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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