A Diagnostic model based on MRI for Meniere's disease: a multicenter study

A Diagnostic model based on MRI for Meniere's disease: a multicenter study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Diagnostic model based on MRI for Meniere's disease: a multicenter study Xinyi Chen, Yanfeng Zhao, Yunchong Han, Kai Wei, Shufang Cheng, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5561016/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose To explore the diagnostic performance of delayed post gadolinium enhancement MRI (DEMRI) in the diagnosis of Meniere's disease (MD), and to establish an effective MRI diagnostic model. Materials and methods This retrospective multicenter study evaluated DEMRI descriptors of patients with Ménièriform symptoms examined consecutively from May 2022 to May 2024. A total of 162 ears (95 MD ears, 67 control ears) were enrolled in this study. Each ear was randomly allocated to a training set (n = 98) and a validation set (n = 64). Logistic regression determined three models for the diagnosis of MD in the training cohort. AUC was applied to evaluate the diagnostic performance of different models. Delong test was used to compare the AUC estimates between the different diagnostic models. Results The proposed DEMRI diagnostic model demonstrated good diagnostic performance in both the training (AUC, 0.907) and the validation cohort (AUC, 0.887), outperforming the clinical diagnostic model (Z = 2.503, p = 0.01231; 95%CI:0.033–0.269) in the validation cohort. The AUC value of DEMRI model was higher than combined DEMRI-clinical model in the validation cohort (AUC, 0.796) as well, but there was no statistically significant difference (Z = -1.9291, p = 0.05372). In the training set, the sensitivity, specificity, and accuracy of the DEMRI model were 78.9%, 88.5% and 82.8%, respectively. Conclusion A diagnosis model based on DEMRI features for MD diagnosis efficiency was higher than that of clinical variables alone. Therefore, DEMRI should be recommended when MD is suspected because of its significant potential in the diagnosis of MD. post gadolinium enhancement MRI Menière’s disease endolymphatic hydrops diagnosis model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Key points DEMRI of the inner ear enables us to visualize endolymphatic hydrops and the perilymphatic spaces in patients with MD, which is very important in the diagnosis of MD. In the DEMRI diagnostic model, the most significant features were "Cochlea_EH_Grad", "Cochlea_Apex_EH_Score", "VA" and "Vestibule_EH". The diagnostic performance of delayed post gadolinium enhancement MRI for Menière’s disease is greater than that of basic clinical information alone. Introduction Menière’s disease (MD) is a multifactorial disease where the combined effect of genetics and environmental factors may determine the onset of the disease( 1 ). The main clinical symptoms were idiopathic fluctuating sensorineural hearing loss (SNHL) spontaneous vertigo, aural fullness, and tinnitus. Prosper Ménière described this disease for the first time in 1861, proposed that the pathologic site was in the labyrinth rather than brain( 2 , 3 ). However, diagnosis has been difficult to carry out, especially when the initial symptoms are not obvious, so few articles have been published on the epidemiology of MD. The American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) developed the guidelines for diagnosis and therapy evaluation of MD in 1972 and revised them in 1985 and 1995( 4 ). In 2015, the Barany Society updated and established consensus diagnostic criteria for MD, in part to distinguish migraine-related vertigo from MD( 5 , 6 ). However, the updated criteria still relied on patients’ self-reports rather than medical tests, coupled with insufficient understanding of MD in some clinical departments, which leaded to patients cannot receive timely diagnosis and treatment. In 1937, British and Japanese researchers discovered endolymphatic hydrops (EH) in human temporal bone and revealed the pathological description of Meniere's syndrome( 7 , 8 ). In 2007, Nakashima et al( 9 ) used delayed inner ear scanning with three-dimensional fluid‐attenuated inversion recovery (3D-FLAIR) sequence successfully demonstrated EH in a patient with MD after intratympanic gadolinium injection. Subsequently, a series of MRI studies on EH have emerged( 8 , 10 – 14 ). 3D-FLAIR and 3D inversion recovery with real reconstruction(3D-real IR) are the most commonly used imaging sequence of EH( 11 ). With these new imaging techniques, EH can be demonstrated in vivo and can be used to confirm the diagnosis. In addition to EH, there are many other signs( 15 – 20 ) that can be observed on MRI. As a noninvasive test, the diagnostic performance of DEMRI remains to be seen. The purpose of this study is to establish an intuitive and objective diagnostic model of MD, to provide an effective diagnostic path for MD patients, improve the efficiency and accuracy of diagnosis, and provide a reference for the development of clinical treatment decisions. Materials and methods Patients This was a multicenter retrospective study, approved by the institutional ethical committee. This retrospective study involved the data of consecutive patients who first visited the otology department of three medical centers with Menièriform symptoms such as vertigo, hearing loss, tinnitus, and aural fullness, and underwent delayed post-gadolinium enhancement MRI (DEMRI) of the inner ear labyrinth from May 2022 to May 2024. A total of 136 patients (272 ears) were retrospectively analyzed. Ultimately 85 patients (162 ears, 95 MD ears, 67 control ears) (mean age, 55.2 ± 13.6 years) were enrolled in this study according to the exclusion criteria (Fig. 1 ). Each ear was treated as a whole and randomly allocated to a training set (98 ears) and a validation set (64 ears) in a ratio of 6 to 4 (Fig. 1 ). Baseline clinical data, including gender, age, affected side, inner ear symptoms such as vertigo, hearing loss, tinnitus, aural fullness, and pure tone audiometry (PTA) were recorded from medical record management system (CMIS). According to the average hearing threshold of PTA (0.5kHZ, 1kHZ, 2kHZ), hearing loss was divided into 4 stages: Stage I: the average hearing threshold ≤ 25 dBHL; Stage II: average hearing threshold > 25–40 dBHL; Stage III: average hearing threshold > 40–70 dBHL; Stage IV: Mean hearing threshold > 70 dBHL. MRI examinations Patients underwent DEMRI on 3T scanners (Center A: uMR 790, UIH, China; Center B: Ingenia CX, Philips;Center C: Ingenia CX, Philips), using a commercially available 32-channel head and neck coil. Prior to gadolinium injection, 3D-T2-Sampling Perfection with Application optimized Contrasts using different flip angle Evolutions (3D-T2-SPACE) scan (TR, 1300 ms; TE, 196.68 ms; slice thickness, 0.5 mm; matrix size, 380×100; FOV, 220 × 180; acceleration factor, 2(2D); scan time, 1 min and 47 s) was performed to exclude patients with organic brain syndrome, inner ear deformities, and acoustic neuroma. 3D-FLAIR sequence(FOV, 220 × 190 mm; section thickness, 0.7 mm; TR, 6500 ms; TE, 426 ms; number of excitations, 1; TI, 1935 ms; flip angle, 54°; matrix, 256 × 100; bandwidth, 500 Hz/pixel; turbo factor, 5(acs); voxel size, 0.86 × 0.86 × 1; and scan time, 2min56s) was performed 4h after a double dose of intravenous gadobutrol (7.5 ml/vial, 1.0 mmol/mL; Bayer AG) administration to assure the maximum perilymphatic enhancement (PLE). Some previous reports ( 21 , 22 ) have proved that gadobutrol has more advantages than other macrocyclic gadolinium contrast agents enhanced MRI in the diagnosis of MD because of its high concentration and high relaxation rate characteristics. Extraction qualitative and quantitative MRI features The MR images were qualitatively analyzed by three experienced radiologists (with an experience of 15, 15 and 20years, respectively) blinded to the clinical findings and symptoms. The degree of EH was indicated by a widening of the negative signal gap within the labyrinth. In this study, cochlear and vestibular EH were dichotomized as EH-positive or EH-negative. Cochlear and vestibular EH grades were evaluated according to Gurkov and Bernaerts' visual 4-grade method( 23 , 24 ). Cochlea: Normal(grade 0): the Scala middle(SM) was a vaguely visible dark area, with a relatively straight border with Scala vestibule(SV) and Scala tympani(ST) (Fig. 2 a). Mild hydrops (grade 1): the SM showed obvious black low signal area, surrounded by clear and continuous white high signal perilymphatic ring (Fig. 2 b). Moderate hydrops (grade 2): white hyperintense perilymphatic ring was significantly interrupted (Fig. 2 c). Severe hydrops (grade 3) (Fig. 2 d): The surrounding white hyperintense perilymphatic area becomes a clear straight line. Vestibular: Normal (grade 0): the saccule and utricle are separated, and the sum of their areas is less than 1/2 of vestibular (Fig. 3 a). Mild hydrops (grade 1): saccule ≥ utricle, and they can be separated (Fig. 3 b). Moderate hydrops (grade 2): the saccule and utricle fused, and the peripheral perilymph was visible (Fig. 3 c). Severe hydrops (grade 3): no perilymphatic enhancement of the vestibule (Fig. 3 d). In addition to this, a new weighted visual scoring system (Table 1 ) based on Inner Ear Structural Assignment Method (IESAM)( 25 , 26 ) was cited in this study. The signal intensity ratio (SIR) of PLE to ipsilateral middle cerebellar peduncle (MCP) was recorded. The semicircular canals and vestibular aqueduct (VA) were graded as 0, 1 and 2 according to whether they were continuously developed. In total, 6 clinical variables and 17 MRI features were included in the study (Table 1 ; Supplementary Text). Table 1 A new weighted visual scoring criteria based on Inner Ear Structural Assignment Method (IESAM) for inner ear 3D-FLAIR images. Appearance Cochlea Vestibule Semicircular canals Base Middle Apex Superior Horizontal Posterior Not visible # 0 0 0 0 0 0 0 Partially visible * 2 1 - a 3 b 1 1 1 Completely visible ! 