Early Cancer Detection from Multianalyte Blood Test Results
Ka-Chun Wong,
Junyi Chen,
Jiao Zhang,
Jiecong Lin,
Shankai Yan,
Shxiong Zhang,
Xiangtao Li,
Cheng Liang,
Chengbin Peng,
Qiuzhen Lin,
Sam Kwong,
Jun Yu
Affiliations
Ka-Chun Wong
Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR; Corresponding author
Junyi Chen
Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
Jiao Zhang
Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
Jiecong Lin
Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
Shankai Yan
Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
Shxiong Zhang
Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
Xiangtao Li
School of Information Science and Technology, Northeast Normal University, Jilin, China
Cheng Liang
School of Information Science and Engineering, Shandong Normal University, Shandong, China
Chengbin Peng
Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Ningbo, China
Qiuzhen Lin
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
Sam Kwong
Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
Jun Yu
Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR
Summary: The early detection of cancers has the potential to save many lives. A recent attempt has been demonstrated successful. However, we note several critical limitations. Given the central importance and broad impact of early cancer detection, we aspire to address those limitations. We explore different supervised learning approaches for multiple cancer type detection and observe significant improvements; for instance, one of our approaches (i.e., CancerA1DE) can double the existing sensitivity from 38% to 77% for the earliest cancer detection (i.e., Stage I) at the 99% specificity level. For Stage II, it can even reach up to about 90% across multiple cancer types. In addition, CancerA1DE can also double the existing sensitivity from 30% to 70% for detecting breast cancers at the 99% specificity level. Data and model analysis are conducted to reveal the underlying reasons. A website is built at http://cancer.cs.cityu.edu.hk/. : Biological Sciences; Cancer Systems Biology; Cancer; Algorithms; Bioinformatics Subject Areas: Biological Sciences, Cancer Systems Biology, Cancer, Algorithms, Bioinformatics
