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Abstract #1728

Multiparametric MRI Texture Analysis in Prediction of Genetic Biomarkers in Patients with Brain Glioma 

Shingo Kihira1, Nadejda Tsankova2, Adam Bauer3, Yu Sakai1, Nicole Zubizarreta4, Jane Houldsworth2, Fahad Khan2, Adilia Hormigo5, Constantinos Hadjipanayis6, and Kambiz Nael1
1Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States, 2Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, United States, 3Radiology, Kaiser Permanente Fontana Medical Center, Fontana, CA, United States, 4Institute for Health Care Delivery Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States, 5Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States, 6Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, United States

In this retrospective study, we used a commercially available texture analysis software (Olea Medical) to construct a multiparametric MRI radiomic model that can be used to predict several important prognostic biomarkers in a cohort of patients with brain glioma. A total of 92 texture features were calculated from both FLAIR and T1C+ images using a volume-of-interest analysis encompassing the entire FLAIR hyperintense tumor. Radiomic features obtained from our multiparametric MR texture model were able to predict genetic biomarkers of brain glioma with predictive accuracies ranging from modest (62.4%) for MGMT to nearing 90% for IDH-1 and ARTX.

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