Humans
  • Machine Learning*
  • Metaplasia* / pathology
  • Spectrum Analysis, Raman* / methods
  • Stomach Neoplasms / diagnosis
  • Stomach Neoplasms / pathology