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
; 262: 116530, 2024 Oct 15.
Article
in En
| MEDLINE
| ID: mdl-38943854
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Spectrum Analysis, Raman
/
Machine Learning
/
Gastritis
/
Metaplasia
Limits:
Humans
Language:
En
Journal:
Biosens Bioelectron
Journal subject:
BIOTECNOLOGIA
Year:
2024
Document type:
Article
Affiliation country:
China