3 2 1 6 c 2 2 2 Data represent scores awarded on 3D-FLAIR images. # Absence of high-signal contrast medium. * Failure to show high-signal image of entire cochlear canal, or high-signal image of cochlear canal limited to tympanic or vestibular scale, or interrupted high-signal images of semicircular canals, or incomplete high-signal image of vestibule. ! All labyrinth structures completely visible. a No this option (Apex of Cochlea is very small, only set a score of 0 or 1, if it is visible 1 is scored, do not distinguish between partially or completely visible). b The hypointintensity zone in the vestibule exceeds the lower margin of the horizontal semicircular canal, point a score of 3. c The hypointintensity zone in the vestibule was located only above the plane of the horizontal semicircular canal, point a score of 6. Statistics The IBM SPSS (version 27.0) and R software (version 4.2.1) were applied to analyze all data. Continuous variables are shown as mean with standard deviation (SD) or median with interquartile range (IQR). The measurement data conforming to normal distribution were compared by independent sample t- test. The Mann—Whitney U test was used to compare the measurement data that did not conform to the normal distribution. Categorical data were compared by χ 2 test or Fisher's exact test. The Kendall's W test was used to analyze the inter-observer agreement. Multivariable logistic regression analysis was applied to select MD-related features. Variables of clinical and DEMRI with p < 0.05 were included into the multivariate logistic regression model using backward stepwise method to develop three models for MD diagnosis in the training cohort: including DEMRI signature, clinical variables, and DEMRI-clinical parameters, respectively. The validation set was used to validate the models. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) was applied to evaluate the diagnostic performance of the different models. Delong test was used to compare the AUC estimates between the different diagnostic models. A two-tailed p-value less than 0.05 was considered statistically significant. Results Patient characteristics A total of 85 patients, 162 ears (mean age, 53.2±13.6 years; age range, 17–86 years) were identified. The detailed clinical characteristics and EDMRI characteristics results of all ears in the MD group (n = 95 ears) and control group (n = 67ears) are listed in Table 1 Supplementary Text. Except for gender, BMI and VA visualization degree, the other observation indicators between the MD group and the control group were statistically different ( p < 0.05). The detailed clinical characteristics and EDMRI characteristics results of all ears in the training set (n = 98) and validation set (n = 64) are listed in Table 2 Supplementary Text. Table 2 Risk factors of delayed post gadolinium enhancement MRI for Menière’s disease in the training cohort Variable B Wald SE P OR 95% CI Lower Upper (Intercept) 30.073 0 2955.414 0.992 1.15E + 13 0 NA Cochlea_EH_Grad 3.19 10.347 0.992 0.001* 24.292 5.058 297.868 Cochlea_Apex_EH_Score 3.698 4.298 1.784 0.038* 40.384 1.906 3014.737 Vestibule_EH_Score 0.631 1.915 0.456 0.166 1.879 0.852 5.211 Semicircular Canal Horizontal -21.236 0 1477.708 0.989 0 NA 1.91E + 27 VA 1.116 4.579 0.522 0.032* 3.053 1.196 9.632 Vestibule_EH 3.729 6.663 1.445 0.010* 41.631 3.44 1172.845 PE/MCPE 1.612 2.332 1.056 0.127 5.014 0.607 46.332 Note. —Data are results of the multivariable regression analysis. SE standard error, OR odds ratio, CI confidence interval. * Indicates significant difference. VA vestibular aqueduct, PE/MCPE perilymphatic enhancement/ middle cerebellar peduncle enhancement. Diagnostic model development and validation In the training set, the 17 DEMRI independent descriptors were analyzed by multivariate logistic regression using backward induction. Four descriptors with p < 0.05 in Table 2 were selected to establish the DEMRI diagnostic model, which indicated a favorable diagnostic performance of MD with an AUC of 0.907 (95% confidence interval [CI]: 0.848–0.966) in the training cohort (Fig. 4 a) and 0.887 (95% CI:0.802–0.971) in the validation cohort (Fig. 4 b). The same analysis method was used to establish a clinical diagnostic model based on two independent descriptors (PTA stage, p < 0.001; tinnitus fullness, p < 0.001). The AUCs of the clinal model in the training and validation cohorts were 0.915 (95% CI: 0.860–0.970) (Fig. 4 c) and 0.736 (95% CI: 0.617–0.855) (Fig. 4 d), respectively. With the use of multivariable logistic regression analysis, independent descriptors include Cochlea_EH_Grad, Vestibule_EH, PTA Stage and Tinnitus fullness were identified for the combined DEMRI-clinical model (Table 3 ). The AUCs of the DEMRI-clinical model for diagnose MD in the training and validation cohorts were 0.947 (95% CI: 0.903–0.990) (Fig. 4 e) and 0.796(95% CI: 0689–0.902) (Fig. 4 f), respectively. DeLong's test was used to compare the two correlated ROC curves. The AUC value of the DEMRI model was nearly equal to that of the clinical model in training set, but the difference was statistically significant (Z = 2.503, p = 0.01231; 95%CI:0.033–0.269) in the validation cohort, the AUC value of the DEMRI model was significant higher, while the DEMRI-clinical model was significantly better than the clinical model in the diagnostic of MD as well (Z = 2.2149, p = 0.02677). Although the AUC value of DEMRI model was higher than DEMRI-clinical model, but there was no statistically significant difference (Z = -1.9291, p = 0.05372) (Table 4 ). Table 3 Risk factors of DEMRI-clinical for Menière’s disease in the training cohort Variable B Wald SE P OR 95% CI Lower Upper (Intercept) -2.711 5.258 1.182 0.022 0.066 0.005 0.531 Cochlea_EH_Grad 1.252 6.755 0.482 0.009* 3.498 1.499 10.256 Vestibule_EH 1.377 2.996 0.796 0.083* 3.964 0.845 20.353 Stage 1.581 5.911 0.65 0.015* 4.861 1.603 20.68 Tinnitus fullness -1.821 5.988 0.744 0.014* 0.162 0.034 0.673 Note. —Data are results of the multivariable regression analysis. SE standard error, OR odds ratio, CI confidence interval. * Indicates significant difference. Table 4 Diagnostic performances of three models in the training and validation cohort Model AUC 95% CI Sensitivity Specificity PPV NPV Accuracy Lower Upper DEMRI Training cohort Validation cohort 0.907 0.848 0.966 0.825 0.927 0.940 0.792 0.867 0.887 0.802 0.971 0.789 0.885 0.909 0.742 0.828 Clinical Training cohort 0.915 0.860 0.970 0.772 0.951 0.957 0.75 0.847 Validation cohort 0.736 0.617 0.855 0.553 0.923 0.913 0.585 0.703 DEMRI-clinical Training cohort Validation cohort 0.947 0.903 0.990 0.877 0.658 0.927 0.943 0.844 0.898 0.796 0.689 0.902 0.885 0.893 0.639 0.750 AUC: area under the receiver operating characteristic curve, CI: confidence interval, PPV: positive predictive value; NPV: negative predictive value. Features used for the DEMRI model are Cochlea_EH_Grad, Cochlea_Apex_EH_Score, VA and Vestibule_EH. Features used for the Clinical model are Stage and Tinnitus fullness. Features used for the DEMRI-clinical model are Cochlea_EH_Grad, Vestibule_EH, Stage and Tinnitus fullness. The weights of the four independent risk factors for constructing the DEMRI model are presented in a nomogram (Fig. 5 a). The calibration curve of the DEMRI nomogram demonstrated good agreement both in the training and validation set (Fig. 5 b, c). Inter-observer agreement on the four MRI features of the DEMRI model Inter- observer reliability of the four MRI features of the DEMRI model was evaluated with Kendall's W test. The four observation indicators of “Cochlea_EH_Grade” “Cochlea_Apex_EH_Score” “Vestibule_EH” and “VA” showed very good consistency with the Kendall's coefficient of cooperation were W = 0.954, 0.985, 0.967 and 0.951, respectively, and the P values were all less than 0.001 (Table 5 ). Table 5 Inter-observer reliability Kendall’ W values for the four DEMRI model features Grade/Score Cochlea_EH_Grade Cochlea_Apex_Score Vestibule_EH VA 0 1 2 3 0 1 YES NO 0 1 2 observer1 84 29 26 23 38 124 75 87 66 48 48 observer2 79 38 32 13 40 122 71 91 73 45 44 observer3 81 32 25 24 38 124 75 87 66 57 39 Kendall’s W 0.954 0.985 0.967 0.951 P <0.001 <0.001 <0.001 <0.001 Discussion In this study, we developed and validated three models to diagnose MD. Results showed that the DEMRI model and combined DEMRI- clinical model had better clinical diagnostic performance than clinical model (AUC, 0.736; sensitivity, 55.3%; specificity, 92.3%). The DEMRI model demonstrated excellent predictive performance in the validation set (AUC, 0.887; sensitivity, 78.9%; specificity, 88.5%). The AUC value of the combined DEMRI-clinical model was slightly lower than DEMRI model, but there was no significant difference in the diagnostic performance. In the DEMRI diagnostic model, the characteristics with the maximum significance were “Cochlea_EH_Grad”, “Cochlea_Apex_EH_Score”, “VA” and “Vestibule_EH” by multivariate regression model using backward stepwise method. MD is associated with a variety of comorbidities, such as migraine, anxiety, allergy, and immune disorders, but its pathogenesis remains unknown( 27 ). Researchers have established the histopathological hallmark of MD in EH, which is an increase of the endolymphatic fluid within the membranous labyrinth