Clinical Value of Dual-Energy CT Parameters Combined with Morphological Features in Predicting Graves’ Ophthalmopathy

Clinical Value of Dual-Energy CT Parameters Combined with Morphological Features in Predicting Graves’ Ophthalmopathy | 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 Clinical Value of Dual-Energy CT Parameters Combined with Morphological Features in Predicting Graves’ Ophthalmopathy Zixuan Ma, Tingting Shi, Wei Li, Xiaomei Lu, Lin Yuan, Yongxian Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7332120/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Nov, 2025 Read the published version in BMC Medical Imaging → Version 1 posted 16 You are reading this latest preprint version Abstract Background To investigate the diagnostic value of dual-energy computed tomography (DECT)–derived quantitative parameters combined with morphological features for assessing subtle orbital tissue changes in Graves’ orbitopathy (GO), and to evaluate their feasibility as imaging biomarkers throughout the disease course. Methods Data from patients suspected of having GO were retrospectively collected. All these patients underwent DECT scans and had no history of thyroid function treatment or other medical history, which may have affected the measurement of orbital tissues. Three clinical features, four morphological features, and twenty-four DECT parameters were measured. The overall data were divided into training and test cohorts. Univariate and multivariate analyses were applied to select relevant parameters and construct a nomogram. Results Among the 206 patients suspected of having GO, 134 patients were diagnosed as positive for GO (GO+), and 72 patients were diagnosed as negative (GO-) according to relevant diagnostic criteria. (1) The average thickness, average width, weighted average thickness and weighted average width of orbital muscles significantly differed between the GO + and GO- groups (p < 0.05). (2) The minimum and average values of electron density in orbital muscles and lacrimal glands were significantly different (p < 0.05). (3) A nomogram was constructed to predict the risk of GO, and the area under the curve, sensitivity, and specificity in the training and test cohorts were 0.812, 92.1%, and 59.5% and 0.825, 82.9%, and 71.9%, respectively. Conclusions DECT parameters combined with morphological features not only can be used as robust predictive indicators for diagnosing GO but also represent promising novel biomarkers that may increase the precision and objectivity of clinical assessments. Graves' ophthalmopathy Dual-energy CT Morphological features Nomogram Electron density Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Graves' orbitopathy (GO) is an autoimmune disorder closely associated with thyroid dysfunction and presents with a complex and diverse spectrum of clinical manifestations. It can affect one or both eyes, with symptoms such as eyelid retraction, proptosis, diplopia, restrictive strabismus, exposure keratopathy, and dysthyroid optic neuropathy. These symptoms significantly impact patients’ quality of life[ 1 ]. Given the heterogeneous nature of clinical presentations of GO, precise diagnosis plays a pivotal role in the formulation of appropriate treatment strategies. At present, the management of GO throughout its course relies primarily on a comprehensive evaluation integrating clinical activity score(CAS) and imaging assessments. However, the CAS may be influenced by subjective factors, especially in activity assessment and treatment response, which can lead to variability in clinical decision-making. This underscores the need for objective, reproducible imaging-based parameters to support GO diagnosis and management. Among available imaging modalities, magnetic resonance imaging (MRI) is one of the most widely used tools due to its excellent soft tissue contrast and multiparametric imaging capacities. Contrast-enhanced MRI enables detailed staging of GO and comprehensively evaluate the response of hormonal therapy, as evidenced by previous studies [ 2 – 5 ]. However, this application