Chen Xinyi, Zhao Yanfeng, Han Yunchong, Wei Kai, Cheng Shufang, Ye Yongjun, Feng Jie, Huang Xinchen, Xu Jingjing
Zhejiang University School of Medicine, The Second Affiliated Hospital, Department of Radiology, Hangzhou, China
Chinese Academy of Medical Sciences and Peking Union Medical College, National Cancer Center, Cancer Hospital, National Clinical Research Center for Cancer, Department of Radiology, Beijing, China
Diagn Interv Radiol. 2025 Jul 8;30(4):347-358. doi: 10.4274/dir.2025.253293. Epub 2025 May 29.
To evaluate the diagnostic performance of delayed post-gadolinium enhancement magnetic resonance imaging (DEMRI) in diagnosing Menière’s disease (MD) and to establish an effective MRI-based diagnostic model.
This retrospective multicenter study assessed DEMRI descriptors in patients presenting with Ménièriform symptoms who were examined consecutively between May 2022 and May 2024. A total of 162 ears (95 with MD, 67 controls) were included. Each ear was randomly assigned to either a training set (n = 98) or a validation set (n = 64). In the training cohort, diagnostic models for MD were developed using logistic regression. The area under the curve (AUC) was used to evaluate the diagnostic performance of the different models. The Delong test was applied to compare AUC estimates between models.
The proposed DEMRI diagnostic model demonstrated strong diagnostic performance in both the training cohort (AUC: 0.907) and the validation cohort (AUC: 0.887), outperforming the clinical diagnostic model ( = 0.01231; 95% confidence interval: 0.033–0.269) in the validation cohort. The AUC of the DEMRI model was also higher than that of the combined DEMRI-clinical model (AUC: 0.796), although the difference was not statistically significant ( = 0.054). In the training set, the sensitivity and specificity of the DEMRI model were 78.9% and 88.5%, respectively.
A diagnostic model based on DEMRI features for MD is more effective than one based solely on clinical variables. DEMRI should, therefore, be recommended when MD is suspected, given its significant diagnostic potential.
This model may improve the accuracy and timeliness of MD diagnosis, as it is less influenced by the attending physician’s level of inquiry or the patient’s self-reporting ability. It may also contribute to more effective disease management in patients with MD.
评估钆剂增强后延迟磁共振成像（DEMRI）在梅尼埃病（MD）诊断中的诊断性能，并建立一种基于MRI的有效诊断模型。
这项回顾性多中心研究评估了2022年5月至2024年5月期间连续接受检查的有梅尼埃样症状患者的DEMRI描述符。共纳入162只耳（95只MD耳，67只对照耳）。每只耳被随机分配到训练集（n = 98）或验证集（n = 64）。在训练队列中，使用逻辑回归建立MD的诊断模型。曲线下面积（AUC）用于评估不同模型的诊断性能。采用德龙检验比较模型之间的AUC估计值。
所提出的DEMRI诊断模型在训练队列（AUC：0.907）和验证队列（AUC：0.887）中均表现出较强的诊断性能，在验证队列中优于临床诊断模型（P = 0.01231；95%置信区间：0.033 - 0.269）。DEMRI模型的AUC也高于DEMRI - 临床联合模型（AUC：0.796），尽管差异无统计学意义（P = 0.054）。在训练集中，DEMRI模型的敏感性和特异性分别为78.9%和88.5%。
基于DEMRI特征的MD诊断模型比仅基于临床变量的模型更有效。因此，鉴于其显著的诊断潜力，当怀疑MD时应推荐使用DEMRI。
该模型可能提高MD诊断的准确性和及时性，因为它受主治医生询问水平或患者自我报告能力的影响较小。它也可能有助于MD患者更有效的疾病管理。
