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Cholangiocarcinoma (CCA) is a bile duct cancer that originates in the bile duct epithelium. Northeastern Thailand has the highest incidence of CCA, and there is a direct correlation with liver fluke (Opisthorchis viverrini) infection. The high mortality rate of CCA is a consequence of delayed diagnosis. Fourier transform infrared (FTIR) spectroscopy is a powerful technique that detects the absorbance of molecular vibrations and is perfectly suited for the interrogation of biological samples. In this study, we applied synchrotron radiation-FTIR (SR-FTIR) microspectroscopy and focal plane array (FPA-FTIR) microspectroscopy to characterize periductal fibrosis and bile duct cells progressing to CCA induced by inoculating O. viverrini metacercariae into hamsters. SR-FTIR and FPA-FTIR measurements were performed in liver sections harvested from 1-, 2-, 3-, and 6-month post-infected hamsters compared to uninfected liver tissues. Principal component analysis (PCA) of the tissue samples showed a clear discrimination among uninfected and early-stage (1 and 2 months) and cancerous-stage (3 and 6 months) tissues. The discrimination is based on intensity changes in the phosphodiester band (1081 cm-1), amino acid residue (∼1396 cm-1), and C= O stretching carboxylic esters (1745 cm-1). Infected tissues also show definitive bands at ∼1280, 1234, and 1201 cm-1 characteristic of the collagen triplet and indicative of fibrosis. Hierarchical cluster analysis (HCA) was performed on the FPA data and showed a classification into specific cell types. Hepatocyte, fibrotic lesion, and bile duct (cancer) were classified and HCA mapping showed similar cellular distribution pattern compared to Sirius red staining. This study was also extended to less invasive sample analysis using attenuated total reflectance-FTIR (ATR-FTIR) spectroscopy. Sera from O. viverrini-infected and uninfected hamsters were analyzed using multivariate analysis, including principal component analysis (PCA), and partial least squares-discriminant analysis (PLS-DA). PCA was able to classify spectra of normal, early-stage CCA, and CCA, while the PLS-DA gave 100% accuracy for the validation. The model was established from 17 samples (11 normal, 6 cancer) in the calibration set and 9 samples in the validation set (4 normal, 2 cancer, 3 precancerous). These results indicate that FTIR-based technology is a potential tool to detect the progression of CCA, especially in the early stages of the disease.