also has certain limitations, including limited availability, long scheduling times, contrast agent allergy, and contraindications in certain patients populations. In addition to MRI, single-photon emission computed tomography/X-ray computed tomography (SPECT/CT) is another valuable imaging modality for accessing disease activity and stage[ 6 – 7 ]. However, owing to its high cost and significant radiation exposure limited its routine clinical application. Conventional CT is commonly used as a supplementary tool evaluating orbital muscle hypertrophy. Despite its widespread use, conventional CT lacks the capability for detailed quantitative analysis of microstructure changes in orbital tissue. Dual-energy computed tomography (DECT) has the ability to reflect the characteristics of tissues and lesions, thereby differentiating normal tissues from lesions [ 8 ], benign from malignant tumors [ 9 ], and various pathological subtypes [ 10 ]. Previous studies have demonstrated that DECT-derived quantitative parameters, such as iodine density, spectral attenuation slope, effective atomic number (Z eff ), and electron density (ED), can differentiate benign and malignant ocular tumors [ 11 ] and discriminate orbital lymphoma from other orbital lymphoproliferative diseases. However, research applying DECT in the context of GO remains limited. Therefore, the aim of this study was to quantitatively access changes in subtle orbital tissue using DECT-derived parameters in combined with morphological features, exploring the diagnostic value of these parameters in GO, and evaluating its feasibility as an imaging biomarker in the management of the entire disease course. METHODS Study design and population This retrospective study was approved by the Institutional Review Board. Patients with suspected GO identified through DECT scans between November 2023 and January 2025 were retrospectively included. The inclusion criteria were as follows: (1) orbital DECT examination; (2) no history of thyroid function treatment of other hormonal therapies; and (3) no history of trauma, ocular tumors or any other medical conditions that could potentially affect the observation and measurement of orbital tissues. The diagnostic criteria for GO [ 1 ] are as follows: if eyelid retraction is the initial symptom, the diagnosis can be made after excluding other causes and in combination with one of the following three results. (1) At least one of the indicator of thyroid function or thyroid-related antibody [free triiodothyronine, free thyroxine, total triiodothyronine, total thyroxine, thyroid stimulating hormone in serum, thyrotrophin receptor antibody] is abnormal. (2) Proptosis is present, which is defined as a disparity in exophthalmos between the two eyes exceeding 2 mm. (3) The orbital muscles (OM) are involved, manifested as regular thickening in the middle and posterior segments of one or more OMs that do not involve the tendon, as shown by orbital CT or orbital MRI. If the initial symptom is an abnormality in thyroid function or thyroid-related antibodies, the diagnosis can be made after excluding other causes and in combination with one of the following three findings. (1) Eyelide retraction; (2) proptosis; (3) OM involvement. The case screening flowchart is shown in Fig. 1 . The sinus CT images of 30 patients with sinusitis were selected, and the width and thickness of each extraocular muscle were measured. The measured data were compared with the study of Ruili Li [ 12 ]. Consistent patterns indicated accurate measurements. The reference value for OM thickening was set as the mean + 2 standard deviations, as established by previous research [ 13 ]. DECT Protocol All patients underwent DECT scans using a dual-source CT scanner (Somatom Force, Siemens Healthineers). The scan parameters were as follows: dual X-ray tube voltages of 100/150 kV (Sn), the tube currents were 195/150 mAs, the pitch was set at 0.8, the collimation width was 192×0.6 mm, and a rotation time of 1 second. The reconstruction parameters included: the slice thickness was 0.75 mm, and the