Keywords: Tumors, Machine Learning/Artificial Intelligence, RadiomicsThe study aimed to predict glioma genotypes combined with radiomic features and deep learning networks by using amide proton transfer (APT) imaging. The genetic subtypes of gliomas can be predicted by radiomics and deep learning networks using conventional MRI, however there are still problems with low accuracy and insufficient generalization. This study puts the screened APT radiomics features into a neural network and compares it with traditional radiomic. The results demonstrated that the proposed model had better performance. Therefore, APTw-derived radiomic features have good ability to predict 3-class molecular typing, providing novel classification tool for non-invasive evaluation for glioma genotypes.
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