Multimodal MRI radiomics based on habitat subregions of the tumor microenvironment for predicting risk stratification in glioblastoma

Objective: Accurate prediction of glioblastoma (GBM) progression is essential for improving therapeutic interventions and outcomes. This study aimed to develop and validate an integrated clinical-radiomics model to predict overall survival (OS) and evaluate the risk of disease progression in patients with isocitrate dehydrogenase-wildtype GBM (IDH-wildtype GBM). Materials and Methods: The data of 423 IDH-wildtype GBM patients were retrospectively analyzed. Radiomic features were extracted from preoperatively acquired MR images. Least absolute shrinkage and selection operator-Cox proportional hazards (LASSO-Cox) regression was used to identify radiomic features significantly associated with OS and calculate a risk score and construct a radiomic signature for each patient. Kaplan‒Meier survival analysis and the log-rank test were used to compare survival between the high-risk and low-risk groups. A clinical‒radiomic model and a nomogram were developed on the basis of the results of multivariable Cox proportional hazards regression and were evaluated with the concordance index (C-index). Results: Radiomics models were developed on the basis of feature extracted from the three sub-regions individually, and a multiregional radiomics model was established by aggregating 16 features selected from these subregions. Kaplan-Meier survival analysis indicated that the high-risk group exhibited significantly worse outcomes than the low-risk group did (p