Differentiation of Glioblastoma and Brain Metastases by MRI-Based Oxygen Metabolomic Radiomics and Deep Learning

Stadlbauer A, Heinz G, Marhold F, Meyer-Baese A, Ganslandt O, Buchfelder M, Oberndorfer S (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Metabolites MDPI

Book Volume: 12

Journal Issue: 12

DOI: 10.3390/metabo12121264

Abstract

Glioblastoma (GB) and brain metastasis (BM) are the most frequent types of brain tumors in adults. Their therapeutic management is quite different and a quick and reliable initial characterization has a significant impact on clinical outcomes. However, the differentiation of GB and BM remains a major challenge in today's clinical neurooncology due to their very similar appearance in conventional magnetic resonance imaging (MRI). Novel metabolic neuroimaging has proven useful for improving diagnostic performance but requires artificial intelligence for implementation in clinical routines. Here; we investigated whether the combination of radiomic features from MR-based oxygen metabolism ("oxygen metabolic radiomics") and deep convolutional neural networks (CNNs) can support reliably pre-therapeutic differentiation of GB and BM in a clinical setting. A self-developed one-dimensional CNN combined with radiomic features from the cerebral metabolic rate of oxygen (CMRO2) was clearly superior to human reading in all parameters for classification performance. The radiomic features for tissue oxygen saturation (mitoPO(2); i.e., tissue hypoxia) also showed better diagnostic performance compared to the radiologists. Interestingly, both the mean and median values for quantitative CMRO2 and mitoPO(2) values did not differ significantly between GB and BM. This demonstrates that the combination of radiomic features and DL algorithms is more efficient for class differentiation than the comparison of mean or median values. Oxygen metabolic radiomics and deep neural networks provide insights into brain tumor phenotype that may have important diagnostic implications and helpful in clinical routine diagnosis.

Authors with CRIS profile

Oliver Thomas Ganslandt
Michael Buchfelder Lehrstuhl für Neurochirurgie

Involved external institutions

Karl Landsteiner Privatuniversität für Gesundheitswissenschaften GmbH AT Austria (AT) Florida State University (FSU) US United States (USA) (US) Katharinenhospital Stuttgart DE Germany (DE)

How to cite

APA:

Stadlbauer, A., Heinz, G., Marhold, F., Meyer-Baese, A., Ganslandt, O., Buchfelder, M., & Oberndorfer, S. (2022). Differentiation of Glioblastoma and Brain Metastases by MRI-Based Oxygen Metabolomic Radiomics and Deep Learning. Metabolites, 12(12). https://dx.doi.org/10.3390/metabo12121264

MLA:

Stadlbauer, Andreas, et al. "Differentiation of Glioblastoma and Brain Metastases by MRI-Based Oxygen Metabolomic Radiomics and Deep Learning." Metabolites 12.12 (2022).

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