Postoperative One Year Prediction for Patients with Cervical Spinal Cord Injury Based on Deep Learning and Radiomics

Background: Cervical spinal cord injury (SCI) can lead to significant impairments, requiring extensive care and posing considerable challenges in predicting postoperative outcomes. This study aimed to develop and validate a deep learning radiomics (DLR) model combining deep learning and radiomics features to improve the prognostic prediction of cervical SCI. Methods: This retrospective study included 82 patients with confirmed cervical SCI from three hospitals, collected between January 2012 and January 2021. Patients were divided into good prognosis and poor prognosis groups based on postoperative ASIA grade improvement. Preoperative MRI images were processed using various filtering techniques, and regions of interest (ROI) were segmented and analyzed to extract radiomics features. Deep learning models (ResNet-18, ResNet-50, and ResNet-101) were trained. Features from both radiomics and deep learning models were combined and selected 、 to build the final predictive model using MLP. Results: ResNet-50 outperformed other models, demonstrating an AUC of 0.8750 in the test set. The combined model (Rad + ResNet-50) showed the highest prognostic value with an AUC of 0.9220 in the test set. Grad-CAM images enhanced the interpretability of the model by highlighting critical areas for prognosis prediction. Conclusion: Integrating deep learning and radiomics features significantly improves the prediction accuracy for cervical SCI outcomes. The Rad + ResNet-50 model, with its superior performance and interpretability, holds promise for clinical applications, offering a robust tool for predicting functional prognosis in cervical SCI patients. Further prospective studies with larger datasets are needed to validate these findings.