Integrating preoperative imaging and clinical factors: a nomogram for predicting functional outcomes after rotator cuff repair

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: = 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: < 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.