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

Area of Science:

• Neurosurgery
• Radiology
• Medical Imaging

Background:

• Cervical spondylotic myelopathy (CSM) is a degenerative condition affecting the spinal cord.
• Predicting surgical outcomes in CSM patients is crucial for treatment planning.
• Current prognostic methods may not fully capture the nuances of spinal cord injury in CSM.

Purpose of the Study:

• To develop a predictive nomogram for surgical prognosis in CSM patients.
• To incorporate apparent diffusion coefficient (ADC) values of the cervical spinal cord into the prognostic model.
• To evaluate the predictive performance of a model including clinical and MRI-ADC factors.

Main Methods:

• 100 CSM patients undergoing decompression surgery were enrolled.
• Conventional cervical spine MRI and diffusion-weighted imaging (DWI) were performed preoperatively.
• A nomogram was developed using multivariate logistic regression, comparing models with and without ADC values.
• Predictive performance was assessed using ROC, calibration curves, and decision curve analysis (DCA).

Main Results:

• The nomogram incorporating ADC values demonstrated superior predictive performance (AUC 0.933) compared to a clinical-MRI factors model (AUC 0.864).
• The ADC-based model showed better calibration, resolution, and accuracy.
• Decision curve analysis indicated superior clinical utility for the ADC-based model in predicting poor recovery.

Conclusions:

• Apparent diffusion coefficient (ADC) values of the cervical spinal cord are valuable predictors of surgical outcomes in CSM.
• The developed nomogram effectively predicts postoperative neurological recovery in CSM patients.
• Integrating ADC values into prognostic models enhances prediction accuracy for CSM surgical outcomes.