The aim of this study was to implement a deep-learning approach for automatic therapy response assessment in patients with high-grade-glioma (HGG), based on the response-assessment in neuro-oncology (RANO) criteria. A total of 135 conventional MRI scans from 67 patients were included. A neural network with a U-net architecture was trained for identification and subsegmentation of lesion components. The similarity coefficient score between segmentation results and ground truth was 0.88±0.06. Consistency in therapy response assessment was obtained in the majority of cases. These results demonstrate the potential applicability of the proposed method for automatic therapy response assesment in patients with HGG.