Abstract #0870

An artificial intelligence decision tree diagnostic platform helps neuroradiologists reclassify adult-type diffuse gliomas

Liqiang Zhang¹, Xinyi Xu¹, Hongyu Pan², Jueni Gao³, Linling Wang¹, Zhi Liu⁴, Xu Cao⁵, and Yongmei Li¹

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: Deep learning networks offers an opportunity for diffuse gliomas classification, which may be help for therapeutic decision making and selection of patient groups suitable for targeted genetic analysis.

Goal(s): The purpose of this study is to develop an artificial intelligence method to reclassify adult-type diffuse gliomas based on the new WHO CNS tumor classification.

Approach: An artificial intelligence decision tree diagnostic platform(DTDP) based on MRI and deep learning networks was developed by combined 6 individualized CNNs models in series and parallel

Results: The DTDP performed well with accuracy of 86.67%.

Impact: The DTDP achieved automatic classification and comprehensive diagnosis of adult‑type diffuse gliomas by combining genetic biomarkers and histological grading, and effectively helped neuroradiologists to reclassify adult-type diffuse gliomas.

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