This was a retrospective case-control study using stored two-dimensional midsagittal fetal profile images captured at 11 + 0 to 13 + 6 weeks\' gestation in singleton pregnancies. We included images from 302 trisomy-21 pregnancies and 322 euploid pregnancies. Cases were divided into a training set (200 euploid + 200 with trisomy 21) and a validation set (122 euploid + 102 with trisomy 21) at a ratio of approximately 2:1. For each, the maternal age, gestational age, fetal NT and karyotype were noted, and 12 ultrasonographic fetal facial markers were measured. The least absolute shrinkage and selection operator (LASSO) method and multivariable analysis were used to select automatically the discriminative markers. Logistic regression was used to develop a LASSO model, based on the selected markers, to screen for trisomy 21 in the first trimester of pregnancy. Furthermore, 60 of the 624 images were selected randomly as a retest set to evaluate the model\'s robustness. The predictive performance of screening for trisomy 21 of a model based on fetal NT and maternal age and of the LASSO model was assessed using the area under the receiver-operating-characteristics curve (AUC). A nomogram was developed as an individualized tool to predict patient-specific probability for trisomy 21, which is a more visual presentation of the LASSO model. The performance of the nomogram was assessed using the C-index and calibration curve.
Into the LASSO model were incorporated eight markers, including fetal NT, prenasal-thickness-to-nasal-bone-length ratio, facial profile line, frontomaxillary facial angle, frontonasal facial angle, mandibulomaxillary facial angle, maxilla-nasion-mandible angle and d2 (distance between the anterior edge of the prefrontal skin and the mandibulomaxillary line) (all P < 0.05). The AUCs of the LASSO model for screening for trisomy 21 were 0.983 (95% CI, 0.971-0.994) in the training set and 0.979 (95% CI, 0.966-0.993) in the validation set, and these were higher than the AUCs of all eight individual ultrasonographic markers included in the model. The AUC of the LASSO model in the retest set was 0.997 (95% CI, 0.990-1.000), indicating good robustness of the LASSO model. The AUC of the LASSO model was significantly higher than that of the model based on fetal NT and maternal age in both training and validation sets (P < 0.001 for both). The nomogram of the LASSO model showed good discrimination of trisomy 21, with C-indices of 0.983 in the training set and 0.981 in the validation set.
We present an individualized nomogram which incorporates fetal NT and a series of ultrasonographic facial profile markers selected by the LASSO method and multivariable analysis. This nomogram can potentially be utilized as a convenient and effective tool in screening for trisomy 21 in the first trimester of pregnancy. © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
这是一项回顾性病例对照研究,使用在单胎妊娠中11+0至13+6周时捕获的存储的二维矢状位中胎儿轮廓图像。我们包括来自302个21三体妊娠和322个整倍体妊娠的图像。以大约2:1的比率将病例分成训练集(200整倍体+具有21三体的200)和验证集(122整倍体+具有21三体的102)。对于每一个,产妇年龄,胎龄,注意到胎儿NT和核型,并测量了12个超声胎儿面部标记物。使用最小绝对收缩和选择算子(LASSO)方法和多变量分析自动选择判别标记。采用Logistic回归建立LASSO模型,基于选定的标记,在怀孕的前三个月筛查21三体。此外,随机选择624张图像中的60张作为重新测试集,以评估模型的鲁棒性。使用接受者操作特征曲线下面积(AUC)评估了基于胎儿NT和母体年龄的模型和LASSO模型的21三体筛查的预测性能。列线图被开发为一种个性化的工具来预测21三体的患者特异性概率,这是LASSO模型的更直观的呈现。使用C指数和校准曲线评估列线图的性能。
在LASSO模型中加入了八个标记,包括胎儿NT,鼻前厚度与鼻骨长度之比,面部轮廓线,前腋窝面部角度,额鼻面角度,下颌下颌面部角度,上颌-鼻下-下颌骨角度和d2(前额叶皮肤前缘与下颌腋线之间的距离)(均P<0.05)。用于筛选21三体的LASSO模型的AUC在训练集中为0.983(95%CI,0.971-0.994),在验证集中为0.979(95%CI,0.966-0.993),这些都高于模型中包含的所有8个单独的超声标记的AUC。复测集中LASSO模型的AUC为0.997(95%CI,0.990-1.000),表明LASSO模型具有良好的鲁棒性。在训练集和验证集中,LASSO模型的AUC显著高于基于胎儿NT和母体年龄的模型(两者均P<0.001)。LASSO模型的列线图显示了对21三体的良好区分,训练集中的C指数为0.983,验证集中的C指数为0.981。
我们提供了一个个性化的列线图,其中包含了胎儿NT和一系列通过LASSO方法和多变量分析选择的超声面部轮廓标记。该列线图可以潜在地用作在妊娠的头三个月中筛查21三体的方便和有效的工具。©2020作者由JohnWiley&SonsLtd代表国际妇产科超声学会出版的妇产科超声。