Texture Analysis of Routine Shoulder MRI for Predicting Postoperative Rotator Cuff Repair Integrity After...

Introduction Rotator cuff retear remains a major complication of arthroscopic rotator cuff repair (ARCR) and necessitates practical imaging-based methods for preoperative risk stratification. This study investigated whether texture features (TFs) extracted from routine shoulder MRI can predict postoperative tendon integrity, including the intermediate Sugaya type III category. Methods This retrospective study included 31 patients who had undergone ARCR. On preoperative shoulder MRI, the supraspinatus and infraspinatus muscles were manually segmented on a single oblique sagittal Y-view slice using proton density-weighted imaging (PDWI) and fat-suppressed T2-weighted imaging (FS-T2WI). Ninety-three TFs were extracted from each muscle in each sequence. Sequential feature selection (SFS) was applied, and classification models were developed using linear discriminant analysis (LDA), support vector machines (SVMs) with linear and radial basis function (RBF) kernels, and random forest (RF). Two binary classification tasks were evaluated: Sugaya types I-II versus III-V and Sugaya types I-II versus IV-V. Results For Sugaya types I-II versus III-V, the best single model used PDWI-derived supraspinatus features (accuracy, 0.792; receiver operating characteristic area under the curve (ROC AUC), 0.732). The best combination model achieved an accuracy of 0.815 and a ROC AUC of 0.833. For Sugaya types I-II versus IV-V, the best single model used the PDWI-derived combined supraspinatus and infraspinatus features (accuracy, 0.880; ROC AUC, 0.908). The best combination model achieved an accuracy of 0.839 and an ROC AUC of 0.864. Conclusion Texture analysis (TA) using routine preoperative shoulder MRI may help predict postoperative rotator cuff repair integrity, particularly the presence of definite postoperative tendon discontinuities.