增强MRI全域直方图分析在鉴别间变型与非间变型室管膜瘤中的价值
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摘要
目的研究增强MRI全域直方图分析在鉴别间变型与非间变型室管膜瘤中的价值。方法回顾性分析经手术病理证实的46例室管膜瘤患者,其中非间变型室管膜瘤28例,间变型室管膜瘤18例,分别在两组MRI增强矢状位图像上每一层肿瘤层面用Mazda软件勾画感兴趣区并进行灰度全域直方图分析,对获得的两组直方图参数特征分别进行统计学分析,比较各参数的统计学意义。结果通过灰度全域直方图分析得到的9个参数中,变异度(Variance)、峰度(Kurtosis)、第90百分位数(Perc.90%)及第99百分位数(Perc.99%)这4个参数差异有统计学意义(P值均<0.05),其ROC曲线下面积分别为0.668、0.256、0.679、0.683,变异度鉴别两组肿瘤的敏感性、特异性分别为83.3%和60.7%;第90百分位数分别为88.9%和60.7%;第99百分位数分别为88.9%和60.7%。变异度、第90百分位数、第99百分位数鉴别肿瘤复发的临界值分别为428.7、130.6、146.2。结论磁共振灰度全域直方图分析对鉴别间变型与非间变型室管膜瘤存在一定价值。
Objective To investigate the value of enhanced MRI global histogram analysis in the differential diagnosis of anaplastic and non-anaplastic ependymoma. Methods A retrospective analysis of 46 patients with ependymoma confirmed by operation and pathology, including 28 cases of nonanaplastic ependymoma and 18 cases of anaplastic ependymoma, were performed on the MRI-enhanced sagittal images of the two groups. At the tumor level, Mazda software was used to delineate the region of interest and perform gray-scale global histogram analysis. The obtained two sets of histogram parameters were statistically analyzed, and the statistical significance of each parameter was compared. Results Among the nine parameters obtained by gray-scale global histogram analysis, Variance, Kurtosis, 90 th percentile(Perc.90%) and 99 th percentile(Perc.99) %) The difference of these four parameters was statistically significant(P<0.05), and the area under the ROC curve was 0.668, 0.256, 0.679, and 0.683, respectively. The sensitivity and specificity of the two groups of tumors were 83.3%. And 60.7%; the 90 th percentile is 88.9% and 60.7%, respectively; the 99 th percentile is 88.9% and 60.7%, respectively. The variability, the 90 th percentile, and the 99 th percentile identified the critical values for tumor recurrence as 428.7, 130.6, and 146.2, respectively. Conclusion Magnetic resonance gamma global histogram analysis has certain value in the identification of anaplastic and non-anaplastic ependymoma.
Objective To investigate the value of enhanced MRI global histogram analysis in the differential diagnosis of anaplastic and non-anaplastic ependymoma. Methods A retrospective analysis of 46 patients with ependymoma confirmed by operation and pathology, including 28 cases of nonanaplastic ependymoma and 18 cases of anaplastic ependymoma, were performed on the MRI-enhanced sagittal images of the two groups. At the tumor level, Mazda software was used to delineate the region of interest and perform gray-scale global histogram analysis. The obtained two sets of histogram parameters were statistically analyzed, and the statistical significance of each parameter was compared. Results Among the nine parameters obtained by gray-scale global histogram analysis, Variance, Kurtosis, 90 th percentile(Perc.90%) and 99 th percentile(Perc.99) %) The difference of these four parameters was statistically significant(P<0.05), and the area under the ROC curve was 0.668, 0.256, 0.679, and 0.683, respectively. The sensitivity and specificity of the two groups of tumors were 83.3%. And 60.7%; the 90 th percentile is 88.9% and 60.7%, respectively; the 99 th percentile is 88.9% and 60.7%, respectively. The variability, the 90 th percentile, and the 99 th percentile identified the critical values for tumor recurrence as 428.7, 130.6, and 146.2, respectively. Conclusion Magnetic resonance gamma global histogram analysis has certain value in the identification of anaplastic and non-anaplastic ependymoma.
引文
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2 陈利军,陈士新,李维华,等.脑实质室管膜瘤MRI表现与病理的相关性研究[J].中国医学影像学杂志,2012,20:105-108.
3 Poretti A,Meoded A,Huisman TA.Neuroimaging of pediatric posterior fossa tumors including review of the literature[J].J Magn Reson Imaging,2012,35:32-47.
4 Tozer DJ,Jager HR,Danchaivijitr N,et al.Apparent diffusion coefficient histograms may predict low-grade glioma subtype[J].NMR Biomed,2007,20:49-57.
5 刘杨颖秋,尚劲,田诗云,等.利用肿瘤全域表观扩散系数信号强度直方图鉴别II级与III级胶质瘤[J].磁共振成像杂志,2017,8:276-282.
6 Chen X,Wei X,Zhang Z,et al.Differentiation of tru-progression from pseudoprogression in glioblastoma treated with radiation therapy and concomitant temozolomide by GLCM texture analysis of conventinal MRI[J].Clinical Imaging,2015,39:775-780.
7 Anderaen MB,Harders SW.CT texture ananlysis can help differentiate between malignant and benign lymphnodes in the mediastinum in patients suspected for lung cancer[J].Acta Radiological,2016,57:669-676.
8 Daginawala N,Li B,Buch K,et al.Using texture analyses of contrast enhanced CT to assess hepatic fibrosis[J].Eur J Radiol,2016,85:511-517.
9 Li Z,Mao Y,Huang W,et al.Texture-based classification of different single liver lesion based on SPAIR T2W MRI images[J].BMC Med Imaging,2017,17:1-9.
10 Pereira JA,Rosado E,Bali M,et al.Pancreatic neuroendocrine tumors:correlation between histogram analysis of apparent diffusion coefficient maps and tumor grade[J].Abodm Imaging,2015,40:3122-3128.
11 韩秋月,武江芬,鲁珊珊,等.DCE-MRI直方图分析在脑胶质瘤分级中的应用[J].中国医学计算机成像杂志,2017,23:7-12.
12 Falk A,Fahlstrom M,Rostrup E,et al,Discrimination between glioma grades II and III in suspected low-grade gliomas using dynamic contrast-enhanced and dynamic susceptibility contrast perfusion MR imaging:a histogram analysis approach[J].Neuroradiology,2014,56:1031-1038.
13 Kiefer B,Kiraly AP.Histogram analysis of whole-lesion enhancement in differentiating clear cell from papillary subtype of renal cell cancer[J].Radiology.2012,265:790-798.
14 王君鑫,赵文露,杨毅,等.动态增强MRI三维定量参数直方图诊断前列腺癌的价值[J].中华放射学杂志,2016,50:609-614.