Early cancer detection by serum biomolecular fingerprinting spectroscopy with machine learning
Shilian Dong, Dong He, Qian Zhang, Chaoning Huang, Zhiheng Hu, Chenyang Zhang, Lei Nie, Kun Wang, Wei Luo, Jing Yu, Bin Tian, Wei Wu, Chen Xu, Xinghuan Wang, Jing Hu, Xiangheng Xiao
Abstract
Abstract Label-free surface-enhanced Raman scattering (SERS) technique with ultra-sensitivity becomes more and more desirable in biomedical analysis, which is yet hindered by inefficient follow-up data analysis. Here we report an integrative method based on SERS and Artificial Intelligence for Cancer Screening (SERS-AICS) for liquid biopsy such as serum via silver nanowires, combining molecular vibrational signals processing with large-scale data mining algorithm. According to 382 healthy controls and 1582 patients from two independent cohorts, SERS-AICS not only distinguishes pan-cancer patients from health controls with 95.81% overall accuracy and 95.87% sensitivity at 95.40% specificity, but also screens out those samples at early cancer stage. The supereminent efficiency potentiates SERS-AICS a promising tool for detecting cancer with broader types at earlier stage, accompanying with the establishment of a data platform for further deep analysis.