Keywords: Segmentation, Machine Learning/Artificial Intelligence, Glioblastoma, immunotherapy, radiotherapyConfounding appearance of radiographic changes in recurrent high grade glioma (HGG) patients treated with multimodality immunotherapy presents a challenge to the neuro-radiologist. A clinical need exists to improve upon conventional criteria for assessment of GBM on standard-of-care (SOC; T1w, T2w, FLAIR, and T1w-enhanced) MRI to help distinguish treatment-related effects from true disease progression. We have investigated the feasibility of intensity-based segmentation of tumor tissue types on multiparametric MRI (mpMRI) to inform response assessment in HGG patients treated with bevacizumab, hypofractionated stereotactic radiotherapy, and pembrolizumab.
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