A novel method based on ultrasound radiomics for predicting attainment of near-adult height in adolescents.
Authors
Affiliations (4)
Affiliations (4)
- Department of Ultrasound, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
- Department of Anesthesiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
- Zhangzhou Municipal Hospital Affiliated to Fujian Medical University, Zhangzhou, Fujian, China.
Abstract
This study aimed to develop and validate a novel ultrasound radiomics model for predicting the attainment of near-adult height (NAH) in adolescents. This dual-center prospective cohort study enrolled 192 adolescents aged 11.0-18.0 years. Standardized ultrasound examinations of the ulnar growth plate region were performed, and 475 radiomic features were extracted. Feature selection was conducted using Least Absolute Shrinkage and Selection Operator (LASSO) algorithm with 5-fold cross-validation. Seven machine learning algorithms were compared to develop predictive models. Three models were constructed: clinical (sex and bone age), radiomics (Rad-score), and combined (sex, bone age, and Rad-score). Model performance was evaluated using area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, calibration curves, and decision curve analysis. Three stable radiomic features were selected with selection frequency ≥80%: wavelet-LH_glcm_Imc2, wavelet-LL_ngtdm_Busyness, and wavelet-HL_firstorder_Energy. The combined model demonstrated superior performance, achieving AUCs of 0.981 (95% CI: 0.948-1.000) in the validation set, with sensitivity of 90% and specificity of 100%. Multivariate analysis identified independent positive predictors including Rad-score (OR = 3.28, P = 0.013) and bone age (OR = 2.65, P < 0.001). And male sex was a significant negative predictor (OR = 0.15, P = 0.005). The combined ultrasound radiomics machine learning model achieved excellent predictive performance for NAH attainment in adolescents. The non-ionizing ultrasound approach enables repeated dynamic monitoring, and the derived nomogram facilitates individualized risk stratification in routine pediatric practice.