Nomogram for predicting the postoperative outcomes in cervical spondylotic myelopathy based on apparent diffusion coefficient.

Abstract

Purpose: To development a nomogram based on clinical features and apparent diffusion coefficient (ADC) of the cervical spinal cord in surgical prognosis in patients with cervical spondylotic myelopathy (CSM).

Methods: Patients with CSM who underwent decompression surgery between March and September 2023 were enrolled. Patients underwent conventional cervical spine MRI and sagittal position ZOOM-DWI before surgery. Recovery rate of neurological function was calculated based on the mJOA before and 6 months after surgery. According to recovery rate, patients were divided into good-recovery group (> 50%) and poor-recovery group (< 50%). Clinical- MRI factors model (Model 1) and Clinical-MRI-ADC factors model (Model2) were bulid by multivariate logistic regression to predict. Receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) were utilized to compare the predictive performance of the two models. A nomogram based on model 2 was constructed to predict poor recovery rate.

Results: 100 CSM patients were included in this study, including 60 patients in the good-recover group and 40 patients in the poor-recover group. Compared with model 1, the nomogram based on model 2 had a better AUC (0.933vs0.864). The calibration curve of model 2 is closer to the reference line, which indicates that model 2 has better resolution and accuracy. The DCA curve analysis of model 2 also showed better clinical utility. The nomogram based on model 2 performs well in predicting poor recovery rates.

Conclusion: The nomogram based on ADC values can effectively predict the outcome of postoperative neurological recovery in CSM patients.