Enhancing the Diagnosis of Discogenic Low Back Pain: A SHAP-Explained Random Forest Model Combined with Lumbar Spine MRI T2 Radiomics

Low back pain (LBP) is a leading cause of disability and reduced quality of life globally, with discogenic low back pain (DLBP) accounting for 39% of cases. Accurate diagnosis of LBP etiology is challenging due to the lack of reliable methods. This study aims to improve DLBP diagnostic efficiency using lumbar spine MRI T2 data combined with radiomics and machine learning. This retrospective study analyzed MRI data from 81 DLBP patients and 162 healthy controls. Radiomics features, clinical data, and high-intensity zone (HIZ) imaging features were extracted. The data were divided into four groups (d0, d1, d2, D), and 20 predictive models were built using Random Forest (RF), Decision Tree (TREE), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression (LOG). Model performance was evaluated using Receiver Operating Characteristic (ROC) area under the curve (AUC), precision recall (PR) AUC, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. SHapley Additive exPlanations (SHAP) were applied to interpret the most significant features. The Random Forest in group D showed the best performance, with ROC AUCs of 0.9861 (train) and 0.9580 (test), PR AUCs of 0.9813 and 0.9179, and F1 scores of 0.9254 and 0.8148, respectively. SHAP analysis identified first-order kurtosis as the top feature contributing to DLBP diagnosis. The Random Forest model with SHAP analysis significantly improved DLBP diagnosis, offering high performance and interpretability to enhance clinical decision-making.