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In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature

机译:通过复杂的多参数MRI签名对原发性胶质母细胞瘤患者EGFRvIII突变进行体内评估

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摘要

Background. 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.
机译:背景。表皮生长因子受体变体III(EGFRvIII)是胶质母细胞瘤的驱动程序突变和潜在的治疗靶标。使用临床上获得的多参数MRI序列进行的非侵入性体内EGFRvIII测定,可以帮助评估与EGFRvIII相关的空间异质性,目前尚无法通过单标本分析来捕获。我们假设使用机器学习对微妙而又独特的定量成像/放射学模式进行整合可能会导致非侵入性地确定分子特征,尤其是EGFRvIII突变。

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