Abstract:
Detecting cancers at the population level can dramatically reduce mortality rates, but there is no way to systematically detect all cancers today. Here we develop an accurate cancer detection system to discriminate multiple types of cancers by integrating an artificial intelligence deep learning neural network and universal noncoding RNA biomarkers selected from massive data. Our system can accurately detect cancer vs. healthy objects with 96.3% of AUC of ROC (Area Under Curve of a Receiver Operating Characteristic curve), and it surprisingly reaches 78.77% of AUC when validated by real-world raw data from a completely independent data set. Even validating with raw exosome data from blood, our system can reach 72% of AUC. Moreover, our system significantly outperforms conventional machine learning models, such as random forest. Intriguingly, with no more than six biomarkers, our approach can easily discriminate any individual cancer type vs. normal with 99% to 100% AUC. This detection system provides a promising practical framework for automatic cancer screening at the population level.
