Highly Accurate and Robust Early Stage Detection of Cholangiocarcinoma Using Near-Lossless SERS Signal Processing...

Cholangiocarcinoma (CCA), a malignancy of the bile ducts, presents a significant health burden with a notably high prevalence in Northeast Thailand, where its incidence ratio is 85 per 100,000 population per year. The prognosis for CCA patients remains poor, particularly for proximal tumors, with a dismal 5-year survival rate of just 10%. The challenge in managing CCA is exacerbated by its typically late detection, contributing to a high mortality rate. Current screening methods, such as ultrasound, are insufficient, as many CCA patients do not exhibit prior symptoms or detectable liver fluke (Opisthorchis viverrini : OV) infections, underscoring the urgent need for alternative early detection methods.

In this study, we introduce a novel approach utilizing surface-enhanced Raman spectroscopy (SERS) combined with near-lossless signal compression via discrete wavelet transform (DWT) together with 2D CNN for the first time. Hamster serums of different stages were collected as the data set. DWT was employed for feature extraction, enabling the capture of the entire SERS spectrum, unlike traditional methods like PCA and LDA, which focus only on specific peaks. These features were used to train a 2D convolutional neural network (2D CNN), which is particularly robust against translation, rotation, and scaling, thus effectively addressing the SERS peak shifting issues. We validated our approach using gold-standard histology, and notably, our method could detect CCA at an early stage. The ability to identify CCA at the early stage significantly improves the chances of successful intervention and patient outcomes.

Our results demonstrate that our method, combining SERS with extremely compact wavelet feature extraction and 2D CNN, outperformed other approaches (PCA + SVM, PCA + 1D CNN, PCA + 2D CNN, LDA + SVM, and DWT + 1D CNN), achieving performance of 95.1% accuracy, 95.08% sensitivity, 98.4% specificity, and an area under the curve (AUC) of 95%. The trained model was further deployed on a server and mobile application interface, paving the way for future field experiments in rural areas and home-use potential point-of-care services.