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

Introduction Survival varies in patients with glioblastoma due to intratumoral heterogeneity
and radiomics/imaging biomarkers have potential to demonstrate heterogeneity. The …

Background Based on promising results from radiomic approaches to predict O6-
methylguanine DNA methyltransferase promoter methylation status (MGMT status) and …

Glioblastoma (GBM) is the most common adult glioma. Differentiating post-treatment effects
such as pseudoprogression from true progression is paramount for treatment. Radiomics …

Background and purpose We aimed to develop a clinically applicable prognosis prediction
model predicting overall survival (OS) and progression-free survival (PFS) for glioblastoma …

We conducted a feasibility study to predict malignant glioma grades via radiomic analysis
using contrast-enhanced T1-weighted magnetic resonance images (CE-T1WIs) and T2 …

Background The purpose of this study was to analyze the potential of radiomics for disease
stratification beyond key molecular, clinical, and standard imaging features in patients with …

Purpose To investigate whether radiomic features at MRI improve survival prediction in
patients with glioblastoma multiforme (GBM) when they are integrated with clinical and …

Abstract O6-Methylguanine-DNA-methyltransferase (MGMT) promoter methylation was
shown in many studies to be an important predictive biomarker for temozolomide (TMZ) …

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 …