Shoulder MRI-based radiomics for diagnosis and severity staging assessment of surgically treated supraspinatus tendon tears.

OBJECTIVE

To develop and validate MRI-based radiomics models capable of evaluating supraspinatus tendon tears within the shoulder joints by using arthroscopy as the reference standard.

METHODS

A total of 432 patients (332 in the training set and 100 in the external validation set) with intact supraspinatus tendon (n = 202) and supraspinatus tendon tear (n = 230, 130 full-thickness tears and 100 partial-thickness tears) were enrolled. Radiomics features were extracted from fat-saturated T2-weighted coronal images. Two radiomics signature models for detecting supraspinatus tendon abnormalities (tear or not), and stage lesion severity (full- or partial-thickness tear) and radiomics scores (Rad-score), were constructed and calculated using multivariate logistic regression analysis. The diagnostic performance of the two models was validated using ROC curves on the training and validation datasets.

RESULTS

For the radiomics model of no tears or tears, thirteen features from MR images were used to build the radiomics signature with an AUC value of 0.98 in the training set, 0.97 in the internal validation set, and 0.98 in the external validation set. For the radiomics model of full- or partial-thickness tears, thirteen features from MR images were used to build the radiomics signature with an AUC value of 0.79 in the training set, 0.69 in the internal validation set, and 0.77 in the external validation set.

CONCLUSION

The proposed radiomics models in this study can accurately rule out supraspinatus tendon tears and are capable of assessing the severity staging of tears with moderate accuracy based on shoulder MR images.

KEY POINTS

• The radiomics model of no tears or tears achieved a high overall accuracy of 93.6%, sensitivity of 91.6%, and specificity of 95.2% for supraspinatus tendon tears. • The radiomics model of full- or partial-thickness tears displayed moderate performance with an accuracy of 76.4%, a sensitivity of 79.2%, and a specificity of 74.3% for supraspinatus tendon tears severity staging.