Background Superior labrum anterior and posterior (SLAP) lesions are a common cause of shoulder pain and instability. Accurate diagnosis remains challenging in clinical practice. This study aims to develop and evaluate radiomics models and combined models integrating radiomics and clinical features for SLAP lesion detection. Methods This retrospective study included 149 patients who underwent shoulder arthroscopic surgery with preoperative shoulder magnetic resonance imaging (MRI) between 2019 and 2024. Regions of interest (ROIs) were manually delineated on MRI oblique coronal proton density-weighted fat-suppressed (PD FS) images, and radiomics features were subsequently extracted from these defined regions. Feature selection employed independent t-tests, Mann-Whitney U tests, Pearson correlation analysis, and least absolute shrinkage and selection operator (LASSO) regression. Common machine learning models including Support Vector Machine (SVM), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM) were employed to construct diagnostic models based on radiomics features. A combined model integrating radiomics and clinical features was developed and visualized using nomograms. Results In the test cohort, the LightGBM-based radiomics model achieved optimal performance with the Area Under the Curve (AUC) of 0.867, sensitivity of 0.952, and specificity of 0.625. The combined model demonstrated enhanced diagnostic capability with AUC of 0.899, sensitivity of 0.762, and specificity of 0.917. Manual diagnosis of SLAP injury using MRI achieved an accuracy of 50.3%, with a sensitivity of 27.7%, specificity of 78.8%, and AUC of 0.619. Conclusion Machine learning models based on MRI radiomics features demonstrated superior diagnostic accuracy compared to traditional radiologist assessment for SLAP lesions. The combined model incorporating both radiomics and clinical features provides effective risk prediction for SLAP lesions.
Wang et al. (Thu,) studied this question.