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Noninvasive radiomics-based method for evaluating idiopathic central precocious puberty in girls
Authors:Hongyang Jiang  Zhenyu Shu  Xiaoming Luo  Meizhen Wu  Mei Wang  Qi Feng  Junfa Chen  Chunmiao Lin  Zhongxiang Ding
Affiliation:1.Department of Radiology, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical College, Hangzhou, China; 2.Department of Pediatrics, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical College, Hangzhou, China; 3.Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
Abstract:ObjectiveTraditional approaches that involve measuring the height and volume of the pituitary by magnetic resonance imaging (MRI) are unreliable. We investigated the use of a more accurate method using texture analysis to evaluate idiopathic central precocious puberty (ICPP) by MRI.MethodsIn total, 352 texture features of the pituitary were extracted from 12 healthy girls and 18 girls with ICPP. A LASSO regression model and linear regression model were used to create the prediction model. Pearson’s correlation analysis and receiver operating characteristic curves were used to evaluate the predictive performance.ResultsThe radiomics score had a significant linear relationship with the luteinizing hormone concentration and the luteinizing hormone/follicle-stimulating hormone ratio. The radiomics score showed better predictive performance than traditional pituitary measurements. The area under the curve of the radiomics score, pituitary height, and variable combinations was 0.759 (95% confidence interval [CI], 0.583–0.936), 0.681 (95% CI, 0.483–0.878), and 0.829 (95% CI, 0.681–0.976), respectively.ConclusionCombination of the radiomics score with pituitary height measurements allows for better evaluation of the pituitary during diagnostic imaging, indicating satisfactory potential for efficacy assessments.
Keywords:Radiomics   texture features   precocious puberty   magnetic resonance imaging   pituitary   regression model
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