of the inner ear. EH is considered the result of a change in endolymph homeostasis, generated by increased endolymph production, endolymph malabsorption, or a combination of both( 28 ). In the case of EH, excess volume leads to longitudinal endolymphatic flow from the cochlea to the endolymphatic sac to achieve endolymphatic homeostasis. Gibson( 29 ) proposed that when the endolymphatic sac (ES) and duct (ED) is functional, it can remove excess endolymph, but in Menière’s patient who may have a dysfunctional ES and ED, endolymph may accumulate in the sinus of the ED, leading to significant overflow. Various methods have been proposed to assess the endolymphatic space qualitatively and quantitatively( 23 , 24 , 26 ). Studies have shown that he relationship between MD and EH was strong enough to consider it as a hallmark feature of Meniere's disease and a sensitive target for diagnostic detection( 12 ). In this study, statistical differences were found in all MRI features related to EH between the MD and control groups (Table 1 ; Supplementary Text). Among them, three MRI features associated with EH were included in the DEMRI model, including “Cochlea_EH_Grad”, “Cochlea_Apex_EH_Score”, and “Vestibule_EH”. It seems, that cochlea related EH has a greater weight in the diagnosis of MD, and whether there had hydrops at the cochlear apical turn was of great significance. It has been shown that cochlear hydrops has a reliable pattern of hydropic progression over time, which always originates in the apex and proceeds to the base, tonotopically like the progression of hearing loss( 12 , 30 , 31 ). Hydrodynamic pressure shunt in the pars superior stimulates the utricle and the saccule of vestibule, causing “Vestibule_EH”( 28 , 29 ). This longitudinal hydrops process may explain why the presence or absence of EH in the apical turn of the cochlea is of diagnostic significance for MD. In addition, experimental studies suggested that the cytochemical and ultrastructural disruption of the fibrocytes of the hair cells, afferent neurons and fibrocytes of the lateral cochlear wall involved in the pathogenesis of EH and occurred prior to EH( 10 , 32 , 33 ). These theories support the conclusion that “Cochlea_EH_Grad” and “Vestibule_EH” are risk factors for the diagnosis of MD. It is more important to consider the grade of EH in the cochlea and the presence or absence of EH in the vestibule. The more severe the degree of cochlear EH, the more likely the diagnosis of MD is when combined with vestibular EH, regardless of the severity of vestibular EH. In addition, this study also found a relatively new point that vestibular aqueduct (VA) is a significant risk factor in the diagnosis of MD. A study by Steve Connor et al. ( 15 ) showed that all VA descriptors demonstrated excellent reliability to diagnose MD, incomplete visualization of the VA adds value to the diagnosis. The study by Jeanne et al. even suggested that the evaluation by temporal bone CT of the VA could predict the presence of EH on MRI with a high positive predictive value( 16 ). Arnaud et al.( 34 ) proposed discontinuous VA showed a correlation with MD. The non-visible or partially visible VA may be due to bony abnormalities or central fibrosis of the VA, resulting in endolymphatic stenosis. Although the performance of VA was included in the model of this study, there was no statistically significant difference in the performance of VA between the MD and control groups in the supplementary file. This may be related to the low detection rate of VA in MRI, so we may need clearer imaging methods, and in the future, it may need to be combined with other imaging such as CT for comprehensive analysis. Our study had several limitations. First, although this study was a multicenter study, it was retrospective, and the number of cases collected was not enlarge enough relatively. Thus, researchers should be more logical and cautious in the analysis. Second, there may be deviations in the judgment of unilateral or bilateral MD. Some asymptomatic patients with early MD may be classified into control group. Therefore, it may be necessary to supplement normal people for later research. Third, the main MRI indicators used at present are still traditional imaging features, some new imaging characteristics that need to be more careful excavations. Conclusions In summary, we developed and validated a new DEMRI model to diagnose MD, which has higher diagnostic value for MD than clinical inquiry information alone. The combination of high degree of cochlear EH, invisible cochlea apical turn, vestibular hydrops and incomplete visualization suggests a high risk of MD. Therefore, we suggest DEMRI should be recommended when MD is suspected because of its significant potential in the diagnosis of MD. Moreover, further studies are required to explore the generalized utility of our model and apply our results to clinical application. Abbreviations DEMRI delayed post gadolinium enhancement MRI MD Menière’s disease AIC area under the curve ROC receiver operating characteristic curve (ROC) AAO-HNS The American Academy of Otolaryngology-Head and Neck Surgery EH endolymphatic hydrops SV Scala vestibule ST Scala tympani IESAM Inner Ear Structural Assignment Method PE perilymphatic enhancement MCP middle cerebellar peduncle ES endolymphatic sac ED endolymphatic duct VA vestibular aqueduct Declarations Data availability Data is provided within the manuscript or supplementary information files. Critical Relevance Statement To investigate the diagnostic value of delayed enhancement MRI (DEMRI) in Meniere's disease (MD), and to establish an effective MRI diagnostic model. Funding Funder name: National Natural Science Foundation of China.ID:82202089 Author information Authors and Affiliations Xinyi Chen 1# , Yanfeng Zhao 2# , Yunchong Han 3 , Kai Wei 1 , Shufang Cheng 4 , Yongjun Ye 4 , Jie Feng 4 , Xinchen Huang 1 , Jingjing Xu 1* 1 Department of Radiology, The 2nd Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. 2 Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. 3 Department of Radiology, The Second Affiliated Hospital of Jiaxing University, Jiaxing, China. 4 Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China. # Xinyi Chen and Yanfeng Zhao contributed equally. * Correspondence: Jingjing Xu E-mail: [email protected] Address: NO.88 Jiefang Road, Shangcheng District, Hangzhou, Zhejiang Province, China Contributions Xinyi Chen wrote the main manuscript text and prepared figures1-3. Yanfeng Zhao main performed statistical analysis and prepared figures 4-5. All authors (Xinyi Chen, Yanfeng Zhao, Yunchong Han, Kai Wei, Shufang Cheng, Yongjun Ye, Jie Feng, Xinchen Huang, Jingjing Xu) participated in data collection and experimental design, as well as the review and revision of the manuscript. Corresponding author Correspondence to Jingjing Xu E-mail: [email protected] Address: NO.88 Jiefang Road, Shangcheng District, Hangzhou, Zhejiang Province, China Ethics declarations Ethics approval and consent to participate The study followed the principles outlined in the Declaration of Helsinki, including all amendments and revisions. The research was approved by the Medical Ethics Committee of the Second Affiliated Hospital of Zhejiang University School of Medicine. (NO. [2022] 038). 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Comparison of inner ear MRI enhancement in patients with Meniere's disease after intravenous injection of gadobutrol, gadoterate meglumine, or gadodiamide. Eur J Radiol. 2021; 139:109682. Gurkov R, Flatz W, Louza J, Strupp M, Krause E. In vivo visualization of endolyphatic hydrops in patients with Meniere's disease: correlation with audiovestibular function. Eur Arch Otorhinolaryngol. 2011; 268(12):1743-8. Bernaerts A, Vanspauwen R, Blaivie C, et al. The value of four stage vestibular hydrops grading and asymmetric perilymphatic enhancement in the diagnosis of Meniere's disease on MRI. Neuroradiology. 2019; 61(4):421-9. Fang ZM, Chen X, Gu X, et al. A new magnetic resonance imaging scoring system for perilymphatic space appearance after intratympanic gadolinium injection, and its clinical application. J Laryngol Otol. 2012; 126(5):454-9. Xiao H, Guo X, Cai H, et al. Magnetic resonance imaging of endolymphatic hydrops in Meniere's disease: A comparison of the diagnostic value of multiple scoring methods. Front Neurol. 2022; 13:967323. Lopez-Escamez JA, Liu Y. Epidemiology and genetics of Meniere's disease. Curr Opin Neurol. 2024; 37(1):88-94. Mohseni-Dargah M, Falahati Z, Pastras C, et al. Meniere's disease: Pathogenesis, treatments, and emerging approaches for an idiopathic bioenvironmental disorder. Environ Res. 2023; 238(Pt 1):116972. Gibson WP. Hypothetical mechanism for vertigo in Meniere's disease. Otolaryngol Clin North Am. 2010; 43(5):1019-27. Wu Q, Dai C, Zhao M, Sha Y. The correlation between symptoms of definite Meniere's disease and endolymphatic hydrops visualized by magnetic resonance imaging. Laryngoscope. 2016; 126(4):974-9. Li J, Wang L, Hu N, et al. Longitudinal variation of endolymphatic hydrops in patients with Meniere's disease. Ann Transl Med. 2023; 11(2):44. Nadol JB, Adams JC, Kim JR. Degenerative changes in the organ of Corti and lateral cochlear wall in experimental endolymphatic hydrops and human Menière's disease. Acta Otolaryngol Suppl. 1995; 519:47-59. Ichimiya I, Adams JC, Kimura RS. Changes in immunostaining of cochleas with experimentally induced endolymphatic hydrops. Ann Otol Rhinol Laryngol. 1994; 103(6):457-68. Attye A, Barma M, Schmerber S, Dumas G, Eliezer M, Krainik A. The vestibular aqueduct sign: Magnetic resonance imaging can detect abnormalities in both ears of patients with unilateral Meniere's disease. J Neuroradiol. 