摘要
目的探讨表观扩散系数(ADC)全域灰度直方图分析对原发侧脑室中枢神经细胞瘤(CN)、室管膜瘤(EP)、高级别胶质瘤(HGG)的鉴别诊断价值。方法收集2013年2月至2019年8月郑州大学第一附属医院经手术病理证实的45例患者的临床资料,其中CN 22例,EP 10例,HGG 13例。采用Mazda软件对3组肿瘤ADC图像逐层勾画感兴趣区并进行直方图分析,得到9个参数,即均值、方差、峰度、偏度和第1、10、50、90、99百分位数,比较3种肿瘤间各参数的差异,并采用受试者工作特征(ROC)曲线评价差异性参数对3种肿瘤的鉴别诊断效能。结果3组间9个参数中均值和第1、10、50、90百分位数总体差异有统计学意义(P<0.05),其中第10百分位数鉴别诊断CN与EP的ROC曲线下与坐标轴围成的面积(AUC)最大,为0.920,诊断敏感度为90.91%,特异度为87.50%;CN与HGG间,第1百分位数的AUC最大,为0.864,敏感度为86.36%,特异度为81.82%;EP与HGG组间各参数差异均无统计学意义(P>0.05)。结论ADC直方图分析有助于鉴别侧脑室CN、EP与HGG。
Objective To explore whole lesion histogram analysis of apparent diffusion coefficient(ADC)in identifying primary central neurocytoma,ependymoma and high-grade glioma in lateral ventricle.Methods The clinical data of 45 patients confirmed by surgery and pathology in the First Affiliated Hospital of Zhengzhou University from February 2013 to August 2019 were collected,including 22 cases of central neurocytoma(CN),10 cases of ependymoma(EP)and 13 cases of high-grade glioma(HGG).Mazda software was used to sketch ROI on ADC images of 3 groups of tumors layer by layer and histogram analysis was carried out to get following parameters:mean,variance,kurtosis,skewness and 1st,10th,50th,90th,99th percentiles.The differences of each parameter among 3 groups were compared and the ROC curves were generated to evaluate diagnosis efficiency of statistically significant parameters among 3 kinds of tumors.Results Among 3 groups,mean,1st,10th,50th,90th percentiles had statistically significant differences(P<0.05).The ROC curve of 10th percentile in differing CN from EP had the largest AUC(0.920),with diagnostic sensitivity of 90.91%and specificity of 87.50%.Between CN and HGG,the AUC of 1st percentile was the largest(0.864),with diagnostic sensitivity of 86.36%and specificity of 81.82%.There was no parameter that was statistically significant between EP and HGG.Conclusion Histogram analysis of ADC values is helpful in differentiating lateral ventricle CN,EP and HGG.
作者
李晓茗
张焱
彭媛媛
刘俊宏
LI Xiaoming;ZHANG Yan;PENG Yuanyuan;LIU Junhong(Department of MRI,the First Affiliated Hospital of Zhengzhou University,Zhengzhou 450052,China)
出处
《河南医学研究》
CAS
2021年第6期1001-1004,共4页
Henan Medical Research
基金
河南省医学科技攻关计划项目(201701011)。