Early Cancer Detection from Multianalyte Blood Test Results

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

20 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)332-341
Number of pages9
Journal / PublicationiScience
Volume15
Online published4 May 2019
Publication statusPublished - 31 May 2019

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Abstract

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/.

Research Area(s)

  • Algorithms, Bioinformatics, Biological Sciences, Cancer, Cancer Systems Biology

Citation Format(s)

Early Cancer Detection from Multianalyte Blood Test Results. / Wong, Ka-Chun; Chen, Junyi; Zhang, Jiao et al.
In: iScience, Vol. 15, 31.05.2019, p. 332-341.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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