Artificial Neural Network-Derived Cerebral Metabolic Rate of Oxygen for Differentiating Glioblastoma and Brain Metastasis in MRI: A Feasibility Study

Hakim Baazaoui,Sherif A Mohamed,Máté E Maros,Simon Hubertus,Alex Förster,Holger Wenz,Lothar R Schad

doi:10.3390/app11219928

Abstract

Glioblastoma may appear similar to cerebral metastasis on conventional MRI in some cases, but their therapies differ significantly. This prospective feasibility study was aimed at differentiating them by applying the quantitative susceptibility mapping and quantitative blood-oxygen-level-dependent (QSM + qBOLD) model to these entities for the first time. We prospectively included 15 untreated patients with glioblastoma (n = 7, median age: 68 years, range: 54–84 years) or brain metastasis (n = 8, median age 66 years, range: 50–78 years) who underwent preoperative MRI including multi-gradient echo and arterial spin labeling sequences. Oxygen extraction fraction (OEF), cerebral blood flow (CBF) and cerebral metabolic rate of oxygen (CMRO2) were calculated in the contrast-enhancing tumor (CET) and peritumoral non-enhancing T2 hyperintense region (NET2), using an artificial neural network. We demonstrated that OEF in CET was significantly lower (p = 0.03) for glioblastomas than metastases, all features were significantly higher (p = 0.01) in CET than in NET2 for metastasis patients only, and the ratios of CET/NET2 for CBF (p = 0.04) and CMRO2 (p = 0.01) were significantly higher in metastasis patients than in glioblastoma patients. Discriminative power of a support-vector machine classifier was highest with a combination of two features, yielding an area under the receiver operating characteristic curve of 0.94 with 93% diagnostic accuracy. QSM + qBOLD allows for robust differentiation of glioblastoma and cerebral metastasis while yielding insights into tumor oxygenation.

Highlights

Glioblastoma (GBM) and cerebral metastasis are the most common brain tumors in adult patients [1]
The Oxygen extraction fraction (OEF) map of a 54-year-old female patient with left occipito-temporal GBM supplied in Figure 3 gives an illustrative example of the oxygenation values emulated by the artificial neural network
No significant differences were found between contrast-enhancing tumor (CET) of GBM and cMET in terms of cerebral blood flow (CBF) (p = 0.33)

Summary

Introduction

Glioblastoma (GBM) and cerebral metastasis (cMET) are the most common brain tumors in adult patients [1]. Differentiating GBM and cMET based on their conventional magnetic resonance imaging (MRI) characteristics has proven difficult [2,3], as both tumor types can show necrotic centers, contrast-enhancing peripheral areas and peritumoral edema (Figure 1) [4]. Sci. 2021, 11, 9928 moral edema (Figure 1) [4]. Studies employing advanced MR-imaging techniques focusing on the tumor microenvironment and hypoxia-induced changes in the microvasculature foundmicroenvironment that an elevated cerebral blood flow (CBF)

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