slice interval was 0.75 mm. The matrix was 512×512, and the ADMIRE level was set at 3. Image analysis All the images were processed and analyzed using the Siemens CT postprocessing workstation (Simens Syngo.via). Two radiologists independently analyzed the images. All the individuals involved in the observation remaining blinded to the clinical pathological results and the research design. A total of 31 parameters were measured and analyzed in this study, including 3 clinical features, 4 morphological features, and 24 DECT-derived parameters. The clinical features were obtained by consulting the hospital information system. The morphological features and DECT-derived parameters were extracted using Syngo.via workstation. On the Syngo.via workstation, oblique coronal images perpendicular to the optic nerve were reconstructed, with the slice thickness of 2 mm. The morphological features of the OM were measured 4.5 mm posterior to the junction of the optic nerve and the ocular wall (Fig. 2 A). For the medial and lateral rectus muscles, the vertical diameter was used to represent their width, and the horizontal diameter was used to represent their thickness. For the superior and inferior rectus muscles, the vertical diameter represents their thickness, and the horizontal diameter represents their width. The vertical diameter was measured as the distance between the uppermost and lowermost edges of the muscle belly, and the horizontal diameter was measured as the distance between the innermost and outermost edges of the muscle belly (Fig. 2 B). Typically, the level 4.5 mm posterior to the eyeball is the plane where the maximum diameter of the orbital muscle belly of healthy individuals is located [ 12 ]. However, the thickening of the OM is not necessarily confined to this plane. Therefore, if significant thickening of the OM is detected, the plane with the thickest part of the muscle is selected for measurement. For the widths and thicknesses of the eight OMs on both sides, the bilateral data of each orbital muscle are combined. The larger value of each pair of corresponding OMs is retained, and the combined data are used to calculate the average thickness (AT), weighted average thickness (WAT), average width (AW), and weighted average width (WAW). The calculation method for the WAT is as follows: $$\:\text{W}\text{A}\text{T}=\frac{\sum\:_{\text{i}=0}^{\text{n}}{\text{t}\text{h}\text{i}\text{c}\text{k}\text{n}\text{e}\text{s}\text{s}\:\text{o}\text{f}\:\text{t}\text{h}\text{e}\:\text{O}\text{M}}_{\text{i}}\times\:{\text{r}\text{e}\text{f}\text{e}\text{r}\text{e}\text{n}\text{c}\text{e}\:\text{v}\text{a}\text{l}\text{u}\text{e}\text{s}\:\text{f}\text{o}\text{r}\:\text{t}\text{h}\text{e}\:\text{t}\text{h}\text{i}\text{c}\text{k}\text{n}\text{e}\text{s}\text{s}\:\text{o}\text{f}\:\text{O}\text{M}}_{\text{i}}}{\sum\:_{\text{i}=0}^{\text{n}}{\text{r}\text{e}\text{f}\text{e}\text{r}\text{e}\text{n}\text{c}\text{e}\:\text{v}\text{a}\text{l}\text{u}\text{e}\text{s}\:\text{f}\text{o}\text{r}\:\text{t}\text{h}\text{e}\:\text{t}\text{h}\text{i}\text{c}\text{k}\text{n}\text{e}\text{s}\text{s}\:\text{o}\text{f}\:\text{O}\text{M}}_{\text{i}}}$$ Twenty-four DECT-derived parameters, including the maximum, minimum, and average values of Z eff and ED of the OM, lacrimal glands (LG), optic nerves (ON), and fat (F), were used. To obtain morphological features, all OMs were manually delineated to cover their areas as completely as possible (Fig. 2 C). Oblique transverse images parallel to the ON were reconstructed, with the slice thickness of 2 mm. On the oblique transverse image where the LG is most prominently displayed, the bilateral LG is manually delineated to cover their areas as completely as possible (Fig. 2 D). On the oblique transverse image where the ON is most prominently shown, the Z eff and ED of the bilateral ON and F surrounding the ON were measured. Circular regions of interest with areas of 5 mm² were delineated at the middle and posterior thirds of the intraorbital segment of the ON and manually delineated F to cover their areas as completely