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目的探讨组织弥散弹性成像技术在中孕期先兆子痫中的应用价值。方法回顾性分析72例中孕期孕妇,其中40例无围产期并发症的正常孕妇作为对照组,32例临床最终诊断为先兆子痫的孕妇作为病例组,排除后壁胎盘及其它产科异常。对所有孕妇行常规及弹性成像检查,比较先兆子痫组和正常妊娠组胎盘弹性参数MEAN、AREA%的差异,绘制受试者特征曲线(ROC曲线),并计算曲线下面积。结果先兆子痫组胎盘弹性图像多呈蓝色,夹杂部分绿色及少许红色;而正常妊娠组胎盘弹性图像呈均匀分布的绿色,夹杂少许红色或蓝色。与正常妊娠组比较,先兆子痫组的弹性参数MEAN较小,AREA%较大,差异均有统计学意义(P<0.05)。进一步比较ROC曲线发现,AREA%有较大的曲线下面积,以AREA%≥43.29,判断先兆子痫的敏感度83%,特异度85%。结论中孕期先兆子痫孕妇胎盘硬度比正常妊娠孕妇大,组织弥散弹性成像可无创评价胎盘功能,可成为中孕期预测子痫发生的有力补充,其中弹性参数AREA%要优于MEAN。
Objective To investigate the value of tissue diffusion elastography in pre-eclampsia during pregnancy. Methods Retrospective analysis of 72 pregnant women in the second trimester, of which 40 cases of normal pregnant women without perinatal complications as a control group, 32 cases of clinically diagnosed as preeclampsia in pregnant women as a case group to rule out posterior wall placenta and other obstetric abnormalities. All pregnant women underwent routine and elastography examination. The differences of MEAN and AREA% of placental elasticity between preeclampsia group and normal pregnancy group were compared. The receiver operating characteristic curve (ROC curve) was plotted and the area under the curve was calculated. Results In the preeclamptic group, the placental elasticity images were mostly blue with a few green and a little red. However, the placental elasticity images in the normal pregnancy group were evenly distributed green with a little red or blue. Compared with normal pregnancy group, MEAN of preeclampsia group was smaller and AREA% was larger, the differences were statistically significant (P <0.05). Further comparison of ROC curves showed that AREA% had a larger area under the curve, with AREA% ≥43.29, to determine the sensitivity of preeclampsia 83%, specificity 85%. Conclusions The placental hardness of pregnant women with preeclampsia in pregnancy is larger than that of normal pregnant women. Tissue diffusion elastography can evaluate placental function noninvasively and may be a powerful complement to predict preeclampsia in middle pregnancy. The elasticity parameter AREA% is better than MEAN.