Minimally invasive detection for an early stage of opisthorchiasis-associated cholangiocarcinoma in hamster serum using label-free surface-enhanced Raman spectroscopy (SERS)
Description
Background Cholangiocarcinoma (CCA) is a deadly cancer often detected late. Current diagnostic methods, such as ultrasound and invasive biopsies, have limitations; there is a critical need for a rapid, minimally invasive, and effective strategy for the early diagnosis and staging of CCA. Methods We aimed to address this need using serum samples and label-free surface-enhanced Raman spectroscopy (SERS) combined with machine learning. CCA development was induced in hamsters using a combination of Opisthorchis viverrini infection and administration of N-nitrosodimethylamine, with induction time courses spanning 1–5 months (s). Normal and pathological stages (inflammation, precancerous lesion, and CCA) were assigned based on histopathological features, as well as the expression of cytokeratin 19 and alpha-fetoprotein. Raman spectra were subjected to dimensionality reduction using principal component analysis, and diagnostic clusters were acquired using partial least-squares discriminant analysis. Results Histopathological analysis confirmed a clear CCA progression through chronic inflammation-mediated carcinogenesis. The process initiated with high inflammation, advanced to include significant cholangiofibrosis and cholangiofibroma in the precancerous stage, and culminated in definitive tumor development in the CCA stage. The integration of SERS and machine learning achieved a diagnostic sensitivity of 93%, specificity of 95%, and accuracy of ≥ 67% for precancerous lesions and CCA, with an area under the receiver operating characteristic curve exceeding 0.67. Conclusions Our findings demonstrate that this cost-effective, label-free SERS approach can accurately detect precancerous and cancerous stages of cholangiocarcinoma in a preclinical hamster model, highlighting its strong potential for future development as a community-based screening tool.
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Institutions
- Khon Kaen University Faculty of Medicine