2020; 47(2):174-9. Additional Declarations No competing interests reported. Supplementary Files Table1.SupplementaryText.docx Table2.SupplementaryText.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5561016","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":391431464,"identity":"6dad63ff-8c62-4d67-8ebd-22f7c91cb496","order_by":0,"name":"Xinyi Chen","email":"","orcid":"","institution":"Second Affiliated Hospital of Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Xinyi","middleName":"","lastName":"Chen","suffix":""},{"id":391431465,"identity":"82f04f6d-69c3-463e-88ee-f2a622c9ec82","order_by":1,"name":"Yanfeng Zhao","email":"","orcid":"","institution":"National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yanfeng","middleName":"","lastName":"Zhao","suffix":""},{"id":391431466,"identity":"2d8e0344-1ac7-486a-92ad-4aa278fc0357","order_by":2,"name":"Yunchong Han","email":"","orcid":"","institution":"The Second Affiliated Hospital of Jiaxing University","correspondingAuthor":false,"prefix":"","firstName":"Yunchong","middleName":"","lastName":"Han","suffix":""},{"id":391431467,"identity":"54dc3d8d-0b1b-4d79-9ae4-56bd47bd9385","order_by":3,"name":"Kai Wei","email":"","orcid":"","institution":"Second Affiliated Hospital of Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Wei","suffix":""},{"id":391431468,"identity":"8705472f-e25e-4df6-8c3f-5a8f960c5d62","order_by":4,"name":"Shufang Cheng","email":"","orcid":"","institution":"The Fifth Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shufang","middleName":"","lastName":"Cheng","suffix":""},{"id":391431469,"identity":"82d65e1d-682b-4bc2-a8d9-1c7dc73046dd","order_by":5,"name":"Yongjun Ye","email":"","orcid":"","institution":"The Fifth Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yongjun","middleName":"","lastName":"Ye","suffix":""},{"id":391431470,"identity":"67d980e6-c3c1-4600-9f35-ff02730a919d","order_by":6,"name":"Jie Feng","email":"","orcid":"","institution":"The Fifth Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Feng","suffix":""},{"id":391431471,"identity":"68a42ae9-7e26-4e90-9a52-b7eb6808456e","order_by":7,"name":"Xinchen Huang","email":"","orcid":"","institution":"Second Affiliated Hospital of Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Xinchen","middleName":"","lastName":"Huang","suffix":""},{"id":391431472,"identity":"606d4942-6ab1-4b7e-812d-cc49b2b137c0","order_by":8,"name":"Jingjing Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIie2RsQrCQAxAUw461bqmFPQXTgqiIPVXTgpOFZxcLQi6CK72L/oJlQ4u4tzBQRHEsXKrg1EXp/NGwXtDcoE8Ei4ABsMPYs0oIH8+WQ74zLm+Ygs95QOHv/M3hS1qxbUzPoRZGcuqO4eGWwpLjpWLucMO8kuUrkcZ9+YQeKVg/lqpOG2OvIjqOMoEKYOsFDZzdBQb42NOylRHCY6khHWMrYQUwXWm0CcXwlteAsA9ttLdaearlNZqF0i8F32+jc4SJ72mu402UqkkYPt0wUFCBcPXMa1EIQA0qfFWAfRfW1bKXoPBYPhXHk1pR6ujYQSJAAAAAElFTkSuQmCC","orcid":"","institution":"Second Affiliated Hospital of Zhejiang University","correspondingAuthor":true,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2024-12-02 03:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5561016/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5561016/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72287753,"identity":"e2a22dbd-f37a-422a-8ddb-87d935df69ee","added_by":"auto","created_at":"2024-12-24 17:10:43","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1482632,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of patient recruitment pathway\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5561016/v1/68053baaade985c9e84f3525.jpg"},{"id":72287750,"identity":"ae48abcb-c5e8-4c0d-acc1-2696ac4e84fe","added_by":"auto","created_at":"2024-12-24 17:10:43","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":416312,"visible":true,"origin":"","legend":"\u003cp\u003eAxial 3D-FLAIR delayed-enhancement image. Grading of cochlear hydrops. Normal（grade 0）: the Scala middle was a vaguely visible dark area, with a relatively straight border with Scala vestibule and Scala tympani(a); Mild hydrops (grade 1): the SM showed obvious black nodular low signal area (white arrow), surrounded by clear and continuous white high signal perilymphatic ring (b); Moderate hydrops (grade 2): white hyperintense perilymphatic ring was significantly interrupted (c); Severe hydrops (grade 3): The surrounding white hyperintense perilymphatic area becomes a clear straight line (d).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5561016/v1/4af857295c2e8a69ed761879.jpg"},{"id":72287777,"identity":"fe1310c6-133c-448e-8aa6-d9822cafe999","added_by":"auto","created_at":"2024-12-24 17:10:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":393292,"visible":true,"origin":"","legend":"\u003cp\u003eAxial 3D-FLAIR delayed-enhancement image. Grading of vestibular hydrops: Normal (grade 0): the saccule (short arrow) and utricle (long arrow) are separated, and the sum of their areas is less than 1/2 of vestibular (a); Mild hydrops (grade 1): saccule ≥ utricle, and they can be separated (b); Moderate hydrops (grade 2): the saccule and utricle fused, and the peripheral perilymph (swallow tail arrow in c and d) was visible (c). Severe hydrops (grade 3): no perilymphatic enhancement of the vestibule (d).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5561016/v1/49ad57c6b83ccc906271ab65.jpg"},{"id":72287766,"identity":"d22825e6-a37b-4b53-b7fa-862b6ae49947","added_by":"auto","created_at":"2024-12-24 17:10:44","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":421024,"visible":true,"origin":"","legend":"\u003cp\u003ePerformances of three models in training cohort and validation cohort. a, b DEMRI model, including four features- Cochlea_EH_Grad, Cochlea_Apex_EH_Score, VA and Vestibule_EH. c, d Clinical model, including two clinical variables- Stage and Tinnitus fullness. e, f DEMRI-clinical model, integrated two DEMRI features and two clinical variables. ROC receiver operating characteristic curve.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5561016/v1/53ebe461f903a05be919d341.jpg"},{"id":72287774,"identity":"6223cbd8-2c94-42ba-9d4b-611adb4c10f5","added_by":"auto","created_at":"2024-12-24 17:10:47","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1148215,"visible":true,"origin":"","legend":"\u003cp\u003eA DEMRI nomogram was constructed in the training cohort, with Cochlea_EH_Grad, Cochlea_Apex_EH_Score, VAmand Vestibule_EH(a). Calibration curves of the radiomics nomogram in the(b) training and (c) validation cohorts.\u003c/p\u003e","description":"","filename":"Figure5a.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5561016/v1/61de6213a86d24aef0942ffc.jpg"},{"id":73343871,"identity":"c285f1b2-c2f0-487a-a823-d99337100232","added_by":"auto","created_at":"2025-01-09 06:03:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4813379,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5561016/v1/7f075f91-1195-4908-a5b4-6d3672d4396a.pdf"},{"id":72287746,"identity":"db0f6e78-2e8d-4591-989c-7ad4df6dc8e9","added_by":"auto","created_at":"2024-12-24 17:10:43","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24463,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.SupplementaryText.docx","url":"https://assets-eu.researchsquare.com/files/rs-5561016/v1/28baf0e3ceed09de88fa0189.docx"},{"id":72287756,"identity":"4814f57d-ff9e-4ce9-8e2f-cf685c5459ec","added_by":"auto","created_at":"2024-12-24 17:10:44","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":24163,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.SupplementaryText.docx","url":"https://assets-eu.researchsquare.com/files/rs-5561016/v1/cda1ee3132719c851ad6451b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Diagnostic model based on MRI for Meniere's disease: a multicenter study","fulltext":[{"header":"Key points","content":"\u003col\u003e\n \u003cli\u003eDEMRI of the inner ear enables us to visualize endolymphatic hydrops and the perilymphatic spaces in patients with MD, which is very important in the diagnosis of MD.\u003c/li\u003e\n \u003cli\u003eIn the DEMRI diagnostic model, the most significant features were \u0026quot;Cochlea_EH_Grad\u0026quot;, \u0026quot;Cochlea_Apex_EH_Score\u0026quot;, \u0026quot;VA\u0026quot; and \u0026quot;Vestibule_EH\u0026quot;.\u003c/li\u003e\n \u003cli\u003eThe diagnostic performance of delayed post gadolinium enhancement MRI for Meni\u0026egrave;re\u0026rsquo;s disease is greater than that of basic clinical information alone.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Introduction","content":"\u003cp\u003eMeni\u0026egrave;re\u0026rsquo;s disease (MD) is a multifactorial disease where the combined effect of genetics and environmental factors may determine the onset of the disease(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The main clinical symptoms were idiopathic fluctuating sensorineural hearing loss (SNHL) spontaneous vertigo, aural fullness, and tinnitus. Prosper M\u0026eacute;ni\u0026egrave;re described this disease for the first time in 1861, proposed that the pathologic site was in the labyrinth rather than brain(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e3\u003c/span\u003e). However, diagnosis has been difficult to carry out, especially when the initial symptoms are not obvious, so few articles have been published on the epidemiology of MD. The American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) developed the guidelines for diagnosis and therapy evaluation of MD in 1972 and revised them in 1985 and 1995(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In 2015, the Barany Society updated and established consensus diagnostic criteria for MD, in part to distinguish migraine-related vertigo from MD(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). However, the updated criteria still relied on patients\u0026rsquo; self-reports rather than medical tests, coupled with insufficient understanding of MD in some clinical departments, which leaded to patients cannot receive timely diagnosis and treatment.