ArticleYonsei medical journal2022
A Radiomics-Based Model with the Potential to Differentiate Growth Hormone Deficiency and Idiopathic Short Stature on Sella MRI.
Article in Yonsei medical journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Pituitary MRI features in identifying idiopathic short stature from growth hormone deficiency in children with short stature.European radiology · 2025Article
- Development and Validation of a Prediction Model Using Sella Magnetic Resonance Imaging-Based Radiomics and Clinical Parameters for the Diagnosis of Growth Hormone Deficiency and Idiopathic Short Stature: Cross-Sectional, Multicenter Study.Journal of medical Internet research · 2024Article
- Article
- The influence of pituitary volume on the growth response in growth hormone-treated children with growth hormone deficiency or idiopathic short stature.Annals of pediatric endocrinology & metabolism · 2024Article
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7 authors.
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Abstract
purposeWe hypothesized that a radiomics approach could be employed to classify children with growth hormone deficiency (GHD) and idiopathic short stature (ISS) on sella magnetic resonance imaging (MRI). Accordingly, we aimed to develop a radiomics prediction model for differentiating GHD from ISS and to evaluate the diagnostic performance thereof. MATERIALS AND
methodsShort stature pediatric patients diagnosed with GHD or ISS from March 2011 to July 2020 at our institution were recruited. We enrolled 312 patients (GHD 210, ISS 102) with normal sella MRI and temporally split them into training and test sets (7:3). Pituitary glands were semi-automatically segmented, and 110 radiomic features were extracted from the coronal T2-weighted images. Feature selection and model development were conducted by applying mutual information (MI) and a light gradient boosting machine, respectively. After training, the model's performance was validated in the test set. We calculated mean absolute Shapley values for each of the selected input features using the Shapley additive explanations (SHAP) algorithm. Volumetric comparison was performed for GHD and ISS groups.
resultsTen radiomic features were selected by MI. The receiver operating characteristics curve of the developed model in the test set was 0.705, with an accuracy of 70.6%. When analyzing SHAP plots, root mean squared values had the highest impact in the model, followed by various texture features. In volumetric analysis, sagittal height showed a significant difference between GHD and ISS groups.
conclusionRadiomic analysis of sella MRI may be able to differentiate between GHD and ISS in clinical practice for short-statured children.
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