{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T12:06:40Z","timestamp":1790510800679,"version":"4.1.0"},"reference-count":73,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T00:00:00Z","timestamp":1670976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Lower Austrian Provincial Health Agency (N\u00d6 LGA)"},{"name":"Karl Landsteiner University of Health Sciences, Seed Funding Project (Forschungsimpulse) SF45"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Metabolites"],"abstract":"<jats:p>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\u2019s 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 (\u201coxygen metabolic radiomics\u201d) 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 (mitoPO2; i.e., tissue hypoxia) also showed better diagnostic performance compared to the radiologists. Interestingly, both the mean and median values for quantitative CMRO2 and mitoPO2 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.<\/jats:p>","DOI":"10.3390\/metabo12121264","type":"journal-article","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T01:52:30Z","timestamp":1671069150000},"page":"1264","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Differentiation of Glioblastoma and Brain Metastases by MRI-Based Oxygen Metabolomic Radiomics and Deep Learning"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8348-2620","authenticated-orcid":false,"given":"Andreas","family":"Stadlbauer","sequence":"first","affiliation":[{"name":"Institute of Medical Radiology, University Clinic St. P\u00f6lten, Karl Landsteiner University of Health Sciences, 3100 St. P\u00f6lten, Austria"},{"name":"Department of Neurosurgery, Friedrich-Alexander University (FAU) Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gertraud","family":"Heinz","sequence":"additional","affiliation":[{"name":"Institute of Medical Radiology, University Clinic St. P\u00f6lten, Karl Landsteiner University of Health Sciences, 3100 St. P\u00f6lten, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8338-8060","authenticated-orcid":false,"given":"Franz","family":"Marhold","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, University Clinic of St. P\u00f6lten, Karl Landsteiner University of Health Sciences, 3100 St. P\u00f6lten, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anke","family":"Meyer-B\u00e4se","sequence":"additional","affiliation":[{"name":"Department of Scientific Computing, Florida State University, 400 Dirac Science Library Tallahassee, Tallahassee, FL 32306, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliver","family":"Ganslandt","sequence":"additional","affiliation":[{"name":"Clinic for Neurosurgery, Katharinenhospital Stuttgart, 70174 Stuttgart, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Buchfelder","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Friedrich-Alexander University (FAU) Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4229-8920","authenticated-orcid":false,"given":"Stefan","family":"Oberndorfer","sequence":"additional","affiliation":[{"name":"Department of Neurology, University Clinic of St. P\u00f6lten, Karl Landsteiner University of Health Sciences, 3100 St. P\u00f6lten, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"v1","DOI":"10.1093\/neuonc\/noz150","article-title":"CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2012\u20132016","volume":"21","author":"Ostrom","year":"2019","journal-title":"Neuro Oncol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1038\/s41582-019-0220-2","article-title":"Genetic and molecular epidemiology of adult diffuse glioma","volume":"15","author":"Molinaro","year":"2019","journal-title":"Nat. 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