Combination of Label-Free SERS-Based Nanosensors and Machine Learning for Diagnosis of Cholangiocarcinoma

Early detection of cholangiocarcinoma (CCA) is vital for forming therapeutic strategies and predicting survival outcomes in patients. This study aimed to demonstrate an accurate, label-free method for diagnosing CCA by analyzing a single drop of serum using surface-enhanced Raman spectroscopy (SERS) with a silver nanorod substrate, combined with machine learning (ML) analysis. 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 serum (diluted 1:320 with deionized water) was drop-cast onto 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 for unsupervised clustering, followed by linear discriminant analysis (LDA) to identify diagnostic clusters. Among the different supervised ML models tested, the integration of SERS with LDA yielded a diagnostic accuracy of 81% for differentiation among cancers (CCA, HCC, LM) and HA as evaluated by the train-test split method. Additionally, the area under the receiver operating characteristic curve was achieved as 0.95 for detecting 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.