Deep learning-radiomics assessment of intervertebral disc and paraspinal muscle heterogeneity for predicting postoperative recurrent lumbar disc herniation

Objective Although imaging and paraspinal muscle parameters are linked to postoperative recurrent lumbar disc herniation (PRLDH), micro-level texture characteristics and their interactions remain underexplored. This study applied deep learning (DL)-radiomics to quantify the microstructural heterogeneity of responsible intervertebral discs and paraspinal muscles (L3-S1), and assessed a combined disc-muscle model for predicting PRLDH. Method Clinical and imaging data from 170 lumbar disc herniation (LDH) patients undergoing percutaneous transforaminal endoscopic surgery (Jan 2022-Dec 2024) were retrospectively analyzed. DL and radiomics features were extracted from intervertebral discs and paraspinal muscles. Feature selection via mutual information was followed by construction of a DL-radiomics Radscore model. Internal validation used leave-one-out, 10-fold cross-validation, and bootstrapping. Pfirrmann grading performance was compared with the disc Radscore, and potential disc-muscle interactions were explored using optimal cutoffs. Results Among 170 patients, 39 had postoperative recurrence. Disc Radscore included 2 DL and 3 radiomics features, while muscle Radscore comprised 2 DL and 5 radiomics features. The disc Radscore demonstrated good predictive ability (AUC 0.857, 95% CI 0.797–0.918) across validation methods (AUC 0.846–0.857). Muscle Radscore showed moderate performance (AUC 0.718, 95% CI 0.627–0.809). Pfirrmann grade poorly predicted recurrence (AUC 0.506, 95% CI 0.412–0.600). Combined disc-muscle analysis was less stable than disc Radscore alone. Conclusion DL-radiomics-derived intervertebral disc Radscore robustly predicts PRLDH. While combined disc-muscle assessment is less consistent, their interactions may inform postoperative risk stratification and management in LDH patients.