Machine learning-assisted label-free colorectal cancer diagnosis using plasmonic needle-endoscopy system

Abstract

Early and accurate detection of colorectal cancer (CRC) is critical for improving patient outcomes. Existing diagnostic techniques are often invasive and carry risks of complications. Herein, we introduce a plasmonic gold nanopolyhedron (AuNH)-coated needle-based surface-enhanced Raman scattering (SERS) sensor, integrated with endoscopy, for direct mucus sampling and label-free detection of CRC. The thin and flexible stainless-steel needle is coated with polymerized dopamine, which serves as an adhesive layer and simultaneously initiates the nucleation of gold nanoparticle (AuNP) seeds on the needle surface. The AuNP seeds are further grown through a surface-directed reduction using Au ions-hydroxylamine hydrochloride solution, resulting in the formation of dense AuNHs. The formation mechanism of AuNHs and the layered structure of the plasmonic needle-based SERS (PNS) sensor are thoroughly analyzed. Furthermore, a strong field enhancement of the PNS sensor is observed, amplified around the edges of the polyhedral shapes and at nanogap sites between AuNHs. The feasibility of the PNS sensor combined with endoscopy system is further investigated using mouse models for direct colonic mucus sampling and verifying noninvasive label-free classification of CRC from normal controls. A logistic regression-based machine learning method is employed and successfully differentiates CRC and normal mice, achieving 100% sensitivity, 93.33% specificity, and 96.67% accuracy. Moreover, Raman profiling of metabolites and their correlations with Raman signals of mucus samples are analyzed using the Pearson correlation coefficient, offering insights for identifying potential cancer biomarkers. The developed PNS-assisted endoscopy technology is expected to advance the early screening and diagnosis approach of CRC in the future.