Clinical outcome and deep learning imaging characteristics of patients treated by radio-chemotherapy for a "molecular" glioblastoma.

Abstract

Background: Since 2021, glioblastomas have been classified into two subgroups: classic glioblastomas (histGB), defined as IDH wild-type grade 4 astrocytomas with necrosis and vascular proliferation, showing contrast enhancement (CE) on MRI; and molecular glioblastomas (molGB), characterized by specific alterations (7+/10-, EGFR amplification, TERT mutation). Although not always the case, molGB often lack CE and may mimic low-grade gliomas (LGG), hence complicating the diagnosis. Survival outcomes remain debated. This study aimed to evaluate the response of molGB to standard treatment and assess the ability of machine learning and deep learning to differentiate molGB without CE from LGG on MRI.

Methods: We retrospectively studied 132 glioblastoma patients treated with radiotherapy and temozolomide, comparing the survival outcomes of histGB and molGB. Artificial intelligence (AI) models were trained using features from MRI FLAIR hypersignal segmentation to distinguish molGB without CE from LGG.

Results: No significant difference in median overall survival (OS) (20.6 vs 18.4 months, P = .2) or progression-free survival (10.1 vs 9.3 months, P = .183) was observed between molGB and histGB. However, molGB without CE demonstrated improved median OS (31.2 vs 18 months, hazard ratios 0.45). Artificial intelligence models distinguished molGB without CE from LGG, achieving a best-performing ROC AUC of 0.85.

Conclusions: While patients with molGB and histGB have similar overall survival, patients with molGB without CE appear to have better outcomes. Artificial intelligence models effectively differentiate molGB from LGG, supporting their potential diagnostic utility.