Cong Mengdi, Qiu Shi, Li Rongpin, Sun Haiyan, Cong Lining, Hou Zhenzhou
Department of Computed Tomography and Magnetic Resonance, Children's Hospital of Hebei Province, Shijiazhuang, Hebei 050031, P.R. China.
Key Laboratory of Spectral Imaging Technology Chinese Academy of Science, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi 710119, P.R. China.
Exp Ther Med. 2021 May;21(5):494. doi: 10.3892/etm.2021.9925. Epub 2021 Mar 17.
The aim of the present study was to develop predictive models using clinical features and MRI texture features for distinguishing between growth hormone deficiency (GHD) and idiopathic short stature (ISS) in children with short stature. This retrospective study included 362 children with short stature from Children's Hospital of Hebei Province. GHD and ISS were identified via the GH stimulation test using arginine. Overall, there were 190 children with GHD and 172 with ISS. A total of 57 MRI texture features were extracted from the pituitary gland region of interest using C++ language and Matlab software. In addition, the laboratory examination data were collected. Receiver operating characteristic (ROC) regression curves were generated for the predictive performance of clinical features and MRI texture features. Logistic regression models based on clinical and texture features were established for discriminating children with GHD and ISS. Two clinical features [IGF-1 (insulin growth factor-1) and IGFBP-3 (IGF binding protein-3) levels] were used to build the clinical predictive model, whereas the three best MRI textures were used to establish the MRI texture predictive model. The ROC analysis of the two models revealed predictive performance for distinguishing GHD from ISS. The accuracy of predicting ISS from GHD was 64.5% in ROC analysis [area under the curve (AUC), 0.607; sensitivity, 57.6%; specificity, 72.1%] of the clinical model. The accuracy of predicting ISS from GHD was 80.4% in ROC analysis (AUC, 0.852; sensitivity, 93.6%; specificity, 65.8%) of the MRI texture predictive model. In conclusion, these findings indicated that a texture predictive model using MRI texture features was superior for distinguishing children with GHD from those with ISS compared with the model developed using clinical features.
本研究的目的是利用临床特征和MRI纹理特征建立预测模型，以区分身材矮小儿童的生长激素缺乏症（GHD）和特发性矮小症（ISS）。这项回顾性研究纳入了河北省儿童医院的362例身材矮小儿童。通过使用精氨酸的GH刺激试验来确定GHD和ISS。总体而言，有190例GHD儿童和172例ISS儿童。使用C++语言和Matlab软件从垂体感兴趣区域提取了总共57个MRI纹理特征。此外，收集了实验室检查数据。针对临床特征和MRI纹理特征的预测性能生成了受试者操作特征（ROC）回归曲线。建立了基于临床和纹理特征的逻辑回归模型，以区分GHD和ISS儿童。使用两个临床特征[胰岛素生长因子-1（IGF-1）和IGF结合蛋白-3（IGFBP-3）水平]建立临床预测模型，而使用三个最佳MRI纹理建立MRI纹理预测模型。两种模型的ROC分析显示了区分GHD和ISS的预测性能。在临床模型的ROC分析中[曲线下面积（AUC）为0.607；灵敏度为57.6%；特异性为72.1%]，从GHD预测ISS的准确率为64.5%。在MRI纹理预测模型的ROC分析中（AUC为0.852；灵敏度为93.6%；特异性为65.8%），从GHD预测ISS的准确率为80.4%。总之，这些发现表明，与使用临床特征建立的模型相比，使用MRI纹理特征的纹理预测模型在区分GHD儿童和ISS儿童方面更具优势。
