Development of a predictive model of growth hormone deficiency and idiopathic short stature in children

Cong,Mengdi; Qiu,Shi; Li,Rongpin; Sun,Haiyan; Cong,Lining; Hou,Zhenzhou

doi:10.3892/etm.2021.9925

Development of a predictive model of growth hormone deficiency and idiopathic short stature in children

Authors:
- Mengdi Cong
- Shi Qiu
- Rongpin Li
- Haiyan Sun
- Lining Cong
- Zhenzhou Hou
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Affiliations: 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, Department of Radiology, Children's Hospital of Hebei Province, Shijiazhuang, Hebei 050031, P.R. China
Published online on: March 17, 2021 https://doi.org/10.3892/etm.2021.9925
Article Number: 494
Copyright: © Cong et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

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.

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