Novel methods to diagnose rotator cuff tear and predict post-operative Re-tear: Radiomics models

Yang Fei; Yidong Wan; Lei Xu; Zizhan Huang; Dengfeng Ruan; Canlong Wang; Peiwen He; Xiaozhong Zhou; Boon Chin Heng; Tianye Niu; Weiliang Shen; Yan Wu

doi:10.1016/j.asmart.2024.03.003

Novel methods to diagnose rotator cuff tear and predict post-operative Re-tear: Radiomics models

Asia Pac J Sports Med Arthrosc Rehabil Technol. 2024 May 6:37:14-20. doi: 10.1016/j.asmart.2024.03.003. eCollection 2024 Jul.

Authors

Yang Fei^{1

2

3}, Yidong Wan^{4

5}, Lei Xu⁶, Zizhan Huang^{1

2

3}, Dengfeng Ruan^{1

2

3}, Canlong Wang^{1

2

3}, Peiwen He^{1

2

3}, Xiaozhong Zhou^{1

2

3}, Boon Chin Heng⁷, Tianye Niu^{4

5}, Weiliang Shen^{1

2

3}, Yan Wu^{1

2

3}

Affiliations

¹ Department of Orthopedics, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
² Key Laboratory of Motor System Disease Research and Precision Therapy of Zhejiang Province, Hangzhou, Zhejiang, China.
³ Orthopedics Research Institute of Zhejiang University, Hangzhou, Zhejiang, China.
⁴ Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
⁵ Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
⁶ Department of Radiation Oncology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
⁷ School of Stomatology, Peking University, Beijing, China.

Abstract

Objective: To validated a classifier to distinguish the status of rotator cuff tear and predict post-operative re-tear by utilizing magnetic resonance imaging (MRI) markers.

Methods: This retrospective study included patients with healthy rotator cuff and patients diagnosed as rotator cuff tear (RCT) by MRI. Radiomics features were identified from the pre-operative shoulder MRI and selected by using maximum relevance minimum redundancy (MRMR) methods. A radiomics model for diagnosis of RCT was constructed, based on the 3D volume of interest (VOI) of supraspinatus. Another model for the prediction of rotator re-tear after rotator cuff repair (Re-RCT) was constructed based on VOI of humerus, supraspinatus, infraspinatus and other clinical parameters.

Results: The model for diagnosing the status of RCT produced an area under the receiver operating characteristic curve (AUC) of 0.989 in the training cohort and 0.979 for the validation cohort. The radiomics model for predicting Re-RCT produced an AUC of 0.923 ± 0.017 for the training dataset and 0.790 ± 0.082 for the validation dataset. The nomogram combining radiomics features and clinical factors yielded an AUC of 0.961 ± 0.020 for the training dataset and 0.808 ± 0.081 for the validation dataset, which displayed the best performance among all models.

Conclusion: Radiomics models for the diagnosis of rotator cuff tear and prediction of post-operative Re-RCT yielded a decent prediction accuracy.

Keywords: Magnetic resonance imaging; Radiomics; Rotator cuff injuries.