Machine learning classification and biochemical characteristics in the real-time diagnosis of gastric adenocarcinoma using Raman spectroscopy.

This study aimed to identify biomolecular differences between benign gastric tissues (gastritis/intestinal metaplasia) and gastric adenocarcinoma and to evaluate the diagnostic power of Raman spectroscopy-based machine learning in gastric adenocarcinoma. Raman spectroscopy-based machine learning was applied in real-time during endoscopy in 19 patients (aged 51-85 years) with high-risk for gastric adenocarcinoma. Raman spectra were captured from suspicious lesions and adjacent normal mucosa, which were biopsied for matched histopathologic diagnosis. Spectral data were analyzed using principal component analysis (PCA) and linear discriminant analysis (LDA) with leave-one-out cross-validation (LOOCV) to develop a machine learning model for diagnosing gastric adenocarcinoma. High-quality spectra (800-3300 cm⁻¹) revealed distinct patterns: adenocarcinoma tissues had higher intensities below 3150 cm⁻¹, while benign tissues exhibited higher intensities between 3150 and 3290 cm⁻¹ (p < 0.001). The model achieved diagnostic accuracy, sensitivity, specificity, and AUC values of 0.905, 0.942, 0.787, and 0.957, respectively. Biochemical correlations suggested adenocarcinoma tissues had increased protein (e.g., phenylalanine), reduced lipids, and lower water content compared to benign tissues. This study highlights the potential of Raman spectroscopy with machine learning as a real-time diagnostic tool for gastric adenocarcinoma. Further validation could establish this technique as a non-invasive, accurate method to aid clinical decision-making during endoscopy.