Machine learning classification and biochemical characteristics in the real-time diagnosis of gastric adenocarcinoma using Raman spectroscopy
Abstract 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 lea...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-025-86763-9 |
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| author | Alex Noh Sabrina Xin Zi Quek Nuraini Zailani Juin Shin Wee Derrick Yong Byeong Yun Ahn Khek Yu Ho Hyunsoo Chung |
| author_facet | Alex Noh Sabrina Xin Zi Quek Nuraini Zailani Juin Shin Wee Derrick Yong Byeong Yun Ahn Khek Yu Ho Hyunsoo Chung |
| author_sort | Alex Noh |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-b71dbd2a42c24f82bfaab87fafc97191 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-b71dbd2a42c24f82bfaab87fafc971912025-08-20T01:54:22ZengNature PortfolioScientific Reports2045-23222025-01-0115111010.1038/s41598-025-86763-9Machine learning classification and biochemical characteristics in the real-time diagnosis of gastric adenocarcinoma using Raman spectroscopyAlex Noh0Sabrina Xin Zi Quek1Nuraini Zailani2Juin Shin Wee3Derrick Yong4Byeong Yun Ahn5Khek Yu Ho6Hyunsoo Chung7School of Clinical Medicine, Faculty of Medicine and Health, University of New South WalesDivision of Gastroenterology and Hepatology, Department of Medicine, National University HospitalSingapore University of Technology and DesignNational University of SingaporeNational University of SingaporeArmed Forces Seoul Center District HospitalNational University of SingaporeDepartment of Internal Medicine and Liver Research Institute, Department of Medical Device Development, Seoul National University Hospital, Seoul National University College of MedicineAbstract 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.https://doi.org/10.1038/s41598-025-86763-9Raman spectroscopyMass spectrometry imagingMachine learningGastric cancerReal-time diagnosis |
| spellingShingle | Alex Noh Sabrina Xin Zi Quek Nuraini Zailani Juin Shin Wee Derrick Yong Byeong Yun Ahn Khek Yu Ho Hyunsoo Chung Machine learning classification and biochemical characteristics in the real-time diagnosis of gastric adenocarcinoma using Raman spectroscopy Scientific Reports Raman spectroscopy Mass spectrometry imaging Machine learning Gastric cancer Real-time diagnosis |
| title | Machine learning classification and biochemical characteristics in the real-time diagnosis of gastric adenocarcinoma using Raman spectroscopy |
| title_full | Machine learning classification and biochemical characteristics in the real-time diagnosis of gastric adenocarcinoma using Raman spectroscopy |
| title_fullStr | Machine learning classification and biochemical characteristics in the real-time diagnosis of gastric adenocarcinoma using Raman spectroscopy |
| title_full_unstemmed | Machine learning classification and biochemical characteristics in the real-time diagnosis of gastric adenocarcinoma using Raman spectroscopy |
| title_short | Machine learning classification and biochemical characteristics in the real-time diagnosis of gastric adenocarcinoma using Raman spectroscopy |
| title_sort | machine learning classification and biochemical characteristics in the real time diagnosis of gastric adenocarcinoma using raman spectroscopy |
| topic | Raman spectroscopy Mass spectrometry imaging Machine learning Gastric cancer Real-time diagnosis |
| url | https://doi.org/10.1038/s41598-025-86763-9 |
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