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Abstract #2086

MRI based texture analysis on FLAIR and ADC to predict malignant transformation of Low Grade Gliomas

Shun Zhang1,2, Gloria Chia-Yi Chiang2, Yihao Yao1, Ramin Jafari2, Rajiv S. Magge3, Howard Alan Fine3, Rohan Ramakrishna4, Yi Wang2,5, and Ilhami Kovanlikaya2

1Radiolgy, Tongji Hospital, Tongji Medical College, HUST, Wuhan, China, 2Radiolgy, Weill Cornell Medical College, NewYork, NY, United States, 3Neurology, Weill Cornell Medical College, NewYork, NY, United States, 4Neurological Surgery, Weill Cornell Medical College, NewYork, NY, United States, 5Biomedical Engineerring, Cornell University, Ithaca, NY, United States

Low grade gliomas (LGG) may undergo malignant transformation into high-grade gliomas, which generally occur within 5 years in about 50% of patients. Hence assessing whether or not a LGG will convert to high grade is of great importance in treatment. In this study, we use texture and histogram analyses on preoperative MRI FLAIR and ADC images to predict malignant transformation from low grade to higher grade glioma, as well as to discriminate between astrocytoma and oligodendroglioma. Based on the receiver operating characteristic (ROC) curves from training data, texture analysis had a higher area under the curve (AUC) value than histogram parameters, and it also more accurately predicted whether LGGs would convert and discriminated between astrocytoma and oligodendroglioma in the testing data. Texture analysis on conventional preoperative FLAIR and ADC images can accurately predict malignant transformation of low grade gliomas, as well as discriminate between astrocytoma and oligodendroglioma.

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