\u003c/p\u003e \u003cp\u003eIn 1937, British and Japanese researchers discovered endolymphatic hydrops (EH) in human temporal bone and revealed the pathological description of Meniere's syndrome(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In 2007, Nakashima et al(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) used delayed inner ear scanning with three-dimensional fluid‐attenuated inversion recovery (3D-FLAIR) sequence successfully demonstrated EH in a patient with MD after intratympanic gadolinium injection. Subsequently, a series of MRI studies on EH have emerged(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). 3D-FLAIR and 3D inversion recovery with real reconstruction(3D-real IR) are the most commonly used imaging sequence of EH(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). With these new imaging techniques, EH can be demonstrated in vivo and can be used to confirm the diagnosis. In addition to EH, there are many other signs(\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) that can be observed on MRI. As a noninvasive test, the diagnostic performance of DEMRI remains to be seen.\u003c/p\u003e \u003cp\u003eThe purpose of this study is to establish an intuitive and objective diagnostic model of MD, to provide an effective diagnostic path for MD patients, improve the efficiency and accuracy of diagnosis, and provide a reference for the development of clinical treatment decisions.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003e This was a multicenter retrospective study, approved by the institutional ethical committee. This retrospective study involved the data of consecutive patients who first visited the otology department of three medical centers with Meni\u0026egrave;riform symptoms such as vertigo, hearing loss, tinnitus, and aural fullness, and underwent delayed post-gadolinium enhancement MRI (DEMRI) of the inner ear labyrinth from May 2022 to May 2024. A total of 136 patients (272 ears) were retrospectively analyzed. Ultimately 85 patients (162 ears, 95 MD ears, 67 control ears) (mean age, 55.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6 years) were enrolled in this study according to the exclusion criteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Each ear was treated as a whole and randomly allocated to a training set (98 ears) and a validation set (64 ears) in a ratio of 6 to 4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBaseline clinical data, including gender, age, affected side, inner ear symptoms such as\u003c/p\u003e \u003cp\u003evertigo, hearing loss, tinnitus, aural fullness, and pure tone audiometry (PTA) were recorded from medical record management system (CMIS). According to the average hearing threshold of PTA (0.5kHZ, 1kHZ, 2kHZ), hearing loss was divided into 4 stages: Stage I: the average hearing threshold\u0026thinsp;\u0026le;\u0026thinsp;25 dBHL; Stage II: average hearing threshold\u0026thinsp;\u0026gt;\u0026thinsp;25\u0026ndash;40 dBHL; Stage III: average hearing threshold\u0026thinsp;\u0026gt;\u0026thinsp;40\u0026ndash;70 dBHL; Stage IV: Mean hearing threshold\u0026thinsp;\u0026gt;\u0026thinsp;70 dBHL.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMRI examinations\u003c/h3\u003e\n\u003cp\u003ePatients underwent DEMRI on 3T scanners (Center A: uMR 790, UIH, China; Center B: Ingenia CX, Philips;Center C: Ingenia CX, Philips), using a commercially available 32-channel head and neck coil. Prior to gadolinium injection, 3D-T2-Sampling Perfection with Application optimized Contrasts using different flip angle Evolutions (3D-T2-SPACE) scan (TR, 1300 ms; TE, 196.68 ms; slice thickness, 0.5 mm; matrix size, 380\u0026times;100; FOV, 220 \u0026times; 180; acceleration factor, 2(2D); scan time, 1 min and 47 s) was performed to exclude patients with organic brain syndrome, inner ear deformities, and acoustic neuroma. 3D-FLAIR sequence(FOV, 220 \u0026times; 190 mm; section thickness, 0.7 mm; TR, 6500 ms; TE, 426 ms; number of excitations, 1; TI, 1935 ms; flip angle, 54\u0026deg;; matrix, 256 \u0026times; 100; bandwidth, 500 Hz/pixel; turbo factor, 5(acs); voxel size, 0.86 \u0026times; 0.86 \u0026times; 1; and scan time, 2min56s) was performed 4h after a double dose of intravenous gadobutrol (7.5 ml/vial, 1.0 mmol/mL; Bayer AG) administration to assure the maximum perilymphatic enhancement (PLE). Some previous reports (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) have proved that gadobutrol has more advantages than other macrocyclic gadolinium contrast agents enhanced MRI in the diagnosis of MD because of its high concentration and high relaxation rate characteristics.\u003c/p\u003e\n\u003ch3\u003eExtraction qualitative and quantitative MRI features\u003c/h3\u003e\n\u003cp\u003eThe MR images were qualitatively analyzed by three experienced radiologists (with an experience of 15, 15 and 20years, respectively) blinded to the clinical findings and symptoms.\u003c/p\u003e \u003cp\u003eThe degree of EH was indicated by a widening of the negative signal gap within the labyrinth. In this study, cochlear and vestibular EH were dichotomized as EH-positive or EH-negative. Cochlear and vestibular EH grades were evaluated according to Gurkov and Bernaerts' visual 4-grade method(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCochlea: Normal(grade 0): the Scala middle(SM) was a vaguely visible dark area, with a relatively straight border with Scala vestibule(SV) and Scala tympani(ST) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Mild hydrops (grade 1): the SM showed obvious black low signal area, surrounded by clear and continuous white high signal perilymphatic ring (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Moderate hydrops (grade 2): white hyperintense perilymphatic ring was significantly interrupted (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Severe hydrops (grade 3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed): The surrounding white hyperintense perilymphatic area becomes a clear straight line.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eVestibular: Normal (grade 0): the saccule and utricle are separated, and the sum of their areas is less than 1/2 of vestibular (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Mild hydrops (grade 1): saccule\u0026thinsp;\u0026ge;\u0026thinsp;utricle, and they can be separated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Moderate hydrops (grade 2): the saccule and utricle fused, and the peripheral perilymph was visible (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Severe hydrops (grade 3): no perilymphatic enhancement of the vestibule (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition to this, a new weighted visual scoring system (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) based on Inner Ear Structural Assignment Method (IESAM)(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) was cited in this study. The signal intensity ratio (SIR) of PLE to ipsilateral middle cerebellar peduncle (MCP) was recorded. The semicircular canals and vestibular aqueduct (VA) were graded as 0, 1 and 2 according to whether they were continuously developed. In total, 6 clinical variables and 17 MRI features were included in the study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Text).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eA new weighted visual scoring criteria based on Inner Ear Structural Assignment Method (IESAM) for inner ear 3D-FLAIR images.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAppearance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eCochlea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVestibule\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eSemicircular canals\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSuperior\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHorizontal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePosterior\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot visible\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartially visible\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompletely visible\u003csup\u003e!\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eData represent scores awarded on 3D-FLAIR images. \u003csup\u003e#\u003c/sup\u003eAbsence of high-signal contrast medium. \u003csup\u003e*\u003c/sup\u003eFailure to show high-signal image of entire cochlear canal, or high-signal image of cochlear canal limited to tympanic or vestibular scale, or interrupted high-signal images of semicircular canals, or incomplete high-signal image of vestibule. \u003csup\u003e!\u003c/sup\u003eAll labyrinth structures completely visible. \u003csup\u003ea\u003c/sup\u003eNo this option (Apex of Cochlea is very small, only set a score of 0 or 1, if it is visible 1 is scored, do not distinguish between partially or completely visible). \u003csup\u003eb\u003c/sup\u003eThe hypointintensity zone in the vestibule exceeds the lower margin of the horizontal semicircular canal, point a score of 3. \u003csup\u003ec\u003c/sup\u003eThe hypointintensity zone in the vestibule was located only above the plane of the horizontal semicircular canal, point a score of 6.