Glioblastoma, the most common and aggressive adult brain tumor, is considered non-curative at diagnosis. Current literature shows promise on imaging-based overall survival prediction for patients with glioblastoma while integrating advanced (structural, perfusion, and diffusion) multiparametric magnetic resonance imaging (AdvmpMRI). However, most patients prior to initiation of therapy typically undergo only basic structural mpMRI (BasmpMRI, i.e., T1,T1-Gd,T2,T2-FLAIR) pre-operatively, rather than Adv-mpMRI. Here we assess a retrospective cohort of 101 glioblastoma patients with available Adv-mpMRI from a previous study, which has shown that an initial feature panel (IFP) extracted from Adv-mpMRI can yield accurate overall survival stratification. We further focus on demonstrating that equally accurate prediction models can be constructed using augmented feature panels (AFP) extracted solely from Bas-mpMRI, obviating the need for using Adv-mpMRI. The classification accuracy of the model utilizing Adv-mpMRI protocols and the IFP was 72.77%, and improved to 74.26% when utilizing the AFP on Bas-mpMRI. Furthermore, Kaplan-Meier analysis demonstrated superior classification of subjects into short-, intermediate-, and long-survivor classes when using AFP on Basic-mpMRI. This quantitative evaluation indicates that accurate survival prediction in glioblastoma patients is feasible by using solely Bas-mpMRI and integrative radiomic analysis can compensate for the lack of Adv-mpMRI. Our finding holds promise for predicting overall survival based on commonly-acquired Bas-mpMRI, and hence for potential generalization across multiple institutions that may not have access to Adv-mpMRI, facilitating better patient selection.