The value of 2D and 3D MRI texture models in Grade II and III anterior cruciate ligament injuries

Abstract

Objective

To evaluate the diagnostic value 2D and 3D texture models for Grade II and III anterior cruciate ligament (ACL) injuries.

Materials and methods

Patients diagnosed with grade II and III ACL injuries through MRI examinations at our Hospital from January 2023 to December 2023 will be collected as the experimental group (n = 166). These cases were randomly stratified into training and validation sets with a ratio of 7:3. ACL was delineated, and texture features were extracted to establish both 2D and 3D models. The models were evaluated using a test set of patients who underwent surgery for confirmation(n = 81). Diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUC). Differences were compared using the DeLong test. The clinical value of texture models were assessed using clinical decision curve and calibration curves.

Results

A total of 247 cases from a single center were included. 2D and 3D texture models were constructed using three algorithms: RandomForest, Extra Trees, and XGBoost. For 2D texture models, the AUC values for the training, validation, and test sets were (0.998, 0.873, 0.697), (0.930, 0.778, 0.615), and (1.000, 0.821, 0.755), respectively. Corresponding AUC values for 3D models were (0.939, 0.899, 0.861), (0.852, 0.831, 0.826), and (0.982, 0.890, 0.728), respectively. DeLong test results, combined with clinical decision curve and calibration analysis, indicated that the 3D texture model using Random Forest outperformed others.

Conclusion

The 3D model using Random Forest showed high validity and stability in the diagnosis of grade II and III ACL injuries.