as possible (Fig. 2 E). Statistical analysis The data were statistically analyzed using R (version 4.4.2) software. The intraclass correlation coefficient (ICC) with two-way random effects was used to assess the agreement between two radiologists. Values ≥ 0.75 were considered indicative of excellent reliability, and average processing was performed for the two groups of data. Data are presented as means ± standard deviations for normally distributed data and medians (interquartile ranges) for non-normally distributed data. Student’s t-test or the Mann–Whitney U test was used to compare the differences in quantitative parameters between the two groups according to the distribution type of the data. All patients were randomly divided into a training dataset and a test dataset in a 1:1 ratio. Univariable logistic regression was applied to the training dataset to screen the relevant features (univariable p < 0.05) for predicting GO. Backward-stepwise multivariable logistic regression was then used to narrow down the final features from the univariable predictors and construct the predictive nomogram in the training dataset. The receiver operating characteristic (ROC) curve was generated, and the area under the curve (AUC), sensitivity, and specificity were calculated to quantify model performance in the training dataset and test dataset. The AUC, calibration, and decision curve were analyzed for model discrimination, calibration, and clinical application. Comparisons of AUCs were conducted using the DeLong test. The Hosmer–Lemeshow test was used for calibration analysis, where a p value > 0.05 was considered to indicate good model fit and was regarded as statistically significant. RESULTS General Information and Interobserver Consistency The ICCs for the measurement data between the two radiologists were greater than 0.75. As measured from the sinus CT images, the widths (in mm) of the medial rectus muscle, superior rectus muscle, lateral rectus muscle and inferior rectus muscle were 8.930 ± 1.069, 7.775 ± 1.062, 9.833 ± 0.894, and 7.010 ± 1.003, respectively, and the thicknesses (in mm) were 3.305 ± 0.514, 3.117 ± 0.686, 2.995 ± 0.603, and 3.658 ± 0.692, respectively. These measurements are consistent with the research pattern of Li Ruili [ 12 ]. Comparison of Morphological and DECT Parameters between the GO + and GO − Groups There were no significant differences in age or sex between patients in the GO + group and the GO- group (P > 0.05) (Table 1 ). All morphological features, including AT, AW, WAT and WAW, were significantly greater in the GO + group than in the GO- group (P < 0.05). In terms of the DECT parameters, the minimum and average ED values of OM (ED-OM(min), ED-OM(avg)) and LG (ED-LG(min), ED-LG(avg)) were significantly lower in the GO + group than in the GO- group. There were no significant differences in the remaining ED parameters or in any of the Z eff parameters between the two groups (Table 1 ). Table 1 Clinical characteristics and dual-energy CT (DECT) parameters of patients with and without Graves’ orbitopathy Characteristics GO+ (n = 134) GO- (n = 72) P value Clinical features Age(years) 45.22 ± 13.79 42.88 ± 17.88 0.480 BMI 23.8 ± 2.45 23.9 ± 2.21 0.312 Gender 0.213 Male 47(35.1%) 23(31.9%) Female 87(64.9%) 49(68.1%) Shape features AT 4.28(3.79,4.73) 3.29(3.14,3.44) < 0.001 AW 8.80(8.50,9.33) 8.33(8.08,8.74) < 0.001 WAT 4.15(3.91,4.88) 3.44(3.21,4.25) < 0.001 WAW 8.84(8.65,9.40) 8.55(8.18,9.01) 0.001 DECT parameters ED-OM(max) 43.91 ± 3.63 44.46 ± 2.17 0.026 ED-OM(avg) 32.75(28.24,37.14) 40.58(40.07,40.97) < 0.001 ED-OM(min) 34.10(32.94,38.21) 37.62(36.29,39.16) < 0.001 ED-LG(max) 53.42(50.42,56.70) 54.45(50.98,56.60) 0.461 ED-LG(avg) 40.10(34.00,47.78) 51.70(47.60,54.95) < 0.001 ED-LG(min) 38.40(31.50,46.40) 48.88(44.73,53.90) < 0.001 ED-ON(max) 37.20(31.60,42.10) 34.20(29.10,40.80) 0.482 ED-ON(avg) 28.25 ± 8.78 29.21 ± 5.50 0.575 ED-ON(min) 20.80(13.60,27.75) 24.20(20.50,27.50) 0.270 ED-F(max) -49.40(-58.10, -39.80) -52.00(-57.60, -47.88) 0.060 ED-F(avg) -54.34 ± 9.06 -57.21 ± 2.96 0.065 ED-F(min) -60.64 ± 