Innovative label-free and non-invasive urinary metabolite analysis integrating AI and SERS technology for early cancer detection: A retrospective clinical study involving five cancer types

Background: Accurate early cancer detection remains a critical clinical challenge due to the high cost and limited sensitivity and specificity of conventional diagnostic methods. Indeed, non-invasive approaches capable of molecular-level characterization of biofluids are urgently needed to improve early diagnostic accuracy and patient outcomes. Artificial intelligence-assisted surface-enhanced Raman scattering (AI-SERS) offers a powerful platform to test complex metabolic signatures in urine with exceptional sensitivity. By integrating high-resolution SERS spectra with deep learning-based classification, AI-SERS platform transcends the limitations of conventional assays, enabling highly precise cancer differentiation and unlocking new avenues for the discovery of metabolite-based biomarkers for early cancer detection. Methods: This study enrolled a total of 287 clinical urine samples across six groups: prostate cancer (PRC, n = 49), pancreatic cancer (PAC, n = 16), ovarian cancer (OC, n = 64), lung cancer (LC, n = 30), breast cancer (BC, n = 29), and normal controls (NOR, n = 99). A minimal volume (10 μL) of clinical urine samples was applied to a patented SERS sensor to enhance metabolite signals, measured using a Raman spectrometer. The resulting SERS spectra were analyzed using a convolutional neural network (CNN)-based deep learning approach. Results: The SERS spectra of urine samples from 6 different groups exhibited sharp Raman spectral peaks, providing significant information for analysis. Using the developed CNN model, all cancer types combined could be distinguished from normal controls with an accuracy of 96.6%, sensitivity of 99.3%, and specificity of 94.0%, demonstrating excellent overall classification performance between cancer and non-cancer samples. Further analysis of individual cancer types versus normal controls showed robust predictive performance, with accuracy, sensitivity, and specificity of 98.0%, 98.6%, and 97.3% for PRC; 97.9%, 97.9%, and 97.9% for OC; 97.8%, 97.8%, and 97.8% for LC; and 98.9%, 97.7%, and 100% for BC, respectively. These results demonstrate the strong predictive capability of the AI-SERS platform for noninvasive, cancer-specific detection. Conclusions: The noninvasive, label-free urine analysis using the AI-SERS platform revealed remarkable test performance in classifying cancers from normal controls, offering a rapid cancer screening approach and highlighting its potential for early cancer diagnosis. Ongoing clinical studies aim to identify cancer-type-specific metabolic biomarkers to further improve diagnostic specificity and contribute to enhanced patient outcomes.