Zhan Jinfeng, Liu Song, Dong Cheng, Ge Yaqiong, Xia Xiaona, Tian Na, Xu Qi, Jiang Gang, Xu Wenjian, Cui Jiufa
Department of Radiology, the Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Shinan District, Qingdao, 266000, Shandong, China.
GE Healthcare China, Pudong New Town, No. 1, Huatuo Road, Shanghai, 210000, China.
Eur Radiol. 2023 Aug;33(8):5587-5593. doi: 10.1007/s00330-023-09523-1. Epub 2023 Mar 1.
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.
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.
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.
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.
• 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.
以关节镜检查为参考标准，开发并验证基于磁共振成像（MRI）的放射组学模型，用于评估肩关节内的冈上肌腱撕裂情况。
共纳入432例患者（训练集332例，外部验证集100例），其中冈上肌腱完整的患者202例，冈上肌腱撕裂的患者230例（130例全层撕裂，100例部分层撕裂）。从脂肪饱和T2加权冠状位图像中提取放射组学特征。使用多变量逻辑回归分析构建并计算了两个用于检测冈上肌腱异常（撕裂与否）、病变严重程度分期（全层或部分层撕裂）以及放射组学评分（Rad-score）的放射组学特征模型。在训练集和验证数据集上使用ROC曲线验证了这两个模型的诊断性能。
对于无撕裂或有撕裂的放射组学模型，利用磁共振图像的13个特征构建放射组学特征，训练集的AUC值为0.98，内部验证集为0.97，外部验证集为0.98。对于全层或部分层撕裂的放射组学模型，利用磁共振图像的13个特征构建放射组学特征，训练集的AUC值为0.79，内部验证集为0.69，外部验证集为0.77。
本研究中提出的放射组学模型能够准确排除冈上肌腱撕裂，并能够基于肩部磁共振图像以中等准确度评估撕裂的严重程度分期。
• 无撕裂或有撕裂的放射组学模型对冈上肌腱撕裂的总体准确度高达93.6%，灵敏度为91.6%，特异性为95.2%。 • 全层或部分层撕裂的放射组学模型对冈上肌腱撕裂严重程度分期的表现中等，准确度为76.4%，灵敏度为79.2%，特异性为74.3%。
