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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.