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标题：Construction of an MRI-based decision tree to differentiate autoimmune and autoinflammatory inner ear disease from chronic otitis media with sensorineural hearing loss
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作者：Boeun Lee ; Yun Jung Bae ; Byung Yoon Choi 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2021
卷号：11
DOI：10.1038/s41598-021-98557-w
语种：English
出版社：Springer Nature
摘要：Autoimmune and autoinflammatory inner ear diseases (AIED/AID) are characterized by the symptom of sensorineural hearing loss (SNHL). To date, standardized diagnostic tools for AIED/AID are lacking, and clinically differentiating AIED/AID from chronic otitis media (COM) with SNHL is challenging. This retrospective study aimed to construct a magnetic resonance imaging (MRI)-based decision tree using classification and regression tree (CART) analysis to distinguish AIED/AID from COM. In total, 67 patients were enrolled between January 2004 and October 2019, comprising AIED/AID (n = 18), COM (n = 24), and control groups (n = 25). All patients underwent 3 T temporal bone MRI, including post-contrast T1-weighted images (postT1WI) and post-contrast FLAIR images (postFLAIR). Two radiologists evaluated the presence of otomastoid effusion and inner ear contrast-enhancement on MRI. A CART decision tree model was constructed using MRI features to differentiate AIED/AID from COM and control groups, and diagnostic performance was analyzed. High-intensity bilateral effusion (61.1%) and inner ear enhancement (postFLAIR, 93.8%; postT1WI, 61.1%) were the most common findings in the AIED/AID group. We constructed two CART decision tree models; the first used effusion amount as the first partitioning node and postT1WI-inner ear enhancement as the second node, whereas the second comprised two partitioning nodes with the degree of postFLAIR-enhancement of the inner ear. The first and second models enabled distinction of AIED/AID from COM with high specificity (100% and 94.3%, respectively). The amount of effusion and the degree of inner ear enhancement on MRI may facilitate the distinction between AIED/AID and COM with SNHL using decision tree models, thereby contributing to early diagnosis and intervention.