Fusion level selection in posterior hemivertebra resection for congenital scoliosis: clinical effect size driven feature selection and multi-criteria optimization decision model.

Study design: Retrospective study. Objectives: There is a lack of objective criteria for the selection of fixed segment length in hemivertebra resection and fusion. This study aimed to develop and validate decision models to guide the selection of the best surgical strategy, based on radiographic parameters. Methods: Thirty six patients who underwent posterior hemivertebra resection and fusion were included. Preoperative, postoperative and follow-up imaging data were integrated, and the key predictors were screened by effect size. Multi-weight threshold analysis was used to determine the optimal decision threshold of each factor. Classification and regression tree (CART) algorithm was used to construct the prediction model of fusion strategy selection, and the performance was evaluated by leave-one-out cross validation. Results: Lumbar lordosis, thoracolumbar kyphosis, and pelvic tilt were identified as predictors to distinguish segments fusion. Model was stratified according to locations of the hemivertebra, with an accuracy of 93.5% and an area under the curve (AUC) of 0.982. The core predictors maintained stable discriminative power across anatomical subgroups. Conclusions: The decision tree model based on preoperative imaging can effectively distinguish the patients with hemivertebra deformity requiring long- and short-segment fusion, which provides an objective decision-making tool for optimizing the surgical strategy, balancing the correction effect and function preservation.