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Abstract #3067

Conventional MR-based machine learning for distinguishing brain glioma and solitary metastasis

Zhe Liu1, Chao Jin1, Xiaotong Liu2, Changchang Yin2, Ting Liang1, Yitong Bian1, Yonghao Du1, Qinli Sun1, Zhongqiang Shi1, Buyue Qian2, and Jian Yang1

1The first affiliated Hospital of XI'AN Jiaotong University, XI'AN, China, 2Xi’an Jiaotong University, XI'AN, China

Differentiation of brain glioma and solitary metastasis is clinically crucial for prescribing the patients’ management and assessing the prognosis. However, indistinguishable signs between two tumors on conventional MRI always embarrass the radiologists and thus lead to high misdiagnosis rate. To address such issue, series of MR features like grey level co-occurrence matrix, histograms of oriented gradient, shape and etc. were first extracted to detail the tumors’ histologic and morphologic characteristics. Then, a gradient-boosting machine learning approach was employed to distinguish the two tumors by the MR features. A good performance with area under receiver operating characteristic curve 0.80, sensitivity 85% and specificity 78% was obtained, suggesting the potential role of our approach in identifying brain glioma and solitary metastasis.

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