Highly Accurate and Robust Early Stage Detection of Cholangiocarcinoma Using Near-Lossless SERS Signal Processing with Machine Learning and 2D CNN for Point-of-care Mobile Application

Introduction: 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.
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. Results and conclusion: 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.