Purpose: Analyzing post-treatment MRIs from glioblastoma patients can be challenging due to similar radiological presentations of disease progression and treatment effects. Identifying radiomics features (RFs) revealing progressive glioblastoma can contribute to an improved evaluation of the response assessment.

Methods: 3 Tesla MRI data from 560 glioblastoma patients (mean age 58.1 years) after treatment according to Stupp's protocol were analyzed retrospectively. A total of 418 RFs were extracted from contrast-enhancing tumors, non-enhancing lesions, peritumoral regions (PeriCET) and normal-appearing white matter as regions of interest using PyRadiomics. Dataset was initially split into a training (70%) and a validation (30%) cohort. The training cohort was used for feature selection and model-optimization. Logistic regression was used as a machine-learning model to identify patients with progression-free survival (PFS) as defined by the RANO criteria at 6 and 12 months after treatment. Models were trained with (i) clinical features only, (ii) RFs only, and (iii) a combination of clinical and radiomics features. The performance of each model was evaluated with the validation cohort.

Results: The predictive performances of the model trained with only RFs from the PeriCET were AUC = 0.61 (95%-CI: 0.51-0.70) and AUC = 0.71 (95%-CI: 0.61-0.81) for 6-months and 12-months PFS respectively. Combining clinical and RFs from PeriCET resulted in overall best performance in predicting patients with progression within 12-months AUC = 0.75 (95%-CI: 0.65-0.85).

Conclusion: RFs from peritumoral region combined with clinical features including age, sex, and MGMT status can identify patients with 12-months PFS, suggesting the important role of peritumoral regions for the progression of glioblastoma.