Integration of machine learning and SERS technology for precise classification and diagnosis of colon cancer from plasma samples.

Surface-Enhanced Raman Spectroscopy (SERS) combined with machine learning offers a transformative label-free approach for colorectal cancer detection, addressing limitations of current diagnostic methods such as invasiveness, high cost, and limited accessibility. In this study, we developed a liquid biopsy platform utilizing a bacterial cellulose substrate functionalized with silver nanoparticles, synthesized via an ascorbic acid cycling reduction method, to enhance SERS signals from plasma without the need for complex reagents. The study enrolled 20 colorectal cancer patients and 20 healthy volunteers, and plasma SERS spectra were collected from each participant. To improve reproducibility and minimize environmental interference, 4-mercaptopyridine was introduced as an internal standard for signal calibration. Machine learning models-including Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM)-achieved classification accuracies all exceeding 86.25 %, with SVM reaching 100 % accuracy in distinguishing between colorectal cancer patients and healthy individuals. This study establishes a clinically promising SERS-machine learning framework that integrates standardized calibration with interpretable artificial intelligence, facilitating rapid cancer screening and advancing precision oncology. It should be noted, however, that these findings are based on a limited sample size, and further validation in larger cohorts is essential.