T_2WI全域灰度直方图鉴别原发性中枢神经系统淋巴瘤和胶质母细胞瘤的价值
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摘要
目的探讨T_2WI全域灰度直方图分析在原发性中枢神经系统淋巴瘤(PCNSL)和胶质母细胞瘤(GBM)诊断中的价值。方法回顾性分析在本院行脑部MRI检查并经病理证实的41例PCNSL和29例GBM。分别在两组T_2WI轴位图像上的每一层肿瘤层面用MaZda软件勾画感兴趣区(ROI)并进行灰度全域直方图分析,观察两种肿瘤的直方图特征,包括均值(Mean)、方差(Variance)、偏度(Skewness)、峰度(Kurtosis)、第1百分位数(Perc.1%)、第10百分位数(Perc.10%)、第50百分位数(Perc.50%)、第90百分位数(Perc.90%)、第99百分位数(Perc.99%),找出两种肿瘤之间的显著性差异。结果通过T_2WI全域灰度直方图分析得到的9个参数中,Mean、Variance、Kurtosis、Skewness、Perc.50%、Perc.90%、Perc.99%这7个参数差异有统计学意义(P均<0.05)。在这些参数中,Variance鉴别PCNSL和GBM的效能最高,受试者工作特征(ROC)曲线下面积(AUC)为0.884,敏感度和特异度为69.0%、90.2%。结论 T_2WI全域灰度直方图分析有助于PCNSL和GBM的鉴别,Variance具有较高诊断效能。
Objective To study the value of whole tumors T_2WI gray histogram analysis of differential diagnosis in primary central nervous system lymphoma and glioblastomaa.Methods A retrospective analysis was conducted by brain MRI examination and pathology diagnosis of 41 cases of primary central nervous system lymphoma and 29 cases of glioblastoma in our hospital.The region of interest was outlined with Mazda software in each layer of the two groups of T_2WI axis images,and the gray whole domain histogram analysis was carried out,observing the two kinds of tumors,including the Mean and Variance,Skewness,Kurtosis,the Perc.1%,Perc.10%,Perc.50%,Perc.90%,Perc.99%,to find out the significant difference between the two types of cancer.Results Through histogram analysis of 9 parameters,these 7 parameters were statistically significant(all P<0.05),including mean,variance,kurtosis,skewness,,Perc.50%,Perc.90% and Perc.99%.In these parameters,the variance identification of the primary central nervous system lymphoma and glioblastomaa was the most effective,and the area under the ROC curve was 0.884,and the sensitivity and specificity were 69.0% and 90.2% respectively.Conclusion The whole tumors T_2WI gray histogram analysis is helpful for the identification of primary central nervous system lymphoma from glioblastomaa and the variance has a high diagnostic efficiency.
Objective To study the value of whole tumors T_2WI gray histogram analysis of differential diagnosis in primary central nervous system lymphoma and glioblastomaa.Methods A retrospective analysis was conducted by brain MRI examination and pathology diagnosis of 41 cases of primary central nervous system lymphoma and 29 cases of glioblastoma in our hospital.The region of interest was outlined with Mazda software in each layer of the two groups of T_2WI axis images,and the gray whole domain histogram analysis was carried out,observing the two kinds of tumors,including the Mean and Variance,Skewness,Kurtosis,the Perc.1%,Perc.10%,Perc.50%,Perc.90%,Perc.99%,to find out the significant difference between the two types of cancer.Results Through histogram analysis of 9 parameters,these 7 parameters were statistically significant(all P<0.05),including mean,variance,kurtosis,skewness,,Perc.50%,Perc.90% and Perc.99%.In these parameters,the variance identification of the primary central nervous system lymphoma and glioblastomaa was the most effective,and the area under the ROC curve was 0.884,and the sensitivity and specificity were 69.0% and 90.2% respectively.Conclusion The whole tumors T_2WI gray histogram analysis is helpful for the identification of primary central nervous system lymphoma from glioblastomaa and the variance has a high diagnostic efficiency.
引文
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4陈杰,潘靓,孙军,等.直方图分析磁敏感信号强度鉴别乳头状与嫌色细胞肾癌的价值[J].中华放射学杂志,2017,51:669-672.
5金观桥,苏丹柯,罗殿中,等.表观扩散系数直方图预测局部晚期乳腺癌患者新辅助化疗疗效的价值[J].中华放射学杂志,2015,49:491-494.
6林宇宁,李辉,陈自谦,等.采用MR扩散加权成像表观扩散系数值直方图诊断ⅠB期宫颈癌的价值[J].中华放射学杂志,2015,49:349-353.
