Department of Ultrasound, Haishu District, Ningbo NO.2 Hospital, No. 41, Northwest Street, Ningbo, Zhejiang, 315010, People's Republic of China.
Department of Ultrasound, Zhenhai Hospital of Traditional Chinese Medicine, No.51, Huancheng W Rd, Zhenhai District, Ningbo, 315200, Zhejiang, People's Republic of China.
Skeletal Radiol. 2024 Jul;53(7):1389-1397. doi: 10.1007/s00256-024-04594-7. Epub 2024 Jan 30.
The aim of our study is to develop and validate a radiomics model based on ultrasound image features for predicting carpal tunnel syndrome (CTS) severity.
This retrospective study included 237 CTS hands (106 for mild symptom, 68 for moderate symptom and 63 for severe symptom). There were no statistically significant differences among the three groups in terms of age, gender, race, etc. The data set was randomly divided into a training set and a test set in a ratio of 7:3. Firstly, a senior musculoskeletal ultrasound expert measures the cross-sectional area of median nerve (MN) at the scaphoid-pisiform level. Subsequently, a recursive feature elimination (RFE) method was used to identify the most discriminative radiomic features of each MN at the entrance of the carpal tunnel. Eventually, a random forest model was employed to classify the selected features for prediction. To evaluate the performance of the model, the confusion matrix, receiver operating characteristic (ROC) curves, and F1 values were calculated and plotted correspondingly.
The prediction capability of the radiomics model was significantly better than that of ultrasound measurements when 10 robust features were selected. The training set performed perfect classification with 100% accuracy for all participants, while the testing set performed accurate classification of severity for 76.39% of participants with F1 values of 80.00, 63.40, and 84.80 for predicting mild, moderate, and severe CTS, respectively. Comparably, the F1 values for mild, moderate, and severe CTS predicted based on the MN cross-sectional area were 76.46, 57.78, and 64.00, respectively..
This radiomics model based on ultrasound images has certain value in distinguishing the severity of CTS, and was slightly superior to using only MN cross-sectional area for judgment. Although its diagnostic efficacy was still inferior to that of neuroelectrophysiology. However, this method was non-invasive and did not require additional costs, and could provide additional information for clinical physicians to develop diagnosis and treatment plans.
本研究旨在开发和验证一种基于超声图像特征的影像组学模型，以预测腕管综合征（CTS）的严重程度。
本回顾性研究纳入了 237 只 CTS 手（轻度症状 106 只，中度症状 68 只，重度症状 63 只）。三组在年龄、性别、种族等方面无统计学差异。数据集以 7:3 的比例随机分为训练集和测试集。首先，一名高级肌肉骨骼超声专家测量舟状骨-钩骨水平正中神经（MN）的横截面积。随后，采用递归特征消除（RFE）方法识别每个 MN 在腕管入口处最具鉴别力的影像组学特征。最终，采用随机森林模型对所选特征进行分类预测。为评估模型性能，计算并相应绘制混淆矩阵、受试者工作特征（ROC）曲线和 F1 值。
当选择 10 个稳健特征时，影像组学模型的预测能力明显优于超声测量。训练集对所有参与者的分类准确率达到 100%，而测试集对 76.39%的参与者进行了准确的严重程度分类，F1 值分别为 80.00、63.40 和 84.80，分别用于预测轻度、中度和重度 CTS。相比之下，基于 MN 横截面积预测的轻度、中度和重度 CTS 的 F1 值分别为 76.46、57.78 和 64.00。
该基于超声图像的影像组学模型在区分 CTS 严重程度方面具有一定价值，且在判断方面略优于仅使用 MN 横截面积。虽然其诊断效能仍低于神经电生理学。然而，该方法具有非侵入性且无需额外费用，可为临床医生制定诊断和治疗方案提供额外信息。
