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. 2025 Jun 4;30(6):oyaf127.
doi: 10.1093/oncolo/oyaf127.

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

Caroline Zerbib  1 , Lucas Robinet  1   2   3 , Soleakhena Ken  4 , Ana Cavillon  5 , Margaux Roques  6 , Delphine Larrieu  7 , Aurore Siegfried  2   8 , Franck Emmanuel Roux  9   10   11 , Ahmad Berjaoui  3 , Elizabeth Cohen-Jonathan Moyal  1   2   11
Affiliations

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

Caroline Zerbib et al. Oncologist. .

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.

Keywords: artificial intelligence and machine learning; clinical imaging; clinical outcome; clinical radiotherapeutic studies; deep learning; molecular glioblastoma; tumor staging MRI.

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Conflict of interest statement

E.C.J.M. served as an expert board member for Novocure and received lecture fees from Accuray and Novocure, travel expenses from Novocure, and research grants from Astra Zeneca, Novocure, Bayer, and Incyte. She also received research grants from the ARC Foundation. All the other authors have no conflict of interest. No disclosures are reported by the other authors.

Figures

Figure 1.
Figure 1.
“Molecular glioblastoma” in the WHO classification of gliomas. Histologic Glioblastoma (Hist) refers to classic glioblastomas defined as IDH wild-type grade 4 astrocytomas with necrosis and vascular proliferation. “Molecular Glioblastoma” refers to IDH wild-type diffuse gliomas WHO grade 2 and 3 that meet molecular criteria making them glioblastoma-like tumors, thus classifying them as grade 4, with or without gadolinium enhancement, resembling either low- or high-grade tumors on MRI.
Figure 2.
Figure 2.
(A) Grade 2 astrocytoma, A1 in T2-FLAIR sequence, A2 T1 postgadolinium sequence. (B) Molecular glioblastoma, B1 in T2-FLAIR sequence, B2 in T1 postgadolinium sequence.
Figure 3.
Figure 3.
(A) OS and PFS between molGB and histGB, in univariable analysis. (B) OS and PFS between molGB without CE, histGB and molGB with CE, in univariable analysis.
See this image and copyright information in PMC

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