7.92 -59.78 ± 2.71 0.522 Z eff -OM(max) 7.84(7.71,8.01) 7.83(7.69,8.03) 0.689 Z eff -OM(avg) 7.60 ± 0.14 7.59 ± 0.14 0.646 Z eff -OM(min) 7.30(7.23,7.45) 7.34(7.22,7.51) 0.629 Z eff -LG(max) 7.38(7.30,7.52) 7.35(7.25,7.49) 0.308 Z eff -LG(avg) 7.25 ± 0.15 7.24 ± 0.18 0.867 Z eff -LG(min) 7.10(6.98,7.19) 7.19(7.04,7.28) 0.149 Z eff -ON(max) 7.80(7.64,8.03) 7.71(7.59,7.85) 0.056 Z eff -ON(avg) 7.54 ± 0.21 7.50 ± 0.19 0.430 Z eff -ON(min) 7.28(7.09,7.36) 7.30(7.18,7.43) 0.322 Z eff -F(max) 6.67(6.56,6.83) 6.59(6.50,6.84) 0.355 Z eff -F(avg) 6.51 ± 0.18 6.47 ± 0.17 0.300 Z eff -F(min) 6.31(6.20,6.45) 6.27(6.21,6.40) 0.587 GO+/- patients with/without Graves’ orbitopathy, AT average thickness, AW average width, WAT weighted average thickness, WAW weighted average width, ED electron density, Z eff effective atomic number, OM orbital muscle, LG lacrimal gland, ON optic nerve, F fat Univariate and Multivariate Logistic Regression: Model Development and Evaluation The univariable and multivariable logistic regressions were based on these characteristics, which selected the DECT parameters with a p value < 0.05 (Table 2 ). These highly correlated factors were subsequently analyzed using backward-stepwise multivariable logistic regression to establish a model with p = 1.61WAT + 0. 79WAW − 0.31ED-OM(min) − 3.13 based on the training set, which was used to test the diagnostic consistency and efficiency of the test set in Table 3 . The combined model is presented as a nomogram (Fig. 3 ). The Hosmer–Lemeshow test yielded p values of 0.631 and 0.364 for the training and test sets, respectively (Fig. 4 ). Decision curve analysis and ROC curve analysis for three highly correlated factors and the model in the training and test sets are shown in Fig. 4 . The AUC, cutoff value, sensitivity, specificity, precision, and accuracy of the highest correlation factors and the model for differential diagnosis are shown in Table 3 . The AUC, sensitivity, specificity, and accuracy of the model in the training and test datasets were 0.812, 92.1%, 59.4%, and 74.5% and 0.825, 82.9%, 71.9%, and 77.6%, respectively (Table 3 ). Sample cases of the diagnostic use of the nomogram are provided in Fig. 5 . Table 2 Univariate and multivariate logistic regression analyses of the characteristics for predicting Graves’ orbitopathy in the training cohort (n = 103) Characteristics Univariate logistic regression Multivariate logistic regression OR 95%CI P value OR 95%CI P value AT 156.889 16.808-4593.773 < 0.001 AW 4.159 1.780-12.371 0.003 WAT 2.686 1.720–4.481 < 0.001 2.142 1.337–3.674 0.003 WAW 1.848 1.223–2.948 < 0.001 1.732 1.066–2.941 0.032 ED-OM(avg) 0.286 0.102–0.536 0.002 ED-OM(min) 0.785 0.691–0.876 < 0.001 0.810 0.711–0.907 0.001 ED-LG(avg) 0.835 0.748–0.909 < 0.001 ED-LG(min) 0.869 0.794–0.932 < 0.001 Notably, the results were based on the training cohort. Table 3 Risk factors and combined model of DECT for Graves’ orbitopathy (n = 206) AUC(95%CI) Cutoff Sen Spe PPV NPV Pre Acc Training set(n = 103) WAT 0.739(0.653–0.824) 3.715 0.873 0.635 0.671 0.855 0.599 0.745 WAW 0.660(0.568–0.752) 8.540 0.857 0.500 0.593 0.804 0.664 0.664 ED-OM(min) 0.687(0.596–0.779) 34.765 0.444 0.122 0.301 0.205 0.679 0.270 Combined model 0.812(0.740–0.884) 0.298 0.921 0.595 0.659 0.898 0.642 0.745 Test set(n = 103) WAT 0.778(0.657–0.899) 3.715 0.857 0.750 0.789 0.828 0.567 0.806 WAW 0.631(0.494–0.769) 8.555 0.857 0.500 0.652 0.762 0.687 0.687 ED-OM(min) 0.695(0.561–0.828) 34.950 0.343 0.125 0.300 0.148 0.597 0.239 Combined model 0.825(0.724–0.926) 0.410 0.829 0.719 0.763 0.793 0.567 0.776 AUC , area under the curve; cutoff cutoff value; Sen , sensitivity; Spe , specificity; PPV , positive predictive value; NPV , negative predictive value; Pre precision; Acc , accuracy. The combined model combines the most powerful DECT-derived parameters (WAT, WAW, and ED-OM(min)) and is based on training set DISCUSSION This study developed a model that combines morphological features and dual-energy CT parameters to predict the risk of Graves' ophthalmopathy. The model demonstrated excellent predictive ability