7 Phillips EH,Fox CP,Cwynarski K.Primary CNS lymphoma[J].Curr Hematol Malig Rep,2014,9:243-253.
8 Malzkorn B,Reifenberger G.Practical implications of integrated glioma classification according to the World Health Organization classification of tumors of the central nervous system 2016[J].Curr Opin Oncol,2016,28:494-501.
9 Ahn SJ,Shin HJ,Chang JH,et al.Differentiation between primary cerebral lymphoma andglioblastoma using the apparent diffusion coefficient:comparison of three different ROI methods[J].PLo S One,2014,9:e112948.
10 Ryu YJ,Choi SH,Park SJ,et al.Glioma:application of whole-tumor texture analysis of diffusion-weighted imaging for theevaluation of tumor heterogeneity[J].PLo S One,2014,9:e108335.
11 Xu XQ,Li Y,Hong XN,et al.Radiological indeterminate vestibular schwannoma and meningioma in cerebellopontine anglearea:differentiating using whole-tumor histogram analysis of apparent diffusion coefficient[J].Int J Neurosci,2017,127:183-190.
12 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,2017,17:42.
13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,2017,8:276-282.
14 Just N.Improving tumour heterogeneity MRI assessment with Histograms[J].Br J Cancer,2014,111:2205-2213.
15张胜,李玉林,黄送.增强T1WI直方图在胶质母细胞瘤和脑单发转移瘤鉴别诊断中的应用[J].中国医学影像学杂志,2017,25:89-92.
16陈鑫,魏新华,杨蕊梦,等.常规MR纹理分析鉴别胶质母细胞瘤和单发转移瘤的价值[J].中华放射学杂志,2016,50:186-190.
2 Kunimatsu A,Kunimatsu N,Kamiya K,et al.Comparison between Glioblastoma and Primary Central Nervous System Lymphoma Using MR Image-based Texture Analysis[J].Magn Reson Med Sci,2018,17:50-57.
3孙梦恬,程敬亮,张勇,等.动态增强MRI在胶质母细胞瘤与脑淋巴瘤鉴别诊断中的应用[J].实用放射学杂志,2013,29:345-348.
4陈杰,潘靓,孙军,等.直方图分析磁敏感信号强度鉴别乳头状与嫌色细胞肾癌的价值[J].中华放射学杂志,2017,51:669-672.
5金观桥,苏丹柯,罗殿中,等.表观扩散系数直方图预测局部晚期乳腺癌患者新辅助化疗疗效的价值[J].中华放射学杂志,2015,49:491-494.
6林宇宁,李辉,陈自谦,等.采用MR扩散加权成像表观扩散系数值直方图诊断ⅠB期宫颈癌的价值[J].中华放射学杂志,2015,49:349-353.
7 Phillips EH,Fox CP,Cwynarski K.Primary CNS lymphoma[J].Curr Hematol Malig Rep,2014,9:243-253.
8 Malzkorn B,Reifenberger G.Practical implications of integrated glioma classification according to the World Health Organization classification of tumors of the central nervous system 2016[J].Curr Opin Oncol,2016,28:494-501.
9 Ahn SJ,Shin HJ,Chang JH,et al.Differentiation between primary cerebral lymphoma andglioblastoma using the apparent diffusion coefficient:comparison of three different ROI methods[J].PLo S One,2014,9:e112948.
10 Ryu YJ,Choi SH,Park SJ,et al.Glioma:application of whole-tumor texture analysis of diffusion-weighted imaging for theevaluation of tumor heterogeneity[J].PLo S One,2014,9:e108335.
11 Xu XQ,Li Y,Hong XN,et al.Radiological indeterminate vestibular schwannoma and meningioma in cerebellopontine anglearea:differentiating using whole-tumor histogram analysis of apparent diffusion coefficient[J].Int J Neurosci,2017,127:183-190.
12 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,2017,17:42.
13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,2017,8:276-282.
14 Just N.Improving tumour heterogeneity MRI assessment with Histograms[J].Br J Cancer,2014,111:2205-2213.
15张胜,李玉林,黄送.增强T1WI直方图在胶质母细胞瘤和脑单发转移瘤鉴别诊断中的应用[J].中国医学影像学杂志,2017,25:89-92.
16陈鑫,魏新华,杨蕊梦,等.常规MR纹理分析鉴别胶质母细胞瘤和单发转移瘤的价值[J].中华放射学杂志,2016,50:186-190.