{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T05:36:40Z","timestamp":1790746600006,"version":"4.1.0"},"reference-count":82,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T00:00:00Z","timestamp":1632873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Metabolites"],"abstract":"<jats:p>The tumor microenvironment is a critical regulator of cancer development and progression as well as treatment response and resistance in brain neoplasms. The available techniques for investigation, however, are not well suited for noninvasive in vivo characterization in humans. A total of 120 patients (59 females; 61 males) with newly diagnosed contrast-enhancing brain tumors (64 glioblastoma, 20 brain metastases, 15 primary central nervous system (CNS) lymphomas (PCNSLs), and 21 meningiomas) were examined with a previously established physiological MRI protocol including quantitative blood-oxygen-level-dependent imaging and vascular architecture mapping. Six MRI biomarker maps for oxygen metabolism and neovascularization were fused for classification of five different tumor microenvironments: glycolysis, oxidative phosphorylation (OxPhos), hypoxia with\/without neovascularization, and necrosis. Glioblastoma showed the highest metabolic heterogeneity followed by brain metastasis with a glycolysis-to-OxPhos ratio of approximately 2:1 in both tumor entities. In addition, glioblastoma revealed a significant higher percentage of hypoxia (24%) compared to all three other brain tumor entities: brain metastasis (7%; p &lt; 0.001), PCNSL (8%; p = 0.001), and meningioma (8%; p = 0.003). A more aggressive biological brain tumor behavior was associated with a higher percentage of hypoxia and necrosis and a lower percentage of remaining vital tumor tissue and aerobic glycolysis. The proportion of oxidative phosphorylation, however, was rather similar (17\u201326%) for all four brain tumor entities. Tumor microenvironment (TME) mapping provides insights into neurobiological differences of contrast-enhancing brain tumors and deserves further clinical cancer research attention. Although there is a long roadmap ahead, TME mapping may become useful in order to develop new diagnostic and therapeutic approaches.<\/jats:p>","DOI":"10.3390\/metabo11100668","type":"journal-article","created":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T00:03:42Z","timestamp":1632960222000},"page":"668","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Metabolic Tumor Microenvironment Characterization of Contrast Enhancing Brain Tumors Using Physiologic MRI"],"prefix":"10.3390","volume":"11","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 Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"}],"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":"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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6037-7534","authenticated-orcid":false,"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\/0000-0001-9330-9266","authenticated-orcid":false,"given":"Max","family":"Zimmermann","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Friedrich-Alexander University Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"},{"name":"Department of Preclinical Imaging and Radiopharmacy, University of T\u00fcbingen, 72076 T\u00fcbingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Buchfelder","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Friedrich-Alexander University Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elisabeth","family":"Heynold","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Friedrich-Alexander University Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4888-543X","authenticated-orcid":false,"given":"Thomas M.","family":"Kinfe","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Friedrich-Alexander University Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"},{"name":"Division of Functional Neurosurgery and Stereotaxy, Friedrich-Alexander University (FAU) Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,29]]},"reference":[{"key":"ref_1","first-page":"454","article-title":"Brain Tumor Detection Using Shape features and Machine Learning Algorithms Diagnosing ASD using machine learning View project Brain Tumor Detection Using Shape features and Machine Learning Algorithms","volume":"6","author":"George","year":"2015","journal-title":"Int. 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