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eStatistics\u003c/h3\u003e\n\u003cp\u003eThe IBM SPSS (version 27.0) and R software (version 4.2.1) were applied to analyze all data. Continuous variables are shown as mean with standard deviation (SD) or median with interquartile range (IQR). The measurement data conforming to normal distribution were compared by independent sample \u003cem\u003et-\u003c/em\u003e test. The Mann\u0026mdash;Whitney \u003cem\u003eU\u003c/em\u003e test was used to compare the measurement data that did not conform to the normal distribution. Categorical data were compared by χ\u003csup\u003e2\u003c/sup\u003e test or \u003cem\u003eFisher's\u003c/em\u003e exact test. The Kendall's W test was used to analyze the inter-observer agreement. Multivariable logistic regression analysis was applied to select MD-related features. Variables of clinical and DEMRI with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were included into the multivariate logistic regression model using backward stepwise method to develop three models for MD diagnosis in the training cohort: including DEMRI signature, clinical variables, and DEMRI-clinical parameters, respectively. The validation set was used to validate the models. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) was applied to evaluate the diagnostic performance of the different models. Delong test was used to compare the AUC estimates between the different diagnostic models. A two-tailed p-value less than 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eA total of 85 patients, 162 ears (mean age, 53.2\u0026plusmn;13.6 years; age range, 17\u0026ndash;86 years) were identified. The detailed clinical characteristics and EDMRI characteristics results of all ears in the MD group (n\u0026thinsp;=\u0026thinsp;95 ears) and control group (n\u0026thinsp;=\u0026thinsp;67ears) are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Supplementary Text. Except for gender, BMI and VA visualization degree, the other observation indicators between the MD group and the control group were statistically different (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The detailed clinical characteristics and EDMRI characteristics results of all ears in the training set (n\u0026thinsp;=\u0026thinsp;98) and validation set (n\u0026thinsp;=\u0026thinsp;64) are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e Supplementary Text.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk factors of delayed post gadolinium enhancement MRI for Meni\u0026egrave;re\u0026rsquo;s disease in the training cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2955.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.15E\u0026thinsp;+\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCochlea_EH_Grad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e297.868\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCochlea_Apex_EH_Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.038*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3014.737\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVestibule_EH_Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSemicircular Canal Horizontal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-21.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1477.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.91E\u0026thinsp;+\u0026thinsp;27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.032*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVestibule_EH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1172.845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE/MCPE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote. \u0026mdash;Data are results of the multivariable regression analysis. SE standard error, OR odds ratio, CI confidence interval. * Indicates significant difference. VA vestibular aqueduct, PE/MCPE perilymphatic enhancement/ middle cerebellar peduncle enhancement.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDiagnostic model development and validation\u003c/h3\u003e\n\u003cp\u003eIn the training set, the 17 DEMRI independent descriptors were analyzed by multivariate logistic regression using backward induction. Four descriptors with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e were selected to establish the DEMRI diagnostic model, which indicated a favorable diagnostic performance of MD with an AUC of 0.907 (95% confidence interval [CI]: 0.848\u0026ndash;0.966) in the training cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea) and 0.887 (95% CI:0.802\u0026ndash;0.971) in the validation cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The same analysis method was used to establish a clinical diagnostic model based on two independent descriptors (PTA stage, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; tinnitus fullness, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The AUCs of the clinal model in the training and validation cohorts were 0.915 (95% CI: 0.860\u0026ndash;0.970) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec) and 0.736 (95% CI: 0.617\u0026ndash;0.855) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed), respectively. With the use of multivariable logistic regression analysis, independent descriptors include Cochlea_EH_Grad, Vestibule_EH, PTA Stage and Tinnitus fullness were identified for the combined DEMRI-clinical model (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The AUCs of the DEMRI-clinical model for diagnose MD in the training and validation cohorts were 0.947 (95% CI: 0.903\u0026ndash;0.990) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee) and 0.796(95% CI: 0689\u0026ndash;0.902) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef), respectively. DeLong's test was used to compare the two correlated ROC curves. The AUC value of the DEMRI model was nearly equal to that of the clinical model in training set, but the difference was statistically significant (Z\u0026thinsp;=\u0026thinsp;2.503, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01231; 95%CI:0.033\u0026ndash;0.269) in the validation cohort, the AUC value of the DEMRI model was significant higher, while the DEMRI-clinical model was significantly better than the clinical model in the diagnostic of MD as well (Z\u0026thinsp;=\u0026thinsp;2.2149, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02677). Although the AUC value of DEMRI model was higher than DEMRI-clinical model, but there was no statistically significant difference (Z = -1.9291, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05372) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk factors of DEMRI-clinical for Meni\u0026egrave;re\u0026rsquo;s disease in the training cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCochlea_EH_Grad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVestibule_EH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.083*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTinnitus fullness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.014*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote. \u0026mdash;Data are results of the multivariable regression analysis. SE standard error, OR odds ratio, CI confidence interval. * Indicates significant difference.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic performances of three models in the training and validation cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEMRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTraining cohort\u003c/p\u003e \u003cp\u003eValidation cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValidation cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEMRI-clinical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTraining cohort\u003c/p\u003e \u003cp\u003eValidation cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eAUC: area under the receiver operating characteristic curve, CI: confidence interval, PPV: positive predictive value; NPV: negative predictive value. Features used for the DEMRI model are Cochlea_EH_Grad, Cochlea_Apex_EH_Score, VA and Vestibule_EH. Features used for the Clinical model are Stage and Tinnitus fullness. Features used for the DEMRI-clinical model are Cochlea_EH_Grad, Vestibule_EH, Stage and Tinnitus fullness.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe weights of the four independent risk factors for constructing the DEMRI model are presented in a nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The calibration curve of the DEMRI nomogram demonstrated good agreement both in the training and validation set (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, c).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eInter-observer agreement on the four MRI features of the DEMRI model\u003c/h3\u003e\n\u003cp\u003eInter- observer reliability of the four MRI features of the DEMRI model was evaluated with Kendall's W test. The four observation indicators of \u0026ldquo;Cochlea_EH_Grade\u0026rdquo; \u0026ldquo;Cochlea_Apex_EH_Score\u0026rdquo; \u0026ldquo;Vestibule_EH\u0026rdquo; and \u0026ldquo;VA\u0026rdquo; showed very good consistency with the Kendall's coefficient of cooperation were \u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.954, 0.985, 0.967 and 0.951, respectively, and the \u003cem\u003eP\u003c/em\u003e values were all less than 0.001 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInter-observer reliability Kendall\u0026rsquo; \u003cem\u003eW\u003c/em\u003e values for the four DEMRI model features\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGrade/Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eCochlea_EH_Grade\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eCochlea_Apex_Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eVestibule_EH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eVA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eobserver1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eobserver2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eobserver3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKendall\u0026rsquo;s W\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we developed and validated three models to diagnose MD. Results showed that the DEMRI model and combined DEMRI- clinical model had better clinical diagnostic performance than clinical model (AUC, 0.736; sensitivity, 55.3%; specificity, 92.3%). The DEMRI model demonstrated excellent predictive performance in the validation set (AUC, 0.887; sensitivity, 78.9%; specificity, 88.5%). The AUC value of the combined DEMRI-clinical model was slightly lower than DEMRI model, but there was no significant difference in the diagnostic performance. In the DEMRI diagnostic model, the characteristics with the maximum significance were \u0026ldquo;Cochlea_EH_Grad\u0026rdquo;, \u0026ldquo;Cochlea_Apex_EH_Score\u0026rdquo;, \u0026ldquo;VA\u0026rdquo; and \u0026ldquo;Vestibule_EH\u0026rdquo; by multivariate regression model using backward stepwise method.