<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Eberlin LS</submitter><funding>NIBIB NIH HHS</funding><funding>NCRR NIH HHS</funding><funding>NINDS NIH HHS</funding><funding>NCI NIH HHS</funding><funding>NIH HHS</funding><pagination>1611-6</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC3562800</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>110(5)</volume><pubmed_abstract>The main goal of brain tumor surgery is to maximize tumor resection while preserving brain function. However, existing imaging and surgical techniques do not offer the molecular information needed to delineate tumor boundaries. We have developed a system to rapidly analyze and classify brain tumors based on lipid information acquired by desorption electrospray ionization mass spectrometry (DESI-MS). In this study, a classifier was built to discriminate gliomas and meningiomas based on 36 glioma and 19 meningioma samples. The classifier was tested and results were validated for intraoperative use by analyzing and diagnosing tissue sections from 32 surgical specimens obtained from five research subjects who underwent brain tumor resection. The samples analyzed included oligodendroglioma, ast</pubmed_abstract><journal>Proceedings of the National Academy of Sciences of the United States of America</journal><pubmed_title>Ambient mass spectrometry for the intraoperative molecular diagnosis of human brain tumors.</pubmed_title><pmcid>PMC3562800</pmcid><funding_grant_id>K08 NS064168</funding_grant_id><funding_grant_id>P41EB015898</funding_grant_id><funding_grant_id>K08NS064168</funding_grant_id><funding_grant_id>1DP2OD007383-01</funding_grant_id><funding_grant_id>P41 EB015898</funding_grant_id><funding_grant_id>R21 EB009459</funding_grant_id><funding_grant_id>1R21EB009459</funding_grant_id><funding_grant_id>P41 RR019703</funding_grant_id><funding_grant_id>P41RR019703</funding_grant_id><funding_grant_id>R25 CA089017</funding_grant_id><funding_grant_id>DP2 OD007383</funding_grant_id><pubmed_authors>Eberlin LS</pubmed_authors><pubmed_authors>Ligon KL</pubmed_authors><pubmed_authors>Golby AJ</pubmed_authors><pubmed_authors>Orringer D</pubmed_authors><pubmed_authors>Dunn IF</pubmed_authors><pubmed_authors>Norton I</pubmed_authors><pubmed_authors>Jolesz FA</pubmed_authors><pubmed_authors>Cooks RG</pubmed_authors><pubmed_authors>Liu X</pubmed_authors><pubmed_authors>Santagata S</pubmed_authors><pubmed_authors>Jarmusch AK</pubmed_authors><pubmed_authors>Agar NY</pubmed_authors><pubmed_authors>Ide JL</pubmed_authors></additional><is_claimable>false</is_claimable><name>Ambient mass spectrometry for the intraoperative molecular diagnosis of human brain tumors.</name><description>The main goal of brain tumor surgery is to maximize tumor resection while preserving brain function. However, existing imaging and surgical techniques do not offer the molecular information needed to delineate tumor boundaries. We have developed a system to rapidly analyze and classify brain tumors based on lipid information acquired by desorption electrospray ionization mass spectrometry (DESI-MS). In this study, a classifier was built to discriminate gliomas and meningiomas based on 36 glioma and 19 meningioma samples. The classifier was tested and results were validated for intraoperative use by analyzing and diagnosing tissue sections from 32 surgical specimens obtained from five research subjects who underwent brain tumor resection. The samples analyzed included oligodendroglioma, ast</description><dates><release>2013-01-01T00:00:00Z</release><publication>2013 Jan</publication><modification>2026-07-16T16:32:13.003Z</modification><creation>2026-07-09T11:10:19.598Z</creation></dates><accession>S-EPMC3562800</accession><cross_references><pubmed>23300285</pubmed><doi>10.1073/pnas.1215687110</doi></cross_references></HashMap>