Early cancer detection by serum biomolecular fingerprinting spectroscopy with machine learning

eLight - Tập 3 - Trang 1-11 - 2023
Shilian Dong1, Dong He1, Qian Zhang2, Chaoning Huang1, Zhiheng Hu3, Chenyang Zhang1, Lei Nie4, Kun Wang4, Wei Luo5, Jing Yu6, Bin Tian7, Wei Wu7, Xu Chen3, Fubing Wang2,8, Jing Hu9,10, Xiangheng Xiao1,8
1Department of Physics, National Demonstration Center for Experimental Physics Education, Wuhan University, Wuhan, China
2Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
3School of Computer Science, Wuhan University, Wuhan, China
4Department of Hepatobiliary Pancreatic Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
5Department of Clinical Laboratory, Tianjin Medical University General Hospital, Tianjin, China
6Department of Blood Transfusion, Wuhan Hospital of Traditional Chinese and Western Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
7Laboratory of Printable Functional Materials and Printed Electronics, Research Center for Graphic Communication, Printing and Packaging, Wuhan University, Wuhan, China
8Wuhan Research Center for Infectious Diseases and Cancer, Chinese Academy of Medical Sciences, Wuhan, China
9Sichuan Provincial Key Laboratory for Human Disease Gene Study and the Center for Medical Genetics, Department of Laboratory Medicine, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, University of Electronic Science and Technology, Chengdu, China
10School of Medicine, University of Electronic Science and Technology of China, Chengdu, China

Tóm tắt

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.

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