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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, J├╝rgen 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 T1c- 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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