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T2WI肿瘤最大层面直方图分析鉴别腮腺多形性腺瘤与恶性肿瘤
引用本文:黄荟玉,张勇,程敬亮,许珂,文萌萌,文宝红.T2WI肿瘤最大层面直方图分析鉴别腮腺多形性腺瘤与恶性肿瘤[J].中国介入影像与治疗学,2019,16(8):464-468.
作者姓名:黄荟玉  张勇  程敬亮  许珂  文萌萌  文宝红
作者单位:郑州大学第一附属医院磁共振科
基金项目:河南省医学科技攻关计划项目(20160285)。
摘    要:目的探讨T2WI肿瘤最大层面直方图分析鉴别腮腺多形性腺瘤与恶性肿瘤的价值。方法回顾性分析经手术病理证实的41例腮腺多形性腺瘤及23例腮腺恶性肿瘤患者的MRI资料。采用MaZda软件在轴位最大肿瘤层面T2WI中勾画ROI,进行灰度直方图分析,获取9个特征参数(均值、方差、峰度、偏度、第1百分位数、第10百分位数、第50百分位数、第90百分位数及第99百分位数)进行统计分析,比较腮腺多形性腺瘤与恶性肿瘤间直方图特征参数的差异;绘制ROC曲线,评价以直方图特征参数鉴别腮腺多形性腺瘤与恶性肿瘤的效能。结果 9个特征参数中,第1百分位数及第10百分位数在腮腺多形性腺瘤与恶性肿瘤间差异有统计学意义(P均0.05),腮腺多形性腺瘤均高于恶性肿瘤。ROC曲线分析显示,第10百分位数最具鉴别诊断效能,AUC为0.70(P=0.01),最佳临界值为76.00,敏感度为66.70%,特异度为60.00%。第1百分位数的ROC曲线AUC、最佳临界值、敏感度及特异度分别为0.67(P=0.04)、46.50、63.90%及60.00%。结论 T2WI肿瘤最大层面直方图分析可作为术前鉴别腮腺多形性腺瘤与恶性肿瘤的重要手段,为临床提供有价值的参考信息。

关 键 词:腮腺肿瘤  腺瘤  多形性  恶性肿瘤  磁共振成像  直方图
收稿时间:2018/11/28 0:00:00
修稿时间:2019/6/9 0:00:00

T2WI maximum tumor level histogram for differentiating parotid pleomorphic adenoma and malignant tumor
HUANG Huiyu,ZHANG Yong,CHENG Jingliang,XU Ke,WEN Mengmeng and WEN Baohong.T2WI maximum tumor level histogram for differentiating parotid pleomorphic adenoma and malignant tumor[J].Chinese Journal of Interventional Imaging and Therapy,2019,16(8):464-468.
Authors:HUANG Huiyu  ZHANG Yong  CHENG Jingliang  XU Ke  WEN Mengmeng and WEN Baohong
Institution:Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China,Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China,Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China,Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China,Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China and Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
Abstract:Objective To assess the value of T2WI maximum tumor level histogram in differentiating pleomorphic adenoma from malignant tumors of parotid gland. Methods MRI of 64 patients with parotid tumors, including 41 cases of pleomorphic adenomas and 23 cases of malignant tumors confirmed by pathology were analyzed retrospectively. Mazda software was used to select ROIs in the maximum tumor level image on axial T2WI. Gray histogram analysis was carried out to obtain 9 characteristic parameters, including mean, variance, kurtosis, skewness, first percentile (perc 1%), tenth percentile (perc 10%), fiftieth percentile (perc 50%), ninetieth percentile (perc 90%) and ninety-ninth percentile (perc 99%). Statistical analysis was performed to compare the characteristic parameters of histogram between pleomorphic adenoma and malignant tumors. ROC curve was drawn to evaluate the effectiveness of the characteristic parameters of histogram in tumors differentiating. Results Among 9 characteristic parameters of histogram, perc 1% and perc 10% had statistical differences between pleomorphic adenoma and malignant tumors (both P<0.05). Both of perc 1% and perc 10% of pleomorphic adenoma were significantly higher than those of malignant tumors. ROC curve analysis showed that perc 10% was the most effective parameter for differential diagnosis. The AUC was 0.70 (P=0.01), and the optimal critical value was 76.00. The sensitivity and specificity was 66.70% and 60.00%, respectively. Besides, the AUC, optimal critical value, sensitivity and specificity of perc 1% was 0.67 (P=0.04), 46.50, 63.90% and 60.00%, respectively. Conclusion T2WI maximum tumor level histogram can be used as an important method to differentiate pleomorphic adenoma from malignant tumors of parotid gland before operation, which can provide valuable references for clinic.
Keywords:parotid neoplasms  adenoma  pleomorphic  malignant tumor  magnetic resonance imaging  histogram
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