Combination of label-free SERS-based nanosensor and machine learning for diagnosis of cholangiocarcinoma

Early detection of cholangiocarcinoma (CCA) is vital for informing therapeutic strategies and predicting survival outcomes in patients. This study aims to demonstrate an accurate, label-free method for diagnosing CCA by analyzing a single drop of serum using surface-enhanced Raman spectroscopy (SERS) with silver nanorod substrate and applying machine learning (ML) to the data. Serum samples (n = 194) included those from CCA cases (n = 58), hepatocellular carcinoma (HCC, n = 48), liver metastases (LM, n = 44), and healthy individuals (HA, n = 44). A 2-µL drop of diluted serum (1:320) with with deionized water) was applied on a label-free silver nanorod SERS chip, and 49 points were randomly examined for each sample. The gathered Raman spectra were analyzed using principal component analysis to reduce their dimensionality, and then partial least-squares discriminant analysis was employed to identify diagnostic clusters. Among different ML models tested, Tthe integration of SERS and machine learningwith linear discriminant analysis (LDA) yielded a diagnostic accuracy of 81% for differentiation among cancers (CCA, HCC, LM) and HA as evaluated by train-test split method. Additionally, the area under the receiver operating characteristic curve achieved 0.95 for separating CCA, HCC, LM, and HA groups. Overall, the results indicate that the use of SERS-based diagnostic techniques in conjunction with machine learning has great potential for accurately distinguishing CCA from other liver cancers, such as HCC and LM. These surface-enhanced Raman spectra are ideally suited for developing cost-effective, clinically relevant, point-of-care diagnostic methods for community-based CCA screening.