\u003c/p\u003e \u003cp\u003eMD is associated with a variety of comorbidities, such as migraine, anxiety, allergy, and immune disorders, but its pathogenesis remains unknown(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Researchers have established the histopathological hallmark of MD in EH, which is an increase of the endolymphatic fluid within the membranous labyrinth of the inner ear. EH is considered the result of a change in endolymph homeostasis, generated by increased endolymph production, endolymph malabsorption, or a combination of both(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). In the case of EH, excess volume leads to longitudinal endolymphatic flow from the cochlea to the endolymphatic sac to achieve endolymphatic homeostasis. Gibson(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) proposed that when the endolymphatic sac (ES) and duct (ED) is functional, it can remove excess endolymph, but in Meni\u0026egrave;re\u0026rsquo;s patient who may have a dysfunctional ES and ED, endolymph may accumulate in the sinus of the ED, leading to significant overflow. Various methods have been proposed to assess the endolymphatic space qualitatively and quantitatively(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Studies have shown that he relationship between MD and EH was strong enough to consider it as a hallmark feature of Meniere's disease and a sensitive target for diagnostic detection(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In this study, statistical differences were found in all MRI features related to EH between the MD and control groups (Table\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Text). Among them, three MRI features associated with EH were included in the DEMRI model, including \u0026ldquo;Cochlea_EH_Grad\u0026rdquo;, \u0026ldquo;Cochlea_Apex_EH_Score\u0026rdquo;, and \u0026ldquo;Vestibule_EH\u0026rdquo;. It seems, that cochlea related EH has a greater weight in the diagnosis of MD, and whether there had hydrops at the cochlear apical turn was of great significance. It has been shown that cochlear hydrops has a reliable pattern of hydropic progression over time, which always originates in the apex and proceeds to the base, tonotopically like the progression of hearing loss(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Hydrodynamic pressure shunt in the pars superior stimulates the utricle and the saccule of vestibule, causing \u0026ldquo;Vestibule_EH\u0026rdquo;(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). This longitudinal hydrops process may explain why the presence or absence of EH in the apical turn of the cochlea is of diagnostic significance for MD. In addition, experimental studies suggested that the cytochemical and ultrastructural disruption of the fibrocytes of the hair cells, afferent neurons and fibrocytes of the lateral cochlear wall involved in the pathogenesis of EH and occurred prior to EH(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). These theories support the conclusion that \u0026ldquo;Cochlea_EH_Grad\u0026rdquo; and \u0026ldquo;Vestibule_EH\u0026rdquo; are risk factors for the diagnosis of MD. It is more important to consider the grade of EH in the cochlea and the presence or absence of EH in the vestibule. The more severe the degree of cochlear EH, the more likely the diagnosis of MD is when combined with vestibular EH, regardless of the severity of vestibular EH.\u003c/p\u003e \u003cp\u003eIn addition, this study also found a relatively new point that vestibular aqueduct (VA) is a significant risk factor in the diagnosis of MD. A study by Steve Connor et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) showed that all VA descriptors demonstrated excellent reliability to diagnose MD, incomplete visualization of the VA adds value to the diagnosis. The study by Jeanne et al. even suggested that the evaluation by temporal bone CT of the VA could predict the presence of EH on MRI with a high positive predictive value(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Arnaud et al.(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) proposed discontinuous VA showed a correlation with MD. The non-visible or partially visible VA may be due to bony abnormalities or central fibrosis of the VA, resulting in endolymphatic stenosis. Although the performance of VA was included in the model of this study, there was no statistically significant difference in the performance of VA between the MD and control groups in the supplementary file. This may be related to the low detection rate of VA in MRI, so we may need clearer imaging methods, and in the future, it may need to be combined with other imaging such as CT for comprehensive analysis.\u003c/p\u003e \u003cp\u003eOur study had several limitations. First, although this study was a multicenter study, it was retrospective, and the number of cases collected was not enlarge enough relatively. Thus, researchers should be more logical and cautious in the analysis. Second, there may be deviations in the judgment of unilateral or bilateral MD. Some asymptomatic patients with early MD may be classified into control group. Therefore, it may be necessary to supplement normal people for later research. Third, the main MRI indicators used at present are still traditional imaging features, some new imaging characteristics that need to be more careful excavations.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, we developed and validated a new DEMRI model to diagnose MD, which has higher diagnostic value for MD than clinical inquiry information alone. The combination of high degree of cochlear EH, invisible cochlea apical turn, vestibular hydrops and incomplete visualization suggests a high risk of MD. Therefore, we suggest DEMRI should be recommended when MD is suspected because of its significant potential in the diagnosis of MD. Moreover, further studies are required to explore the generalized utility of our model and apply our results to clinical application.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eDEMRI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;delayed post gadolinium enhancement MRI\u003c/p\u003e\n\u003cp\u003eMD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Meni\u0026egrave;re\u0026rsquo;s disease\u003c/p\u003e\n\u003cp\u003eAIC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; area under the curve\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;receiver operating characteristic curve (ROC)\u003c/p\u003e\n\u003cp\u003eAAO-HNS \u0026nbsp; \u0026nbsp;The American Academy of Otolaryngology-Head and Neck Surgery\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEH \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; endolymphatic hydrops\u003c/p\u003e\n\u003cp\u003eSV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Scala vestibule\u003c/p\u003e\n\u003cp\u003eST \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Scala tympani\u003c/p\u003e\n\u003cp\u003eIESAM \u0026nbsp; \u0026nbsp; \u0026nbsp; Inner Ear Structural Assignment Method\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; perilymphatic enhancement\u003c/p\u003e\n\u003cp\u003eMCP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; middle cerebellar peduncle\u003c/p\u003e\n\u003cp\u003eES \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; endolymphatic sac\u003c/p\u003e\n\u003cp\u003eED \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; endolymphatic duct\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; vestibular aqueduct\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCritical Relevance Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the diagnostic value of delayed enhancement MRI (DEMRI) in Meniere\u0026apos;s disease (MD), and to establish an effective MRI diagnostic model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunder name: National\u0026nbsp;Natural\u0026nbsp;Science\u0026nbsp;Foundation\u0026nbsp;of\u0026nbsp;China.ID:82202089\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXinyi Chen\u003csup\u003e1#\u003c/sup\u003e, Yanfeng Zhao\u003csup\u003e2#\u003c/sup\u003e, Yunchong Han\u003csup\u003e3\u003c/sup\u003e, Kai Wei\u003csup\u003e1\u003c/sup\u003e, Shufang Cheng\u003csup\u003e4\u003c/sup\u003e, Yongjun Ye\u003csup\u003e4\u003c/sup\u003e, Jie Feng\u003csup\u003e4\u003c/sup\u003e, Xinchen Huang\u003csup\u003e1\u003c/sup\u003e, Jingjing Xu\u003csup\u003e1*\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003eDepartment of Radiology, The 2nd Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eDepartment of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003e Department of Radiology, The Second Affiliated Hospital of Jiaxing University, Jiaxing, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u0026nbsp;\u003c/sup\u003eZhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e#\u003c/sup\u003e Xinyi Chen and Yanfeng Zhao contributed equally.