MRI phenotypes of glioblastomas early after treatment are suggestive of overall patient survival

Author:

Schmitz-Abecassis Bárbara12ORCID,Dirven Linda34ORCID,Jiang Janey5,Keller Jasmin A1ORCID,Croese Robert J I34ORCID,van Dorth Daniëlle1ORCID,Ghaznawi Rashid3ORCID,Kant Ilse M J67ORCID,Taphoorn Martin J B134ORCID,van Osch Matthias J P2ORCID,Koekkoek Johan A F34ORCID,de Bresser Jeroen1ORCID

Affiliation:

1. C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center , Leiden , The Netherlands

2. Medical Delta , South-Holland , The Netherlands

3. Department of Neurology, Leiden University Medical Center , Leiden , The Netherlands

4. Department of Neurology, Haaglanden Medical Center , The Hague , The Netherlands

5. Department of Radiology, HagaZiekenhuis , The Hague , The Netherlands

6. Clinical Artificial Intelligence Implementation and Research Lab (CAIRELab) and Department of Information Technology & Digital Innovation, Leiden University Medical Center , Leiden , The Netherlands

7. Department of Digital Health, University Medical Center Utrecht , Utrecht , The Netherlands

Abstract

Abstract Background Distinguishing true tumor progression (TP) from treatment-induced abnormalities (eg, pseudo-progression (PP) after radiotherapy) on conventional MRI scans remains challenging in patients with a glioblastoma. We aimed to establish brain MRI phenotypes of glioblastomas early after treatment by combined analysis of structural and perfusion tumor characteristics and assessed the relation with recurrence rate and overall survival time. Methods Structural and perfusion MR images of 67 patients at 3 months post-radiotherapy were visually scored by a neuroradiologist. In total 23 parameters were predefined and used for hierarchical clustering analysis. Progression status was assessed based on the clinical course of each patient 9 months after radiotherapy (or latest available). Multivariable Cox regression models were used to determine the association between the phenotypes, recurrence rate, and overall survival. Results We established 4 subgroups with significantly different tumor MRI characteristics, representing distinct MRI phenotypes of glioblastomas: TP and PP rates did not differ significantly between subgroups. Regression analysis showed that patients in subgroup 1 (characterized by having mostly small and ellipsoid nodular enhancing lesions with some hyper-perfusion) had a significant association with increased mortality at 9 months (HR: 2.6 (CI: 1.1–6.3); P = .03) with a median survival time of 13 months (compared to 22 months of subgroup 2). Conclusions Our study suggests that distinct MRI phenotypes of glioblastomas at 3 months post-radiotherapy can be indicative of overall survival, but does not aid in differentiating TP from PP. The early prognostic information our method provides might in the future be informative for prognostication of glioblastoma patients.

Funder

Medical Delta, Cancer Diagnostics

Publisher

Oxford University Press (OUP)

Subject

Surgery,Oncology,Neurology (clinical)

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