Advancing Precision Medicine in Degenerative Cervical Myelopathy

Area of Science:

• Neurology
• Spinal Cord Medicine
• Medical Imaging

Background:

• Degenerative cervical myelopathy (DCM) is a primary cause of non-traumatic spinal cord dysfunction.
• DCM presents with significant clinical heterogeneity in symptoms and disease progression.
• Accurate prediction of neurological outcomes is crucial for effective patient management.

Purpose of the Study:

• To review predictors of neurological outcomes in DCM.
• To explore the integration of conventional and advanced quantitative neuroimaging metrics.
• To examine the role of machine learning in a precision medicine framework for DCM.

Main Methods:

• Systematic review of existing literature on DCM prognostic factors.
• Assessment of conventional clinical and macrostructural metrics.
• Evaluation of quantitative MRI biomarkers (diffusion tensor imaging, magnetization transfer, myelin water imaging).
• Analysis of machine learning models incorporating diverse data types.

Main Results:

• Conventional prognostic factors show limited standalone utility due to inconsistency.
• Quantitative MRI biomarkers offer enhanced risk stratification by indexing microstructural integrity.
• Machine learning models effectively integrate clinical, imaging, and demographic data for outcome prediction.
• ML models show promise in predicting postoperative outcomes and natural history of mild DCM.

Conclusions:

• Multifactorial modeling, particularly with machine learning, is essential for accurate DCM prognosis.
• Quantitative neuroimaging biomarkers significantly improve predictive capabilities.
• Precision medicine tools, driven by ML, are vital for personalized DCM management.
• Future research requires protocol harmonization and multicenter validation for widespread adoption.