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/.
Bibliographical noteFunding Information:
The authors would like to thank the reviewers for their constructive comments. They would also like to thank Prashant Sridhar and Ajay Rajnikanth for their English proofreading. The work described in this paper was substantially supported by three grants from the Research Grants Council of the Hong Kong Special Administrative Region [CityU 21200816 ], [CityU 11203217 ], and [CityU 11200218 ]. We acknowledge the donation support of the Titan Xp GPU from the Nvidia Corporation.
- Biological Sciences
- Cancer Systems Biology