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作 者:任琦[1] 程敬亮[1] 马桢[1] 张勇[1] REN Qi;CHENG Jingliang;MA Zhen;ZHANG Yong(Department of MRI,the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052,China)
机构地区:[1]郑州大学第一附属医院磁共振科,河南郑州450052
出 处:《中国介入影像与治疗学》2018年第10期610-614,共5页Chinese Journal of Interventional Imaging and Therapy
摘 要:目的探讨MRI增强灰度直方图鉴别诊断胶质母细胞瘤(GBM)、原发性中枢神经系统淋巴瘤(PCNSL)和单发脑转移瘤(SMT)的价值。方法回顾性分析29例GBM(GBM组)、38例PCNSL(PCNSL组)和28例SMT(SMT组)患者的MRI资料。分析肿瘤增强全域灰度直方图分布参数均值、方差、偏度、峰度及第1、10、50、90、99百分位数(Perc.01%、Perc.10%、Perc.50%、Perc.90%、Perc.99%),比较3组间参数差异。结果除Perc.99%外,3组间均值、方差、偏度、峰度、Perc.01%、Perc.10%、Perc.50%、Perc.90%差异均具有统计学意义。GBM组与PCNSL组、GBM组与SMT组、PCNSL组与SMT组间偏度差异有统计学意义,其鉴别诊断GBM与PCNSL、GBM与SMT、PCNSL与SMT的曲线下面积分别为0.97、0.70和0.87(P均<0.05)。结论MRI增强肿瘤全域灰度直方图参数可作为鉴别GBM、PCNSL和SMT的辅助方法,以偏度鉴别GBM和PCNSL的效能最高。Objective To explore the value of whole-tumor histogram analysis of enhanced-MRI in differential diagnosis of glioblastoma multiform(GBM),primary central nervous system lymphoma(PCNSL)and solitary brain metastasis(SMT).Methods Totally 29 patients of GBM(GBM group),38 of PCNSL(PCNSL group)and 28 of SMT(SMT group)underwent enhanced-MRI.The histogram distribution of gray-scale in enhanced tumors were obtained from the whole-tumor voxels in the contrast-enhanced lesions.The parameters including the mean,variance,kurtosis,skewness,the first percentile(Perc.01%),the 10th percentile(Perc.10%),the 50th percentile(Perc.50%),the 90th percentile(Perc.90%)and the 99th percentile(Perc.99%)were obtained and compared among 3 groups.Results Except for Perc.99%,the mean,variance,kurtosis,skewness,Perc.01%,Perc.10%,Perc.50%and Perc.90%had significant differences among 3 groups.Only skewness had statistical differences between GBM group and PCNSL group,GBM group and SMT group,as well as PCNSL group and SMT group.AUC of ROC curve of skewness in differentiating GBM group and PCNSL group,GBM group and SMT group,PCNSL group and SMT group were 0.97,0.70 and 0.87,respctively(all P<0.05).Conclusion The skewness from histogram analysis of enhanced-MRI facilitates the differentiation among GBM,SMT and PCNSL,especially in differentiating GBM and PCNSL.
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