in both the training and test sets, with AUCs of 0.812 and 0.825, respectively, and high diagnostic accuracies of 74.5% and 77.6%, respectively. Through univariate and multivariate logistic regression analyses, the most predictive features-namely, WAW, WAT, and ED-OM (min)-were identified. During the model construction process, these features were shown to have a significant correlation with the risk of GO. These DECT-derived quantitative parameters can not only be directly applied to diagnose GO but also serve as promising novel biomarkers that may enhance the precision and objectivity of clinical assessments. All morphological features, including AT, AW, WAT and WAW, were significantly greater in the GO + group than in the GO- group, indicating that the OMs of patients with GO are significantly thicker than those of the normal population. This finding is consistent with the conclusions of Bartalena L. [ 14 ]. Univariate logistic regression analysis revealed that these four features were closely related to the risk of GO. After verification by multivariate logistic regression analysis, WAW and WAT were found to be more significant than AW and AT in predicting the risk of GO. This is because WAW and WAT calculation designs incorporate reference values of OM thickness and width for weighted calculation. This fully account for the weights of thicknesses and widths of different OMs overall, keenly capturing the impact of their individual changes, and avoids omitting relatively minor changes. In terms of DECT parameters, ED mainly describes the distribution probability of electrons in space [ 15 ], while Z eff reflects the absorption characteristics of substances to X-rays [ 15 ]. The results of this study revealed significant differences in ED-OM (min), ED-OM (avg), ED-LG (min), and ED-LG (avg) between patients in the GO + and GO- groups. This is because during the development of GO, the autoimmune response affects tissues through lymphocyte infiltration and stimulation of inflammatory factors [ 16 ]. On one hand, lymphocyte infiltration increases the number of cells in affected tissues. These cells contain numerous biological macromolecules, such as proteins and nucleic acids, and the electron cloud distribution in these molecules is relatively complex and dense [ 17 ]. The stimulation of inflammatory factors leads to edema of muscle tissues, and many causing many water molecules to enter the intercellular spaces [ 16 ], which disrupts the originally relatively uniform electron cloud distribution state of the affected tissues and alters ED. On the other hand, the inflammatory response damages the original microstructure of the affected tissues, such as the arrangement of muscle fibers and the tissue structure [ 18 ], disrupting the regular distribution of the electron cloud. Although the microstructure and ED of the affected tissues have altered, there is no significant change in the elemental composition, which makes the macroscopic characteristics of the substance's absorption of X-rays relatively stable. Therefore, there are no significant differences in Z eff between the GO + group and the GO- group. The multivariate logistic regression results revealed that the most severely affected part in GO patients was the OM, which is consistent with the conclusions of previous research by Schuh A [ 19 ]. Currently, ED and Z eff are primarily used in research related to cartilage, ligaments and tumors [ 20 , 21 ] but rarely for predicting GO. The study by Goo HW indicates that the errors in calculating ED and Z eff through DECT are extremely small, i.e., 1.7% and 4.1%, respectively[ 22 ], and both have high accuracy in evaluating tissue components. ED has the ability to capture changes in the microstructure and chemical composition of tissues. It can directly observe a series of pathological changes in GO, such as lymphocyte infiltration and tissue edema caused by the autoimmune response, making it an effective indicator for predicting GO. By comparing the AUCs of single parameters with those of the combined model, it can be concluded that