{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T16:53:23Z","timestamp":1791219203437,"version":"4.1.0"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T00:00:00Z","timestamp":1737331200000},"content-version":"vor","delay-in-days":19,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"German Research Foundation","doi-asserted-by":"publisher","award":["FU 356\/12-2"],"award-info":[{"award-number":["FU 356\/12-2"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,1,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>This study aimed to develop an automated algorithm to noninvasively distinguish gliomas from other intracranial pathologies, preventing misdiagnosis and ensuring accurate analysis before further glioma assessment.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>A cohort of 1280 patients with a variety of intracranial pathologies was included. It comprised 218 gliomas (mean age 54.76\u2005\u00b1\u200513.74 years; 136 males, 82 females), 514 patients with brain metastases (mean age 59.28\u2005\u00b1\u200512.36 years; 228 males, 286 females), 366 patients with inflammatory lesions (mean age 41.94\u2005\u00b1\u200514.57 years; 142 males, 224 females), 99 intracerebral hemorrhages (mean age 62.68\u2005\u00b1\u200516.64 years; 56 males, 43 females), and 83 meningiomas (mean age 63.99\u2005\u00b1\u200513.31 years; 25 males, 58 females). Radiomic features were extracted from fluid-attenuated inversion recovery (FLAIR), contrast-enhanced, and noncontrast T1-weighted MR sequences. Subcohorts, with 80% for training and 20% for testing, were established for model validation. Machine learning models, primarily XGBoost, were trained to distinguish gliomas from other pathologies.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The study demonstrated promising results in distinguishing gliomas from various intracranial pathologies. The best-performing model consistently achieved high area-under-the-curve (AUC) values, indicating strong discriminatory power across multiple distinctions, including gliomas versus metastases (AUC\u2005=\u20050.96), gliomas versus inflammatory lesions (AUC\u2005=\u20051.0), gliomas versus intracerebral hemorrhages (AUC\u2005=\u20050.99), gliomas versus meningiomas (AUC\u2005=\u20050.98). Additionally, across all these entities, gliomas had an AUC of 0.94.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>The study presents an automated approach that effectively distinguishes gliomas from common intracranial pathologies. This can serve as a quality control upstream to further artificial-intelligence-based genetic analysis of cerebral gliomas.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/noajnl\/vdae225","type":"journal-article","created":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T12:46:51Z","timestamp":1737377211000},"source":"Crossref","is-referenced-by-count":6,"title":["AI-guided virtual biopsy: Automated differentiation of cerebral gliomas from other benign and malignant MRI findings using deep learning"],"prefix":"10.1093","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0719-2207","authenticated-orcid":false,"given":"Mathias","family":"Holtkamp","sequence":"first","affiliation":[{"name":"Institute for Artificial Intelligence in Medicine, University Hospital Essen ,","place":["Germany"]},{"name":"Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen , Essen 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Neuroradiology, University Hospital Essen , Essen ,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Forsting","sequence":"additional","affiliation":[{"name":"Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen , Essen ,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felix","family":"Nensa","sequence":"additional","affiliation":[{"name":"Institute for Artificial Intelligence in Medicine, University Hospital Essen ,","place":["Germany"]},{"name":"Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen , Essen ,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lale","family":"Umutlu","sequence":"additional","affiliation":[{"name":"Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen , Essen 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