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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