摘要
目的探讨MR-T_2WI纹理分析在前列腺癌诊断中的价值。方法回顾性分析病理证实的140例前列腺癌与160例良性前列腺增生的MR-T_2WI图像;通过纹理分析方法测得MR-T_2WI图像前列腺病灶的熵、平均值、标准差、不均匀度、峰值和偏度等纹理分析定量参数,用独立样本t检验比较前列腺癌组和良性增生组间各纹理分析定量参数的差异;应用受试者工作特征曲线,获得曲线下面积,确定各定量参数鉴别前列腺癌与良性增生的阈值和诊断效能。运用Z检验比较各参数的曲线下面积。结果纹理分析定量参数中熵、平均值、标准差、不均匀度和峰值五个参数在前列腺癌组与良性增生组间的差异具有统计学意义;平均值鉴别前列腺癌组与良性增生组的AUC值为0.804,当阈值为94.34时,其诊断敏感性为73.6%、特异性为80.0%、准确性为77.0%,其效能最高,均高于其他定量参数。结论 MR-T_2WI纹理分析的部分定量参数有助于前列腺癌与良性前列腺增生的鉴别;纹理分析有助于提升MR-T_2WI的诊断价值。
Objective To evaluate the value of MR-T2WI texture analysis in the diagnosis of prostate cancer. Methods We retrospectively analyzed of 114 patients with PCa and 160 patients with BPH. All cases having been confirmed by operative pathological examination and underwent MR-T2WI examination. Through using the method of MR-T2WI texture analysis, we can get many parameters: Mean, StDev, Entropy, Inhomogenity, Skewness, Kurtosis. The quantitative parameters of the texture analysis between PCa group and BPH group were compared by independent sample T test. The receiver operator characteristic was used to obtain the area under the curve to determine the quantitative parameters of the texture analysis and threshold and diagnostic efficacy of related parameters diagnosing PCa and BPH. We used Z test to compare the area under the curve of each parameter. ResultsIn texture analysis, the five parameters of Entropy, Mean, StDev, Inhomogenity, Skewness value in quantitative parameters were statistically significant in PCa group and BPH group. the AUC value of the PCa group and BPH group identified by the mean value was 0.804, and when the threshold was 94.34, the diagnostic sensitivity was 73.6 %, the specificity was 80.0% and the accuracy was 77.0%. It had the highest efficiency, which was higher than other quantitative parameters. Conclusion Partial quantitative parameters of MR-T2WI texture analysis that contributes to the identification of PCa and BPH. Texture analysis helps to improve the diagnostic value of MR-T2WI.
作者
许岗
赵文露
李梦娟
张跃
袁振洲
姚立正
沈钧康
XU Gang;ZHAO Wenlu;LI Mengjuan;ZHANG Yue;YUAN Zhenzhou;YAO Lizheng(Department of Imaging,Yancheng Third People's Hospital,Yancheng 224001,P.R.China;Department of Imaging,The Second Affiliated Hospital of Soochow University,Suzhou 215004,P.R.China)
出处
《医学影像学杂志》
2019年第3期458-462,共5页
Journal of Medical Imaging