Zhang Zhaojuan, Pan Yiling, Lu Yanjie, Ye Lusi, Zheng Mo, Zhang Guodao, Chen Dan
College of Information Engineering, China Jiliang University, Zhejiang, Hangzhou, China.
Institute of Intelligent Media Computing, Hangzhou Dianzi University, Zhejiang, Hangzhou, China.
Int J Rheum Dis. 2024 Dec;27(12):e70004. doi: 10.1111/1756-185X.70004.
The aim of this study is to develop and validate a model for predicting axial spondyloarthritis (axSpA) based on sacroiliac joint (SIJ)-MRI imaging findings and clinical risk factors.
The study is implemented on the data of 942 patients which contains of 707 patients with axSpA and 235 patients with non-axSpA. To begin with, the patients were split into training (n = 753) and validation (n = 189) cohorts. Secondly, multiple assessors manually extract the features of active inflammation (bone marrow edema) and structural lesions (erosions, sclerosis, ankylosis, joint space changes, and fat lesions). Meanwhile, we utilize 11 machine learning models and TabNet to develop imaging models, which contain six clinical risk factors for clinical models and combined clinical-imaging models. Finally, the diagnostic performance of the aforementioned models was evaluated in the validation cohort including accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, F1-score, and Matthew's correlation coefficient (MCC).
Six features were extracted from the imaging findings. The combined clinical-imaging models outperform the clinical and imaging models. In contrast, the combined clinical-imaging model via TabNet (CCMRT) achieved the optimal AUC of 0.93(95% CI: 0.89, 0.97). Furthermore, it is observed that the bilateral joint space changes and right-sided erosions, HLA-B27 positivity, and CRP values significantly affected axSpA diagnostic prediction.
The prediction model based on clinical risk factors and SIJ-MRI imaging features can distinguish axSpA and non-axSpA effectively. In addition, the TabNet demonstrates superior diagnostic efficacy compared with machine learning models.
本研究旨在基于骶髂关节（SIJ）-MRI成像结果和临床风险因素，开发并验证一种预测轴性脊柱关节炎（axSpA）的模型。
本研究基于942例患者的数据开展，其中包括707例axSpA患者和235例非axSpA患者。首先，将患者分为训练队列（n = 753）和验证队列（n = 189）。其次，多名评估人员手动提取活动性炎症（骨髓水肿）和结构性病变（侵蚀、硬化、强直、关节间隙改变和脂肪病变）的特征。同时，我们利用11种机器学习模型和TabNet开发成像模型，其中临床模型和联合临床-成像模型包含6个临床风险因素。最后，在验证队列中评估上述模型的诊断性能，包括准确性、受试者操作特征曲线下面积（AUC）、敏感性、特异性、F1分数和马修斯相关系数（MCC）。
从成像结果中提取了6个特征。联合临床-成像模型优于临床模型和成像模型。相比之下，通过TabNet的联合临床-成像模型（CCMRT）的AUC最优，为0.93（95%CI：0.89，0.97）。此外，观察到双侧关节间隙改变和右侧侵蚀、HLA-B27阳性以及CRP值显著影响axSpA的诊断预测。
基于临床风险因素和SIJ-MRI成像特征的预测模型能够有效区分axSpA和非axSpA。此外，与机器学习模型相比，TabNet具有更高的诊断效能。