the combined model has more advantages in predicting GO. The results of the Hosmer–Lemeshow test and DCA both indicate that this model can provide more practical benefits to patients and can be used for subsequent clinical analysis and decision-making. Zhao W constructed a model combining morphological and functional features[ 23 ], which also showed good results in predicting the occult metastasis pattern of central cervical lymph nodes in papillary thyroid carcinoma patients. These findings verify that the modeling method adopted in this study has broad applicability and effectiveness and is expected to play an important role in the diagnosis and prediction of more diseases. Nevertheless, the current study has several limitations. First, this study indirectly demonstrates the feasibility of using DECT for disease management, but does not directly compare it with the CAS in activity assessment and treatment response. Additionally, this study was conducted at a single institution, which may limit the generalizability of the findings to different populations and clinical presentations of GO. This study provides a preliminary exploration of the utility of DECT quantitative parameters combined with morphological features for diagnosing GO, and validated the feasibility of using DECT as a biomarker for disease management in GO. Future studies are warranted to further investigate the associations of these DECT parameters with disease activity and specific complications, such as dysthyroid optic neuropathy. Such extended analyses could enhance the clinical applicability of DECT imaging, providing deeper insights into disease progression and facilitating more targeted therapeutic interventions. CONCLUSIONS In conclusion, the parameters derived from DECT (ED-OM(min)) and morphological features (WAW, WAT) collectively serve as robust predictive indicators for diagnosing GO. The DECT parameters have specific relevance for GO. Abbreviations DECT = energy computed tomography; GO = Graves' orbitopathy; MRI = magnetic resonance imaging; Z eff = effective atomic number; ED = electron density; OM = orbital muscles; LG = lacrimal glands; ON = optic nerves; F = fat; AT = average thickness; WAT = weighted average thickness; AW = average width; WAW = weighted average width; AUC = Area under the curve Declarations Ethics approval and consent to participate This study was approved by the Ethics committee of the Beijing Tongren Hospital of Capital Medical University(TRECKY2016-003). All methods were carried out in accordance with relevant guidelines and regulations. Informed consent to participate was obtained from all participants prior to their inclusion in the study Consent for publication Not applicable. Availability of data and materials The data that support the findings of this study are available on request from the corresponding author. Competing interests The authors declare that they have no competing interests. Funding This work has not received any funding. Acknowledgements Not applicable. Authors' contributions ZM: conceptualization, data curation, methodology, formal analysis, resources, writing—original draft, visualization. TS: formal analysis, data curation, writing—review and editing. WL: methodology, formal analysis. XL: visualization, writing—review and editing.LY: resources, conceptualization.YZ :resources, software. YN: writing—review and editing, project administration. DL:writing—review and editing,conceptualization, data curation. All authors read and approved the final manuscript. References Oculoplastic and Orbital Disease Group of Chinese Ophthalmological Society of Chinese Medical Association, Thyroid Group of Chinese Society of Endocrinology of Chinese Medical Association (2022) Chinese guideline on the diagnosis and treatment of thyroid-associated ophthalmopathy. Chinese Journal of Ophthalmology 2022;58(9):646-668 Song C, Luo Y, Yu G, Chen H, Shen J, Current insights of applying MRI in Graves' ophthalmopathy. Front Endocrinol 2022;13:991588 Wu D, Zhu H, Hong S, Li B, Zou M, Ma X, et al. Utility of multiparametric quantitative