基于骶髂 MRI 成像的早期骶髂关节炎自动识别放射组学算法研究。
Research on automatic recognition radiomics algorithm for early sacroiliac arthritis based on sacroiliac MRI imaging.
机构信息
Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, 34 Zhongshan North Road, Quanzhou, 362000, China.
出版信息
J Orthop Surg Res. 2024 Jan 30;19(1):96. doi: 10.1186/s13018-024-04569-3.
OBJECTIVE
To create an automated machine learning model using sacroiliac joint MRI imaging for early sacroiliac arthritis detection, aiming to enhance diagnostic accuracy.
METHODS
We conducted a retrospective analysis involving 71 patients with early sacroiliac arthritis and 85 patients with normal sacroiliac joint MRI scans. Transverse T1WI and T2WI sequences were collected and subjected to radiomics analysis by two physicians. Patients were randomly divided into training and test groups at a 7:3 ratio. Initially, we extracted the region of interest on the sacroiliac joint surface using ITK-SNAP 3.6.0 software and extracted radiomic features. We retained features with an Intraclass Correlation Coefficient > 0.80, followed by filtering using max-relevance and min-redundancy (mRMR) and LASSO algorithms to establish an automatic identification model for sacroiliac joint surface injury. Receiver operating characteristic (ROC) curves were plotted, and the area under the ROC curve (AUC) was calculated. Model performance was assessed by accuracy, sensitivity, and specificity.
RESULTS
We evaluated model performance, achieving an AUC of 0.943 for the SVM-T1WI training group, with accuracy, sensitivity, and specificity values of 0.878, 0.836, and 0.943, respectively. The SVM-T1WI test group exhibited an AUC of 0.875, with corresponding accuracy, sensitivity, and specificity values of 0.909, 0.929, and 0.875, respectively. For the SVM-T2WI training group, the AUC was 0.975, with accuracy, sensitivity, and specificity values of 0.933, 0.889, and 0.750. The SVM-T2WI test group produced an AUC of 0.902, with accuracy, sensitivity, and specificity values of 0.864, 0.889, and 0.800. In the SVM-bimodal training group, we achieved an AUC of 0.974, with accuracy, sensitivity, and specificity values of 0.921, 0.889, and 0.971, respectively. The SVM-bimodal test group exhibited an AUC of 0.964, with accuracy, sensitivity, and specificity values of 0.955, 1.000, and 0.875, respectively.
CONCLUSION
The radiomics-based detection model demonstrates excellent automatic identification performance for early sacroiliitis.
目的
利用骶髂关节 MRI 成像创建用于早期骶髂关节炎检测的自动化机器学习模型，旨在提高诊断准确性。
方法
我们进行了一项回顾性分析，纳入了 71 例早期骶髂关节炎患者和 85 例骶髂关节 MRI 正常的患者。采集了横断 T1WI 和 T2WI 序列，并由两位医师进行放射组学分析。患者按 7:3 的比例随机分为训练组和测试组。首先，我们使用 ITK-SNAP 3.6.0 软件在骶髂关节表面提取感兴趣区，并提取放射组学特征。我们保留了组内相关系数（Intraclass Correlation Coefficient，ICC）>0.80 的特征，然后使用最大相关性和最小冗余度（max-relevance and min-redundancy，mRMR）和 LASSO 算法进行过滤，以建立骶髂关节表面损伤的自动识别模型。绘制接收器工作特征（receiver operating characteristic，ROC）曲线，并计算 ROC 曲线下的面积（area under the ROC curve，AUC）。通过准确性、敏感性和特异性评估模型性能。
结果
我们评估了模型性能，SVM-T1WI 训练组的 AUC 为 0.943，准确性、敏感性和特异性分别为 0.878、0.836 和 0.943。SVM-T1WI 测试组的 AUC 为 0.875，相应的准确性、敏感性和特异性分别为 0.909、0.929 和 0.875。对于 SVM-T2WI 训练组，AUC 为 0.975，准确性、敏感性和特异性分别为 0.933、0.889 和 0.750。SVM-T2WI 测试组的 AUC 为 0.902，准确性、敏感性和特异性分别为 0.864、0.889 和 0.800。在 SVM-双模态训练组中，我们获得了 0.974 的 AUC，准确性、敏感性和特异性分别为 0.921、0.889 和 0.971。SVM-双模态测试组的 AUC 为 0.964，准确性、敏感性和特异性分别为 0.955、1.000 和 0.875。
结论
基于放射组学的检测模型对早期骶髂关节炎具有出色的自动识别性能。
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