Table 1_A novel method based on ultrasound radiomics for predicting attainment of near-adult height in adolescents.docx

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.</p>Results<p>Three stable radiomic features were selected with selection frequency ≥80%: wavelet-LH_glcm_Imc2, wavelet-LL_ngtdm_Busyness, and wavelet-HL_firstorder_Energy.