宋双双,於帆,闫新亭,朴月善,徐建堃,卢洁.MRI预测脑胶质瘤异柠檬酸脱氢酶-1突变状态[J].中国医学影像技术,2019,35(11):1632~1637
MRI预测脑胶质瘤异柠檬酸脱氢酶-1突变状态
Value of MRI in prediction of glioma isocitrate dehydrogenase 1 mutation status
投稿时间:2019-01-10  修订日期:2019-09-03
DOI:10.13929/j.1003-3289.201901063
中文关键词:    胶质瘤  异柠檬酸脱氢酶1  磁共振成像  分子亚型
英文关键词:brain  gliomas  isocitrate dehydrogenase 1  magnetic resonance imaging  molecular subtypes
基金项目:国家自然科学基金优秀青年科学基金(81522021)、北京市医院管理局"登峰"计划专项经费(DFL20180802)。
作者单位E-mail
宋双双 首都医科大学宣武医院放射科, 北京 100053
磁共振成像脑信息学北京市重点实验室, 北京 100053 
 
於帆 首都医科大学宣武医院放射科, 北京 100053
磁共振成像脑信息学北京市重点实验室, 北京 100053 
 
闫新亭 首都医科大学宣武医院放射科, 北京 100053
磁共振成像脑信息学北京市重点实验室, 北京 100053 
 
朴月善 首都医科大学宣武医院病理科, 北京 100053  
徐建堃 首都医科大学宣武医院神经外科, 北京 100053  
卢洁 首都医科大学宣武医院放射科, 北京 100053
磁共振成像脑信息学北京市重点实验室, 北京 100053
首都医科大学宣武医院核医学科, 北京 100053 
imaginglu@hotmail.com 
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中文摘要:
      目的 探讨MR平扫和增强在异柠檬酸脱氢酶-1(IDH1)突变阳性或阴性脑胶质瘤诊断中的价值。方法 回顾性分析92例经病理确诊的脑胶质瘤患者术前MRI影像表现,分析IDH1突变阳性(IDH1突变阳性组)和阴性(IDH1突变阴性组)胶质瘤病变部位、信号、边界、生长模式、强化程度及瘤周水肿及程度的差异,并建立二分类Logistic模型。结果 2组不同级别胶质瘤及脑内病灶分布差异均有统计学意义(P均<0.05)。2组肿瘤信号、边界和生长模式差异无统计学意义(P=0.269、0.606、0.139),强化程度和瘤周水肿及程度差异均有统计学意义(P均<0.01)。经Logistic回归分析发现,脑胶质瘤的信号均匀性(X1)、边界(X2)及强化程度(X3)差异有统计学意义(P=0.004、0.037、0.001),回归方程为:logit(P)=2.668+1.415X1-2.097X2-3.229X3χ2=41.583,P<0.001),模型的敏感度为70.70%,特异度为80.40%。结论 MR平扫和增强扫描可清晰显示IDH1突变阳性和阴性脑胶质瘤的影像学特征,为脑胶质瘤IDH1突变状态的术前预测提供无创的影像学手段。
英文摘要:
      Objective To observe the value of MR in prediction of glioma isocitrate dehydrogenase (IDH) 1 mutation status. Methods Nineteen-two patients with glioma were divided into IDH mutation positive group and negative group, and their imaging characteristics were retrospectively reviewed, including lesions' site, signal intensity, boundary, growth pattern, degree of enhancement and surrounding edema. Then two-class Logistic model was established. Results There were significant differences between different grades and location of gliomas between the two groups (both P<0.05). There were no significant differences in tumor signal intensity, boundary and growth pattern (P=0.269, 0.606, 0.139). There were statistically significant difference in degree of enhancement and surrounding edema (all P<0.01). Logistic regression analysis showed that the signal uniformity (X1), boundary (X2) and degree of enhancement (X3) of gliomas were statistically significant (P=0.004, 0.037, 0.001), and the regression equation was:logit (P)=2.668+1.415X1-2.097X2-3.229X3 (χ2=41.583, P<0.001), the sensitivity of the model was 70.70%, and the specificity was 80.40%. Conclusion MRI can be used to non-invasively predict IDH1 mutation status of gliomas before surgical operation.
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