International journal of rheumatic diseases · 2024
The TabNet Model for Diagnosing Axial Spondyloarthritis Using MRI Imaging Findings and Clinical Risk Factors.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Zhang Zhaojuan, Pan Yiling, Lu Yanjie, Ye Lusi, Zheng Mo, Zhang Guodao, Chen Dan
Аннотация
OBJECTIVES: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSION: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSION: 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.