Noninvasive cholangitis and cholangiocarcinoma screening based on serum Raman spectroscopy and support vector machine

Abstract

The feasibility of serum Raman spectroscopy for rapid screening of cholangitis and cholangiocarcinoma (CCA) was explored Raman spectra were collected from 49 patients with cholangitis, 38 patients with CCA, and 55 healthy volunteers. Normalized mean Raman spectra and spectral attributions reveal disease-specific biomolecular differences. Support vector machine (SVM) was used to establish the two-way (cholangitis vs healthy, CCA vs healthy etc.) and 3-way (cholangitis vs CCA vs healthy) classification model, and leave-one-out cross-validation (LOOCV) was used to verify these models' performance. Based on the support vector machine algorithm, serum Raman spectroscopy could identify cholangitis and CCA. Its diagnostic sensitivity, and specificity were 89.80%, 94.55%, and 89.50%, 98.18%, respectively. This study demonstrates that label-free serum Raman spectroscopy analysis technique combined with SVM diagnostic algorithm has great potential for noninvasive cholangitis and CCA screening.

Keywords: Cholangiocarcinoma (CCA); Cholangitis; Raman spectroscopy; Screening; Serum; Support vector machine (SVM).