BACKGROUND AND PURPOSE: Imaging assessment of an immunotherapy response in
glioblastoma is challenging due to overlap in the appearance of treatment-related changes …

Introduction We describe the imaging findings encountered in GBM patients receiving
immune checkpoint blockade and assess the potential of quantitative MRI biomarkers to …

Background For Glioblastoma (GBM), various prognostic nomograms have been proposed.
This study aims to evaluate machine learning models to predict patients' overall survival …

Objective Monitoring biomarkers using machine learning (ML) may determine glioblastoma
treatment response. We systematically reviewed quality and performance accuracy of …

Background This study aimed to identify a series of prognostically relevant immune features
by immunophenoscore. Immune features were explored using MRI radiomics features to …

Background Physiologic changes quantified by diffusion and perfusion MRI have shown
utility in predicting treatment response in glioblastoma (GBM) patients treated with cytotoxic …

Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adult
patients with a median survival of around one year. Prediction of survival outcomes in GBM …

BACKGROUND AND PURPOSE: Patient survival in high-grade glioma remains poor,
despite the recent developments in cancer treatment. As new chemo-, targeted molecular …

AIM To investigate machine learning based models combining clinical, radiomic, and
molecular information to distinguish between early true progression (tPD) and …

AIM To review how machine learning (ML) is applied to imaging biomarkers in neuro-
oncology, in particular for diagnosis, prognosis, and treatment response monitoring …