Early Cancer Detection from Multianalyte Blood Test Results
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AbstractThe 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.
All Author(s) ListKa-Chun Wong, Junyi Chen, JiaoZhang, Jiecong Lin, Shankai Yan, Shxiong Zhang, Xiangtao Li, Cheng Liang, Chengbin Peng, Qiuzhen Lin, Sam Kwong, JunYu
Journal nameiScience
Year2019
Month5
Day31
Volume Number15
PublisherElsevier (Cell Press): OAJ / Elsevier
Pages332
ISSN2589-0042
LanguagesEnglish-United States

Last updated on 2020-12-07 at 03:43