Abstract
Objective
This study aimed to develop and validate a novel ultrasound radiomics model for predicting the attainment of near-adult height (NAH) in adolescents.
Methods
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.
Results
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
Conclusion
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.