摘要
目的探讨MRI图像纹理分析预测胶质瘤患者Ki-67表达状态的应用价值。方法搜集经手术病理证实的116例胶质瘤患者,对其T1WI增强图像进行纹理分析,获取整个瘤体的平均值、中位数、标准差、偏度、峰度、能量和熵值等定量参数,并进行统计学分析。结果对MRI图像纹理分析后所获得的定量参数中,平均值、中位数、标准差3个参数在Ki-67(-)组与Ki-67(+)组间的差异无统计学意义,偏度、峰度、能量和熵值4个参数在Ki-67(-)组与Ki-67(+)组间的差异有统计学意义(P<0.001);熵值参数鉴别效能明显优于其他参数值,有较高的敏感度、特异度及准确率;对偏度、峰度、能量和熵值4个纹理分析定量参数进行多参数联合分析,AUC值为0.804,当阈值为0.372时,其诊断敏感度95.0%、特异度为76.8%、准确率为85.6%,较利用单个纹理分析定量参数鉴别的效能高。结论纹理分析定量参数中偏度、峰度、能量和熵值有助于预测胶质瘤患者Ki-67是否表达。
Objective To evaluate the value of MRI image texture analysis in predicting the expression of Ki-67 in patients with glioma.Methods 116 patients with glioma confirmed by operation and pathology were collected retrospectively.The T1-enhanced images were analyzed by texture analysis.The quantitative parameters such as mean,median,standard deviation,skewness,kurtosis,energy and entropy were obtained and analyzed statistically.Results Among the quantitative parameters obtained by texture analysis,there was no significant difference in mean,median and standard deviation between Ki-67(-)group and Ki-67(+)group,but there was significant difference in skewness,kurtosis,energy and entropy between Ki-67(-)group and Ki-67(+)group(P<0.001).The identification efficiency of entropy parameters is obviously better than that of other parameters,and has higher sensitivity,specificity and accuracy.The quantitative parameters of skewness,kurtosis,energy and entropy were analyzed by multi-parameter joint analysis.The AUC value was 0.804.When the threshold value was 0.804,the diagnostic sensitivity,specificity and accuracy were 95.0%,76.8%and 85.6%,respectively.Conclusion Some quantitative parameters(skewness,kurtosis,energy and entropy)of texture analysis are helpful to predict the expression of Ki-67 in glioma patients.
作者
董丽娜
李梦双
许倩
蔡璐璐
于可
罗涛
窦宾茹
解婷
李菁菁
徐凯
DONG Lina;LI Mengshuang;XU Qian(Department of Medical Imaging,Affiliated Hospital of Xuzhou Medical University,Xuzhou,Jiangsu Province 221006,P.R.China)
出处
《临床放射学杂志》
CSCD
北大核心
2020年第8期1478-1481,共4页
Journal of Clinical Radiology
基金
江苏省研究生实践创新计划项目(编号:SJCX18_0708)
江苏省自然科学基金优秀青年项目(编号:BK20170054)
江苏省“科教强卫”青年医学人才项目(编号:QNRC2016776)
中国博士后基金资助项目(编号:2016M601890,177607)
江苏省第十四批“六大人才高峰”高层次人才项目(编号:WSN-112)
2018年高层次卫生人才“六个一工程”拔尖人才科研项目(编号:LGY2018083)
