Abstract #3773

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

Xinyi Xu¹, Liqiang Zhang¹, Hongyu Pan², Jueni Gao³, Linling Wang¹, Zhi Liu⁴, Xu Cao⁵, and Ming Wen¹

¹The First Affiliated Hospital of Chongqing Medical University, Chongqing, China, ²Southwest University, Chongqing, Chongqing, China, ³Shanxi Provincial People's Hospital, Shanxi, Shanxi, China, ⁴Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China, ⁵School of Medical and Life Sciences Chengdu University of Traditional Chinese Medicine, Chengdu, China

Synopsis

Keywords: Diagnosis/Prediction, Brain

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.

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