Establishment and validation of a ResNet-based radiomics model for predicting prognosis in cervical spinal cord injury patients.

ABSTRACT: Cervical spinal cord injury (cSCI) poses a significant challenge due to the unpredictable nature of recovery, which ranges from mild paralysis to severe long-term disability. Accurate prognostic models are crucial for guiding treatment and rehabilitation but are often limited by their reliance on clinical observations alone. Recent advancements in radiomics and deep learning have shown promise in enhancing prognostic accuracy by leveraging detailed imaging data. However, integrating these imaging features with clinical data remains an underexplored area. This study aims to develop a combined model using imaging and clinical signatures to predict the prognosis of cSCI patients six months post-injury, helping clinical decisions and improving rehabilitation plans. We retrospectively analyzed

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC11914052 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature