A Decision Tree Diagnostic Scheme Based on Multi-label Deep Learning Network for Classification of Adult-type Diffuse Gliomas

Motivation: Genetic biomarkers and WHO grading of gliomas are critical for the classification of glioma subtypes, treatment planning and survival prognosis. Goal(s): The aim of this study is to apply DL network for non-invasive prediction of multiple genes and classification of subtypes. Approach: A decision tree diagnostic scheme based on multi-label DL network was constructed to classify adult-type diffuse gliomas into 5 subtypes based on the 2021 WHO classification of tumor of the CNS, combining the WHO grading and 3 genetic biomarkers status. Results: The model we developed can reclassify adult-type diffuse glioma with a diagnostic accuracy of 94.4%. Impact: Based on the 2021 WHO CNS tumor classification, this study applies multi-label deep learning to reclassify adult-type diffuse gliomas, which can be helpful for patients to obtain preoperative diagnosis and precise treatment.