ArticleEuropean radiology2023
Shoulder MRI-based radiomics for diagnosis and severity staging assessment of surgically treated supraspinatus tendon tears.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 9 citations in OpenAlex.
- Radiomics-based MRI model for predicting the severity of rotator cuff tears.Orthopadie (Heidelberg, Germany) · 2026Article
- Hierarchical Multimodal Fusion of Multi-Sequence MRI and Clinical Metadata for the Classification of Rotator Cuff Tears.Journal of clinical medicine · 2026Article
- Artificial intelligence revolution in shoulder magnetic resonance imaging: current evidence and future directions for rotator cuff diagnosis.Clinics in shoulder and elbow · 2026Article
- Discriminative and predictive power of radiomics features from preoperative MRI studies in patients with supraspinatus lesions.BMC musculoskeletal disorders · 2026Article
- Patient-Level Classification of Rotator Cuff Tears on Shoulder MRI Using an Explainable Vision Transformer Framework.Journal of clinical medicine · 2026Article
- Noninvasive prediction of failure of the conservative treatment in lateral epicondylitis by clinicoradiological features and elbow MRI radiomics based on interpretable machine learning: a multicenter cohort study.Journal of orthopaedic surgery and research · 2025Article
- Artificial Intelligence in Rotator Cuff Tear Detection: A Systematic Review of MRI-Based Models.Diagnostics (Basel, Switzerland) · 2025Review
- Texture analysis of T1- and T2-weighted images identifies myofiber atrophy and grip strength decline in streptozotocin-induced type 1 diabetic sarcopenia rats.Journal of orthopaedic surgery and research · 2025Article
- An approach leveraging radiomics and model checking for the automatic early diagnosis of adhesive capsulitis.Scientific reports · 2024Article
- Enhancing prediction of supraspinatus/infraspinatus tendon complex injuries through integration of deep visual features and clinical information: a multicenter two-round assessment study.Insights into imaging · 2023Article
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10 authors at 2 institutions in 1 country.
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Abstract
objectiveTo develop and validate MRI-based radiomics models capable of evaluating supraspinatus tendon tears within the shoulder joints by using arthroscopy as the reference standard.
methodsA 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.
resultsFor 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.
conclusionThe 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.
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