{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Akbari H"],"funding":["NCATS","NCI","ITMAT","NINDS NIH HHS","NCI NIH HHS","NIH","NINDS"],"pagination":["1068-1079"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6280148"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["20(8)"],"pubmed_abstract":["<h4>Background</h4>Epidermal growth factor receptor variant III (EGFRvIII) is a driver mutation and potential therapeutic target in glioblastoma. Non-invasive in vivo EGFRvIII determination, using clinically acquired multiparametric MRI sequences, could assist in assessing spatial heterogeneity related to EGFRvIII, currently not captured via single-specimen analyses. We hypothesize that integration of subtle, yet distinctive, quantitative imaging/radiomic patterns using machine learning may lead to non-invasively determining molecular characteristics, and particularly the EGFRvIII mutation.<h4>Methods</h4>We integrated diverse imaging features, including the tumor's spatial distribution pattern, via support vector machines, to construct an imaging signature of EGFRvIII. This signature was evaluated in independent discovery (n = 75) and replication (n = 54) cohorts of de novo glioblastoma, and compared with the EGFRvIII status obtained through an assay based on next-generation sequencing.<h4>Results</h4>The cross-validated accuracy of the EGFRvIII signature in classifying the mutation status in individual patients of the independent discovery and replication cohorts was 85.3% (specificity = 86.3%, sensitivity = 83.3%, area under the curve [AUC] = 0.85) and 87% (specificity = 90%, sensitivity = 78.6%, AUC = 0.86), respectively. The signature was consistent with EGFRvIII+ tumors having increased neovascularization and cell density, as well as a distinctive spatial pattern involving relatively more frontal and parietal regions compared with EGFRvIII- tumors.<h4>Conclusions</h4>An imaging signature of EGFRvIII was found, revealing a complex, yet distinct macroscopic glioblastoma phenotype. By non-invasively capturing the tumor in its entirety, the proposed methodology can assist in evaluating the tumor's spatial heterogeneity, hence overcoming common spatial sampling limitations of tissue-based analyses. This signature can preoperatively stratify patients for EGFRvIII-targeted therapies, and potentially monitor dynamic mutational changes during treatment."],"journal":["Neuro-oncology"],"pubmed_title":["In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature."],"pmcid":["PMC6280148"],"funding_grant_id":["U24CA189523","R01 NS042645","U24 CA189523","UL1TR001878","R01NS042645"],"pubmed_authors":["Davatzikos C","Bakas S","Akbari H","Martinez-Lage M","Dahmane N","Rozycki M","O'Rourke DM","Pisapia JM","Nasrallah MP","Morrissette JJD"],"additional_accession":[]},"is_claimable":false,"name":"In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature.","description":"<h4>Background</h4>Epidermal growth factor receptor variant III (EGFRvIII) is a driver mutation and potential therapeutic target in glioblastoma. Non-invasive in vivo EGFRvIII determination, using clinically acquired multiparametric MRI sequences, could assist in assessing spatial heterogeneity related to EGFRvIII, currently not captured via single-specimen analyses. We hypothesize that integration of subtle, yet distinctive, quantitative imaging/radiomic patterns using machine learning may lead to non-invasively determining molecular characteristics, and particularly the EGFRvIII mutation.<h4>Methods</h4>We integrated diverse imaging features, including the tumor's spatial distribution pattern, via support vector machines, to construct an imaging signature of EGFRvIII. This signature was evaluated in independent discovery (n = 75) and replication (n = 54) cohorts of de novo glioblastoma, and compared with the EGFRvIII status obtained through an assay based on next-generation sequencing.<h4>Results</h4>The cross-validated accuracy of the EGFRvIII signature in classifying the mutation status in individual patients of the independent discovery and replication cohorts was 85.3% (specificity = 86.3%, sensitivity = 83.3%, area under the curve [AUC] = 0.85) and 87% (specificity = 90%, sensitivity = 78.6%, AUC = 0.86), respectively. The signature was consistent with EGFRvIII+ tumors having increased neovascularization and cell density, as well as a distinctive spatial pattern involving relatively more frontal and parietal regions compared with EGFRvIII- tumors.<h4>Conclusions</h4>An imaging signature of EGFRvIII was found, revealing a complex, yet distinct macroscopic glioblastoma phenotype. By non-invasively capturing the tumor in its entirety, the proposed methodology can assist in evaluating the tumor's spatial heterogeneity, hence overcoming common spatial sampling limitations of tissue-based analyses. This signature can preoperatively stratify patients for EGFRvIII-targeted therapies, and potentially monitor dynamic mutational changes during treatment.","dates":{"release":"2018-01-01T00:00:00Z","publication":"2018 Jul","modification":"2025-04-19T22:50:32.209Z","creation":"2019-07-25T07:11:30Z"},"accession":"S-EPMC6280148","cross_references":{"pubmed":["29617843"],"doi":["10.1093/neuonc/noy033"]}}