Four MRI-Based Radiomics Models for Diagnosis of Lumbar Intervertebral Disc Degeneration

Lumbar intervertebral disc degeneration (LIDD) is a leading cause of low back pain, with subtle and variable imaging features that challenge early diagnosis. This study aimed to develop and validate a rigorous MRI-based radiomics ensemble model for disc-level LIDD discrimination, using the patient as the primary sampling unit, with explicit statistical correction for the non-independence of multiple lumbar discs from the same patient. This retrospective single-center study enrolled 122 subjects (102 LIDD patients and 20 healthy controls), contributing a total of 610 lumbar discs. Regions of interest (ROIs) of intervertebral discs were manually segmented on fat-suppressed T2-weighted imaging (FS-T2WI) sequences, and 1409 Image Biomarker Standardization Initiative (IBSI)-compliant radiomic features were extracted. To account for within-patient clustering of discs, multi-step feature selection was performed in the patient-level split training set, including Generalized Estimating Equations (GEE), Benjamini-Hochberg FDR correction, Spearman correlation-based redundancy removal, and L1-regularized logistic regression. Three base classifiers (logistic regression (LR), random forest (RF), radial basis function SVM) and a soft-voting ensemble model were trained with patient-level fivefold group cross-validation to avoid data leakage. Model performance for disc-level LIDD diagnosis was evaluated via AUC, accuracy, sensitivity, and specificity in an independent patient-level test set, with SHapley Additive exPlanations (SHAP) for model interpretability. A compact, reproducible radiomic signature was derived from the final selected features. All models achieved excellent diagnostic performance in the independent test set: RF (AUC = 0.966, 95% CI: 0.937-0.988), SVM (AUC = 0.974, 95% CI: 0.949-0.992), and LR (AUC = 0.974, 95% CI: 0.949-0.992). The soft-voting ensemble model achieved the best discrimination with an AUC of 0.976 (95% CI: 0.954-0.992), along with balanced sensitivity (88%) and specificity (96%). SHAP analysis identified key intensity- and texture-based radiomic features driving model predictions. The MRI-based radiomics ensemble model, built with rigorous statistical correction for within-patient clustering of discs and patient-level validation, enables accurate and interpretable disc-level LIDD discrimination. This model shows strong promise for assisting the early detection and objective diagnosis of LIDD in clinical practice.