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Abstract #3077

Testing Machine Learning Algorithms using Anisotropy Indices of Normal Appearing White Matter as Predictors of Molecular Grouping of Gliomas

Hande Halilibrahimoglu1, Korhan Polat2, Seda Keskin1, Oguzhan Aslan1, Ozan Genc2, Koray Ozduman1, Cengiz Yakicier1, Esin Ozturk Isik2, M. Necmettin Pamir1, Alp Dincer1, and Alpay Ozcan1

1Acibadem Mehmet Ali Aydinlar Univesity, Istanbul, Turkey, 2Bogazici University, Istanbul, Turkey

Grouping gliomas using the telomerase reverse transcriptase (TERT) gene and IDH mutations, and 1p/19q co-deletion status was demonstrated to be useful previously for clinical decisions. MR based radiogenomics might potentially be advantageous.

The aim of this study was to determine for the first time whether full distributions of the fractional anisotropy, relative anisotropy and ADC in normal appearing white matter were adequate predictors for machine learning algorithms to classify molecular subgroups based on TERT, IDH and 1p/19q co-deletion information.

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