World neurosurgery · 2025
Kissing Spine and Other Imaging Predictors of Postoperative Cement Displacement Following Percutaneous Kyphoplasty: A Machine Learning Approach.
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Zhao YingLun, Bo Li, Qian Lu, Chen XueMing, Wang YanHui, Cui LiBin, Xin Yuan, Liu Liang
Аннотация
OBJECTIVE: To investigate the risk factors associated with postoperative cement displacement following percutaneous kyphoplasty in patients with osteoporotic vertebral compression fractures and to develop predictive models for clinical risk assessment.
METHODS: This retrospective study included 198 patients with osteoporotic vertebral compression fracture who underwent percutaneous kyphoplasty. Imaging and clinical variables were collected. Multiple machine learning models, including logistic regression (LR), L1-and L2-regularized LR, support vector machine (SVM), decision tree, gradient boosting, and random forest, were developed to predict cement displacement.
RESULTS: L1-and L2-regularized LR models identified 4 key risk factors: kissing spine (L1: 1.11; L2: 0.91), incomplete anterior cortex (L1: -1.60; L2: -1.62), low vertebral body computed tomography (CT) value (L1: -2.38; L2: -1.71), and large Cobb change (L1: 0.89; L2: 0.87). The SVM model achieved the best performance (accuracy: 0.983, precision: 0.875, recall: 1.000, F1-score: 0.933, specificity: 0.981, area under the curve: 0.997). Other models, including LR, decision tree, gradient boosting, and random forest, also showed high performance but were slightly inferior to SVM.
CONCLUSIONS: Key predictors of cement displacement were identified, and machine learning models were developed for risk assessment. These findings can assist clinicians in identifying high-risk patients, optimizing treatment strategies, and improving patient outcomes.
METHODS: This retrospective study included 198 patients with osteoporotic vertebral compression fracture who underwent percutaneous kyphoplasty. Imaging and clinical variables were collected. Multiple machine learning models, including logistic regression (LR), L1-and L2-regularized LR, support vector machine (SVM), decision tree, gradient boosting, and random forest, were developed to predict cement displacement.
RESULTS: L1-and L2-regularized LR models identified 4 key risk factors: kissing spine (L1: 1.11; L2: 0.91), incomplete anterior cortex (L1: -1.60; L2: -1.62), low vertebral body computed tomography (CT) value (L1: -2.38; L2: -1.71), and large Cobb change (L1: 0.89; L2: 0.87). The SVM model achieved the best performance (accuracy: 0.983, precision: 0.875, recall: 1.000, F1-score: 0.933, specificity: 0.981, area under the curve: 0.997). Other models, including LR, decision tree, gradient boosting, and random forest, also showed high performance but were slightly inferior to SVM.
CONCLUSIONS: Key predictors of cement displacement were identified, and machine learning models were developed for risk assessment. These findings can assist clinicians in identifying high-risk patients, optimizing treatment strategies, and improving patient outcomes.