A Radiomics-Based Model with the Potential to Differentiate Growth Hormone Deficiency and Idiopathic Short Stature on Sella MRI
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Purpose: We 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 radiomicsprediction model for differentiating GHD from ISS and to evaluate the diagnostic performance thereof. Materials and Methods: Short stature pediatric patients diagnosed with GHD or ISS from March 2011 to July 2020 at our institu-tion were recruited. We enrolled 312 patients (GHD 210, ISS 102) with normal sella MRI and temporally split them into trainingand test sets (7:3). Pituitary glands were semi-automatically segmented, and 110 radiomic features were extracted from the coro-nal T2-weighted images. Feature selection and model development were conducted by applying mutual information (MI) and alight gradient boosting machine, respectively. After training, the model’s performance was validated in the test set. We calculatedmean 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. Results: Ten radiomic features were selected by MI. The receiver operating characteristics curve of the developed model in thetest set was 0.705, with an accuracy of 70.6%. When analyzing SHAP plots, root mean squared values had the highest impact inthe model, followed by various texture features. In volumetric analysis, sagittal height showed a significant difference betweenGHD and ISS groups. Conclusion: Radiomic analysis of sella MRI may be able to differentiate between GHD and ISS in clinical practice for short-stat-ured children.
Abstract
Purpose: We 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 radiomicsprediction model for differentiating GHD from ISS and to evaluate the diagnostic performance thereof. Materials and Methods: Short stature pediatric patients diagnosed with GHD or ISS from March 2011 to July 2020 at our institu-tion were recruited. We enrolled 312 patients (GHD 210, ISS 102) with normal sella MRI and temporally split them into trainingand test sets (7:3). Pituitary glands were semi-automatically segmented, and 110 radiomic features were extracted from the coro-nal T2-weighted images. Feature selection and model development were conducted by applying mutual information (MI) and alight gradient boosting machine, respectively. After training, the model’s performance was validated in the test set. We calculatedmean 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. Results: Ten radiomic features were selected by MI. The receiver operating characteristics curve of the developed model in thetest set was 0.705, with an accuracy of 70.6%. When analyzing SHAP plots, root mean squared values had the highest impact inthe model, followed by various texture features. In volumetric analysis, sagittal height showed a significant difference betweenGHD and ISS groups. Conclusion: Radiomic analysis of sella MRI may be able to differentiate between GHD and ISS in clinical practice for short-stat-ured children.
참고문헌 (41)
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