Taha Hashmi

Background:  Glioblastoma (GBM) remains the most aggressive primary malignant brain tumor in adults. Standard multimodal therapy results in a median overall survival of about 14.6 months¹. Current treatment strategies emphasize resection, radiotherapy, and systemic therapy. However, these modalities are often treated as separate domains rather than recognized for their interdependence in determining long-term outcomes. This compartmentalized approach fails to fully balance tumor cytoreduction with preservation of the structural and functional connectome². The result may be neurocognitive decline, diminished quality of life, and loss of vocational identity³. While prognostic factors have been identified across many domains, an integrated, patient-specific decision model is still lacking. This review evaluates evidence supporting a GBM scoring algorithm designed to predict both survival and functional preservation.

Methods:  A scoping literature review was performed using PubMed, Google Scholar, and ClinicalTrials.gov. These consisted of clinical trials, observational studies, and prospective protocols published between 2020 and 2026. The search strategy included terms such as “supratentorial glioma,” “residual volume thresholds”, “subcortical stimulation mapping”, “HCP-180 parcellation”, “proton beam RBE/LETd”, “MGMT levetiracetam”, “subjective cognitive functioning”, and “vocational reintegration”.

Results:  Analysis of the study cohort identified several variables valuable to an integrated prognostic framework. A residual volume below 2 cm³ was linked to longer survival⁴. A wide ventricular opening (VOW) of at least 23.2 mm was a strong prognostic signal (Hazard Ratio (HR): 3.67), possibly because it disrupts periventricular stem cell niches⁵. Subcortical stimulation mapping with the 1 mA rule increased gross total resection rates from 63% to 91%⁶. In radiation planning, proton therapy reduced grade 2 or higher toxicity from 21% to 10% in the first 4 months¹. Optimized VMAT decreased the median cerebellar dose by 37% compared to helical tomotherapy⁷. Levetiracetam (LEV) improved survival specifically in MGMT-methylated tumors (HR: 0.174)⁸. Antidepressant initiation during maintenance therapy (Cycle 4) was linked to worse prognosis (HR: 1.32), likely due to reverse causation of clinical decline⁷. Biology-guided deep learning classified tumor microenvironments with 84% accuracy⁹. Early post-radiotherapy MRI-based models predicted survival beyond 8 months with an AUC of 0.93¹⁰. Eight years after diagnosis, the probability of returning to work was only 52%³. Mental fatigue and use of multiple antiepileptic drugs were stronger barriers to employment than tumor grade alone³.

Conclusions:  Effective GBM prognostication should integrate survival, connectomic, neurocognitive, and socioeconomic variables. Relying on only oncologic or radiographic endpoints is not enough. These findings support the development of a GBM scoring algorithm as a clinical decision-support tool that balances survival with cognitive and functional preservation. Future studies should validate integrated multidomain models and test them in patient-specific treatment planning.

Works Cited

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