JOR spine · 2026
Explainable Supervised Learning Classification Model Using Diffusion Tensor Imaging Predicts Postoperative Outcomes in Cervical Spondylotic Myelopathy: A Preliminary Study.
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Peng Yifei, Zhang Zixuan, Zhang Zhikun, Shi Kaiyi, Wang Ruoyu, Liu Yue, Zhang Li
Аннотация
BACKGROUND: Cervical spondylotic myelopathy (CSM) is common in older adults. Some patients may experience incomplete neurological recovery after surgery, or even deterioration. Accurate prognosis is essential for patients, yet current tools use subjective scores and fail to detect early spinal cord microstructural changes.
METHODS: CSM patients undergoing preoperative cervical MRI and diffusion tensor imaging (DTI) scans between 2024 and 2025 at a single center were included in this retrospective study. Data were retrieved from medical records, and the determination of whether patients achieved minimal clinically important difference (MCID) was based on the change in modified Japanese Orthopaedic Association (mJOA) scores. Three supervised learning classification models, namely extreme gradient boosting (XGB), logistic regression (LR), and support vector machine (SVM), were constructed based on DTI and clinical risk factors. The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC) and precision-recall curve (AP), and decision curve analysis (DCA). The contribution of each feature to model prediction was visualized by SHapley Additive exPlanations (SHAP).
RESULTS: The training set included 163 patients (mean age: 54.43 ± 10.27 years; 57 males), whereas the testing set included 71 patients (mean age: 54.86 ± 12.25 years; 23 males). The AUCs for XGB, LR, and SVM models in the training set were 0.940, 0.791, and 0.908 respectively (p < 0.05). The XGB model showed the best performance in the training set (AUC = 0.940, AP = 0.948), and the results in the testing set demonstrated moderate discriminative ability with good precision (AUC = 0.754; 95% CI, 0.643-0.857, AP = 0.851; 95% CI, 0.742-0.937). DCA showed that the clinical utility of the XGB model was relatively high.
CONCLUSION: An explainable XGB model based on DTI and clinical risk factors provides a foundation for preoperative risk stratification of MCID achievement in postoperative CSM.
METHODS: CSM patients undergoing preoperative cervical MRI and diffusion tensor imaging (DTI) scans between 2024 and 2025 at a single center were included in this retrospective study. Data were retrieved from medical records, and the determination of whether patients achieved minimal clinically important difference (MCID) was based on the change in modified Japanese Orthopaedic Association (mJOA) scores. Three supervised learning classification models, namely extreme gradient boosting (XGB), logistic regression (LR), and support vector machine (SVM), were constructed based on DTI and clinical risk factors. The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC) and precision-recall curve (AP), and decision curve analysis (DCA). The contribution of each feature to model prediction was visualized by SHapley Additive exPlanations (SHAP).
RESULTS: The training set included 163 patients (mean age: 54.43 ± 10.27 years; 57 males), whereas the testing set included 71 patients (mean age: 54.86 ± 12.25 years; 23 males). The AUCs for XGB, LR, and SVM models in the training set were 0.940, 0.791, and 0.908 respectively (p < 0.05). The XGB model showed the best performance in the training set (AUC = 0.940, AP = 0.948), and the results in the testing set demonstrated moderate discriminative ability with good precision (AUC = 0.754; 95% CI, 0.643-0.857, AP = 0.851; 95% CI, 0.742-0.937). DCA showed that the clinical utility of the XGB model was relatively high.
CONCLUSION: An explainable XGB model based on DTI and clinical risk factors provides a foundation for preoperative risk stratification of MCID achievement in postoperative CSM.