Abstract #0896

Prediction of Tumor Progression Time Interval in Malignant Glioma Using Textural Information derived from FLAIR and T1c MR-images and Machine Learning

Johannes Slotboom¹, Urspeter Knecht¹, Nuno Pedrosa de Barros¹, Timo Nannen², Martin Zbinden¹, Ekkehard Hewer³, Erik Vassella³, Alessia Pica⁴, Philippe Schucht⁵, Jürgen Beck⁵, Andreas Raabe⁵, Jan Gralla¹, Roland Wiest¹, and Marwan El-Koussy¹

¹Institute for Diagnostic and Interventional Neuroradiology, University Hosiptal Bern, Bern, Switzerland, ²Institute of Radiooncology, University Hosiptal Bern, Bern, Switzerland, ³Institute of Pathology, University Bern, Bern, Switzerland, ⁴Center for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland, ⁵Neurosurgery, University Hosiptal Bern, Bern, Switzerland

Glioma-patients get regular neuroradiological MRI-follow-ups to evaluate the tumor-progression-status. In this study it was investigated whether it is possible to predict tumor progression within the next follow-up period, from progression on a longer time-scale. The T_1c- and FLAIR-images of two times 20 patients were investigated; one group having stable-disease at two subsequent follow-ups (ST-ST), the second group showed stable-disease during the first but progressive-disease during the second follow-up (ST-PR). By applying machine-learning (random-forests) on textural MRI-information, short-term progression could be predicted with an accuracy of 77.5%. This novel type of information can have an impact on improved personalized-treatment of glioma-patients.

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