Detecting
cancers at early stages can dramatically reduce mortality rates. Therefore, practical
cancer screening at the population level is needed. To develop a comprehensive detection system to classify multiple
cancer types, we integrated an artificial intelligence deep learning neural network and
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. Furthermore, a comprehensive marker panel can simultaneously multi-classify common
cancers with a stable 82.15% accuracy rate for heterogeneous cancerous tissues and conditions. This detection system provides a promising practical framework for automatic
cancer screening at population level. Key points: (1) We developed a practical
cancer screening system, which is simple, accurate, affordable, and easy to operate. (2) Our system binarily classify
cancers vs. normal with >96% AUC. (3) In total, 26 individual
cancer types can be easily detected by our system with 99 to 100% AUC. (4) The system can detect multiple
cancer types simultaneously with >82% accuracy.