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Abstract #3740

Cerebral glioma grading using Bayesian Network with features extracted from multi-modality MRI

Jisu Hu # 1 , Wenbo Wu # 2 , Bin Zhu # 2 , Huiting Wang 2 , Renyuan Liu 2 , Xin Zhang 2 , Ming Li 2 , Yongbo Yang 3 , Jing Yan 4 , Fengnan Niu 5 , Chuanshuai Tian 2 , Kun Wang 2 , Haiping Yu 2 , Weibo Chen 6 , Suiren Wan* 1 , Yu Sun* 1 , and Bing Zhang* 2

1 The Laboratory for Medical Electronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China, 2 Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China, 3 Department of Neurosurgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China, 4 Department of Oncology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China, 5 Department of Pathology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China, 6 Philips Healthcare, Shanghai, China

In order to combine multiple modalities of MRI in preoperative cerebral glioma grading, a diagnosing tool based on Bayesian Network was developed to integrate features extracted from conventional MR imaging, perfusion weighted imaging and MR spectroscopic imaging. The structure of the network was determined in cooperation with experienced neuroradiologists and the parameters learned using EM (Expectation-Maximization) algorithm with the incomplete dataset of 52 clinical cases. The grading performance was evaluated in a leave-one-out analysis, achieving the highest grading accuracy of 88.24% with all the features observed.

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