Radiomics analysis of lumbar spine X-ray images for diagnosing facet joint osteoarthritis: a two-center study

Abstract Objectives This study aims to develop and validate radiomics models utilizing lumbar spine X-rays for the early identification of facet joint osteoarthritis (FJOA). Methods This retrospective two-center study enrolled 1,997 patients who underwent paired lumbar X-ray and CT imaging within one month. Data from one center were used for model training and validation, and data from the other center were used for external testing. Radiomic features were extracted from manually segmented facet joint regions on X-rays. Key features selected through the least absolute shrinkage and selection operator (LASSO) were used to develop models, specifically logistic regression, linear support vector classification (LinearSVC), and support vector machines (SVM). The model performance was primarily evaluated using the area under the ROC curve (AUC) and the area under the precision-recall curve (AUPRC). Results A total of 20 features were selected for modeling. The logistic regression model based on radiomic features demonstrated the highest AUC. In the external testing cohort, this model achieved an AUC of 0.971 (95% CI: 0.956–0.986), a sensitivity of 98.0%, a specificity of 75.0%, and an AUPRC of 0.839. It outperformed both the SVM model (AUC = 0.946, AUPRC = 0.793) and the LinearSVC model (AUC = 0.966, AUPRC = 0.813). Conclusion Radiomics models based on lumbar X-rays showed robust performance and hold promise as a non-invasive, accessible tool for early and accurate identification of FJOA, potentially enabling timely intervention.