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003eCorrespondence:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJingjing Xu\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eE-mail: [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAddress: NO.88 Jiefang Road, Shangcheng District, Hangzhou, Zhejiang Province, China\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXinyi Chen wrote the main manuscript text and prepared figures1-3.\u003c/p\u003e\n\u003cp\u003eYanfeng Zhao main performed statistical analysis and prepared figures 4-5.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors (Xinyi Chen, Yanfeng Zhao, Yunchong Han, Kai Wei, Shufang Cheng, Yongjun Ye, Jie Feng, Xinchen Huang, Jingjing Xu) participated in data collection and experimental design, as well as the review and revision of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to\u0026nbsp;Jingjing Xu\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eE-mail: [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAddress: NO.88 Jiefang Road, Shangcheng District, Hangzhou, Zhejiang Province, China\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study followed the principles outlined in the Declaration of Helsinki, including all amendments and revisions. The research was approved by the Medical Ethics Committee of\u0026nbsp;the\u0026nbsp;Second Affiliated\u0026nbsp;Hospital of Zhejiang University School of Medicine.\u0026nbsp;(NO. [2022] 038). Informed written consent was obtained from all participants after an explanation of the nature of the study, as approved by the Medical Ethics Committee of\u0026nbsp;the\u0026nbsp;Second Affiliated\u0026nbsp;Hospital of Zhejiang University School of Medicine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRizk HG, Mehta NK, Qureshi U, et al. Pathogenesis and Etiology of Meniere Disease: A Scoping Review of a Century of Evidence. JAMA Otolaryngol Head Neck Surg. 2022; 148(4):360-8.\u003c/li\u003e\n\u003cli\u003eMinor LB, Schessel DA, Carey JP. M\u0026eacute;ni\u0026egrave;re\u0026apos;s disease. Current Opinion in Neurology. 2004; 17(1):9-16.\u003c/li\u003e\n\u003cli\u003eSajjadi H, Paparella MM. Meniere\u0026apos;s disease. Lancet. 2008; 372(9636):406-14.\u003c/li\u003e\n\u003cli\u003eCommittee on Hearing and Equilibrium guidelines for the diagnosis and evaluation of therapy in Meni\u0026egrave;re\u0026apos;s disease. American Academy of Otolaryngology-Head and Neck Foundation, Inc. Otolaryngol Head Neck Surg. 1995; 113(3):181-5.\u003c/li\u003e\n\u003cli\u003eHoskin JL. Meniere\u0026apos;s disease: new guidelines, subtypes, imaging, and more. Curr Opin Neurol. 2022; 35(1):90-7.\u003c/li\u003e\n\u003cli\u003eLopez-Escamez JA, Carey J, Chung WH, et al. Diagnostic criteria for Meniere\u0026apos;s disease. 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The vestibular aqueduct ossification on temporal bone CT: an old sign revisited to rule out the presence of endolymphatic hydrops in Meniere\u0026apos;s disease patients. Eur Radiol. 2020; 30(11):6331-8.\u003c/li\u003e\n\u003cli\u003eConnor S, Grzeda MT, Jamshidi B, Ourselin S, Hajnal JV, Pai I. Delayed post gadolinium MRI descriptors for Meniere\u0026apos;s disease: a systematic review and meta-analysis. Eur Radiol. 2023; 33(10):7113-35.\u003c/li\u003e\n\u003cli\u003eChen W, Yu S, Xiao H, et al. A novel radiomics nomogram based on T2-sampling perfection with application-optimized contrasts using different flip-angle evolutions (SPACE) images for predicting cochlear and vestibular endolymphatic hydrops in Meniere\u0026apos;s disease patients. Eur Radiol. 2024.\u003c/li\u003e\n\u003cli\u003eLi J, Jin X, Kong X, et al. Correlation of endolymphatic hydrops and perilymphatic enhancement with the clinical features of M\u0026eacute;ni\u0026egrave;re\u0026apos;s disease. European Radiology. 2024.\u003c/li\u003e\n\u003cli\u003eConnor S, Grzeda MT, Jamshidi B, Ourselin S, Hajnal JV, Pai I. Delayed post gadolinium MRI descriptors for Meniere\u0026apos;s disease: a systematic review and meta-analysis. European Radiology. 2023; 33(10):7113-35.\u003c/li\u003e\n\u003cli\u003eEliezer M, Poillon G, Gillibert A, et al. Comparison of enhancement of the vestibular perilymph between gadoterate meglumine and gadobutrol at 3-Tesla in Meniere\u0026apos;s disease. Diagn Interv Imaging. 2018; 99(5):271-7.\u003c/li\u003e\n\u003cli\u003eXie J, Zhang W, Zhu J, Hui L, Li S, Zhang B. Comparison of inner ear MRI enhancement in patients with Meniere\u0026apos;s disease after intravenous injection of gadobutrol, gadoterate meglumine, or gadodiamide. Eur J Radiol. 2021; 139:109682.\u003c/li\u003e\n\u003cli\u003eGurkov R, Flatz W, Louza J, Strupp M, Krause E. 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Epidemiology and genetics of Meniere\u0026apos;s disease. Curr Opin Neurol. 2024; 37(1):88-94.\u003c/li\u003e\n\u003cli\u003eMohseni-Dargah M, Falahati Z, Pastras C, et al. Meniere\u0026apos;s disease: Pathogenesis, treatments, and emerging approaches for an idiopathic bioenvironmental disorder. Environ Res. 2023; 238(Pt 1):116972.\u003c/li\u003e\n\u003cli\u003eGibson WP. Hypothetical mechanism for vertigo in Meniere\u0026apos;s disease. Otolaryngol Clin North Am. 2010; 43(5):1019-27.\u003c/li\u003e\n\u003cli\u003eWu Q, Dai C, Zhao M, Sha Y. The correlation between symptoms of definite Meniere\u0026apos;s disease and endolymphatic hydrops visualized by magnetic resonance imaging. Laryngoscope. 2016; 126(4):974-9.\u003c/li\u003e\n\u003cli\u003eLi J, Wang L, Hu N, et al. Longitudinal variation of endolymphatic hydrops in patients with Meniere\u0026apos;s disease. Ann Transl Med. 2023; 11(2):44.\u003c/li\u003e\n\u003cli\u003eNadol JB, Adams JC, Kim JR. Degenerative changes in the organ of Corti and lateral cochlear wall in experimental endolymphatic hydrops and human Meni\u0026egrave;re\u0026apos;s disease. Acta Otolaryngol Suppl. 1995; 519:47-59.\u003c/li\u003e\n\u003cli\u003eIchimiya I, Adams JC, Kimura RS. Changes in immunostaining of cochleas with experimentally induced endolymphatic hydrops. Ann Otol Rhinol Laryngol. 1994; 103(6):457-68.\u003c/li\u003e\n\u003cli\u003eAttye A, Barma M, Schmerber S, Dumas G, Eliezer M, Krainik A. The vestibular aqueduct sign: Magnetic resonance imaging can detect abnormalities in both ears of patients with unilateral Meniere\u0026apos;s disease. J Neuroradiol. 2020; 47(2):174-9.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"post gadolinium enhancement MRI, Menière’s disease, endolymphatic hydrops, diagnosis, model","lastPublishedDoi":"10.21203/rs.3.rs-5561016/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5561016/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo explore the diagnostic performance of delayed post gadolinium enhancement MRI (DEMRI) in the diagnosis of Meniere's disease (MD), and to establish an effective MRI diagnostic model.\u003c/p\u003e\u003ch2\u003eMaterials and methods\u003c/h2\u003e \u003cp\u003eThis retrospective multicenter study evaluated DEMRI descriptors of patients with M\u0026eacute;ni\u0026egrave;riform symptoms examined consecutively from May 2022 to May 2024. A total of 162 ears (95 MD ears, 67 control ears) were enrolled in this study. Each ear was randomly allocated to a training set (n\u0026thinsp;=\u0026thinsp;98) and a validation set (n\u0026thinsp;=\u0026thinsp;64). Logistic regression determined three models for the diagnosis of MD in the training cohort. AUC was applied to evaluate the diagnostic performance of different models. Delong test was used to compare the AUC estimates between the different diagnostic models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe proposed DEMRI diagnostic model demonstrated good diagnostic performance in both the training (AUC, 0.907) and the validation cohort (AUC, 0.887), outperforming the clinical diagnostic model (Z\u0026thinsp;=\u0026thinsp;2.503, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01231; 95%CI:0.033\u0026ndash;0.269) in the validation cohort. The AUC value of DEMRI model was higher than combined DEMRI-clinical model in the validation cohort (AUC, 0.796) as well, but there was no statistically significant difference (Z = -1.9291, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05372). In the training set, the sensitivity, specificity, and accuracy of the DEMRI model were 78.9%, 88.5% and 82.8%, respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eA diagnosis model based on DEMRI features for MD diagnosis efficiency was higher than that of clinical variables alone. Therefore, DEMRI should be recommended when MD is suspected because of its significant potential in the diagnosis of MD.\u003c/p\u003e","manuscriptTitle":"A Diagnostic model based on MRI for Meniere's disease: a multicenter study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-24 17:10:33","doi":"10.21203/rs.3.rs-5561016/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"de0afd70-70f4-4332-a69d-e8b2d970e0af","owner":[],"postedDate":"December 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-09T05:38:42+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-24 17:10:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5561016","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5561016","identity":"rs-5561016","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}