摘要目的 探讨基于分段读出扩散加权成像序列(RESOLVE?DWI)ADC图的全域直方图分析,对腮腺常见肿瘤(多形性腺瘤、Warthin瘤、腮腺恶性肿瘤)鉴别诊断的价值.方法 回顾性分析2013年11月至2018年11月郑州大学第一附属医院经病理证实的108例腮腺常见肿瘤治疗前MRI平扫、增强、RESOLVE?DWI及ADC图像,其中多形性腺瘤47例、Warthin瘤25例、腮腺恶性肿瘤36例.利用Mazda软件在包含肿瘤实质的每一层ADC图像上勾画瘤体ROI,进行直方图分析并提取出12个参数(面积、最小值、最大值、均值、变异度、偏度、峰度、第1百分位数、第10百分位数、第50百分位数、第90百分位数、第99百分位数).对3种肿瘤的直方图参数进行多组间比较(单因素方差分析或Kruskal?Wallis检验)及两两比较(LSD检验或Mann?Whitney U检验),找出具有统计学意义的参数,并利用受试者ROC曲线确定各直方图参数鉴别诊断肿瘤性质的效能.结果 直方图中最小值、最大值、均值、变异度、偏度、第10百分位数、第50百分位数、第90百分位数、第99百分位数共9个参数在3种肿瘤间的差异具有统计学意义(P<0.05).在多形性腺瘤与Warthin瘤间,上述9个参数的差异均具有统计学意义(P<0.05),第50百分位数的鉴别诊断效能最高,其次为均值、第10百分位数.在多形性腺瘤与恶性肿瘤间,上述9个参数的差异亦具有统计学意义(P<0.05),其中均值的鉴别诊断效能最高,其次为第10百分位数、第90百分位数.但在Warthin瘤与恶性肿瘤间,仅最小值、均值、偏度、第10百分位数、第50百分位数共5个参数的差异具有统计学意义(P<0.05),其中最小值的鉴别诊断效能最高,其次为第50百分位数、第10百分位数.总体上,均值、第10百分位数和第50百分位数对3种腮腺常见肿瘤的鉴别诊断效能相对较高.结论 基于RESOLVE?DWI ADC的全域直方图分析有助于腮腺常见肿瘤的鉴别诊断.

abstractsObjective The purpose of this study was to evaluate whether whole?tumor histogram?based analysis of readout?segmented echo?planar diffusion?weighted imaging (RESOLVE?DWI) ADC map can help in the discrimination of parotid gland tumors(pleomorphic adenoma, Warthin tumor, malignant parotid gland tumor). Methods The MR images(pre / post?contrast enhanced MRI, RESOLVE?DWI, ADC map) of 47 patients with a biopsy?or surgery?proven pleomorphic adenomas, 25 patients with Warthin tumors and 36 patients with malignant parotid gland tumors were retrospectively analyzed. Histogram?based analysis was performed with the software MaZda. ROIs were drawn on every section of the ADC map containing the tumor, then 12 Parameters(Area, MinNorm, MaxNorm, Mean, Variance, Skewness, Kurtosis, Perc.1%, Perc.10%, Perc.50%, Perc.90%, Perc.99%) derived from histogram were calculated. Statistical analysis among the three groups (One?way ANOVA or Kruskal?Wallis test) were performed to find out the statistical significance of each histogram parameter. Then LSD test or Mann?Whitney U test was used for pairwise comparison between groups. And the differential efficiency of each parameter was determined using ROC analysis. Results Overall, 9 parameters (MinNorm, MaxNorm, Mean, Variance, Skewness, Perc.10%, Perc.50%, Perc.90%, Perc.99%) among three groups were shown to be statistically significant (P<0.05). Between the pleomorphic adenomas and Warthin tumors, these 9 parameters were of statistical significance. Perc.50% revealed the highest diagnostic efficiency, followed by Mean and Perc.10%. Between the pleomorphic adenomas and malignant parotid gland tumors, also all these 9 parameters were of statistical significance. Mean was revealed to have the highest diagnostic efficiency, followed by Perc. 10% and Perc. 90%. However, between the Warthin tumors and malignant parotid gland tumors, only 5 parameters (MinNorm, Mean, Skewness, Perc.10%, Perc.50%) were statistically significant. MinNorm was revealed to have the highest diagnostic efficiency, followed by Perc. 50% and Perc. 10%. Generally, Mean, Perc. 10% and Perc. 50% were more effective in the differential diagnosis of these three types common parotid neoplasms. Conclusion Whole?tumor histogram analysis of ADC maps are effective in differentiating common parotid neoplasms.