Abstract
OBJECTIVE: The predictive value of a nomogram model constructed by integrating radiomics features and clinical risk factors for the functional outcomes of patients after rotator cuff repair was evaluated. METHODS: A total of 367 patients who underwent rotator cuff repair from January 2021 to December 2023 were selected. Pre - operative shoulder MRI images were collected and radiomics features were extracted, and clinical baseline data were also collected. The patients were randomly divided into a training set (n = 257) and a validation set (n = 110) at a ratio of 7:3. In the training set, univariate analysis was used to identify factors associated with postoperative functional outcomes, which were evaluated by the Constant-Murley score at 12 months after surgery and classified into good or poor categories. Least absolute shrinkage and selection operator (LASSO) regression was used for radiomics feature dimensionality reduction and variable screening, and then independent predictive factors were identified by multivariate Logistic regression. A nomogram model was established accordingly. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the discrimination, calibration, and clinical utility of the model, respectively. RESULTS: Multivariate analysis showed that age, pre - operative visual analog scale score, tear area, tear maximum length, tendon retraction distance, standard deviation of gray - scale, and entropy of the gray - level co - occurrence matrix were independent predictive factors for poor postoperative functional outcomes in patients undergoing rotator cuff repair (P < 0.05). The AUCs of the nomogram model developed based on these factors were 0.817 (95% CI: 0.750-0.883) in the training set and 0.721 (95% CI: 0.600-0.843) in the validation set, respectively. The calibration curve showed good consistency between the predicted probability and the actual risk. CONCLUSION: The nomogram model integrating radiomics features and clinical factors has potential utility in predicting functional outcomes after rotator cuff repair, and may thus provide a valuable reference for clinical individualized treatment and prognosis assessment.