magnetic resonance imaging of the lacrimal gland for diagnosing and staging Graves' ophthalmopathy. Eur J Radiol 2021;141:109815 Das T, Roos JCP, Patterson AJ, Graves MJ, Murthy R. T2-relaxation mapping and fat fraction assessment to objectively quantify clinical activity in thyroid eye disease: an initial feasibility study. Eye 2019;33(2):235-243 Zou M, Wu D, Zhu H, Huang X, Zhao X, Zhao J, et al. Multiparametric quantitative MRI for the evaluation of dysthyroid optic neuropathy. Eur Radiol 2022;32(3):1931-1938 Mei T, Xue Z,Yang L, He S, Shu Y, Xin Y, et al. The application of 99 Tc m -DTPA orbital SPECT/CT in staging evaluation of thyroid associated ophthalmopathy. Chinese Journal of Ophthalmology 2021;57(11):830-836 Ochmann A, Winder M, Nalewajka-Kołodziejczak J, Chudek J. Current imaging methods for assessing Graves` orbitopathy activity with particular emphasis on FDG-PET. Front Endocrinol 2023;14:1138569 Wang P, Tang Z, Xiao Z, Wu L, Hong R, Duan F, et al. Dual-energy CT in predicting Ki-67 expression in laryngeal squamous cell carcinoma. Eur J Radiol 2021;140:109774 Kim C, Kim W, Park SJ, Lee YH, Hwang SH, Yong HS, et al. Application of Dual-Energy Spectral Computed Tomography to Thoracic Oncology Imaging. Korean J Radiol 2020;21(7):838-850 Li M, Zheng X, Li J, Yang Y, Lu C, Xu H, et al. Dual-energy computed tomography imaging of thyroid nodule specimens: comparison with pathologic findings. Invest Radiol 2012;47(1):58-64 Luo S, Sha Y, Wu J, Lin N, Pan Y, Zhang F, et al. Differentiation of malignant from benign orbital tumors using dual-energy CT. Clin Radiol 2022;77(4):307-313 Rui L, Shuang X, Jian W, Feng S, Ji Q. MRI study of the thickness and width of the extraocular muscles in normal subjects. Chinese Journal of Ophthalmology 2015;51(6):434-438 Koizumi T, Tanaka T, Umeda K, Komiyama D, Obata H. Correlation between extraocular muscle enlargement and thyroid autoantibodies in thyroid eye disease. Jpn J Ophthalmol 2024;68(3):250-258 Bartalena L, Kahaly GJ, Baldeschi L, Dayan CM, Eckstein A, Marcocci C, et al. The 2021 European Group on Graves' orbitopathy (EUGOGO) clinical practice guidelines for the medical management of Graves' orbitopathy. Eur J Endocrinol 2021; 27;185(4):G43-G67 Hua CH, Shapira N, Merchant TE, Klahr P, Yagil Y. Accuracy of electron density, effective atomic number, and iodine concentration determination with a dual-layer dual-energy computed tomography system. Med Phys 2018;45(6):2486-2497 Moledina M, Damato EM, Lee V. The changing landscape of thyroid eye disease: current clinical advances and future outlook. Eye 2024;38(8):1425-1437 Hai YP, Lee ACH, Frommer L, Diana T, Kahaly GJ. Immunohistochemical analysis of human orbital tissue in Graves' orbitopathy. J Endocrinol Invest 2020;43(2):123-137 Lanzolla G, Marinò M, Menconi F. Graves disease: latest understanding of pathogenesis and treatment options. Nat Rev Endocrinol 2024;20(11):647-660 Schuh A, Ayvaz G, Baldeschi L, Baretić M, Bechtold D, Boschi A, et al. Presentation of Graves' orbitopathy within European Group On Graves' Orbitopathy (EUGOGO) centers from 2012 to 2019 (PREGO III). Br J Ophthalmol 2024;29;108(2):294-300 Rajiah P, Sundaram M, Subhas N. Dual-Energy CT in Musculoskeletal Imaging: What Is the Role Beyond Gout? AJR Am J Roentgenol 2019;213(3):493-505 Agostini A, Borgheresi A, Mari A, Floridi C, Bruno F, Carotti M, et al. Dual-energy CT: theoretical principles and clinical applications. Radiol Med 2019;124(12):1281-1295 Goo HW, Goo JM. Dual-Energy CT: New Horizon in Medical Imaging. Korean J Radiol 2017;18(4):555-569 Zhao W, Shen S, Ke T, Jiang J, Wang Y, Xie X, et al. Clinical value of dual-energy CT for predicting occult metastasis in central neck lymph nodes of papillary thyroid carcinoma. Eur Radiol 2024;34(1):16-25 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Nov, 2025 Read the published version in BMC Medical Imaging → Version 1 posted Editorial decision: Revision requested 15 Oct, 2025 Reviews received at journal 14 Oct, 2025 Reviews received at journal 13 Oct, 2025 Reviewers agreed at journal 11 Oct, 2025 Reviewers agreed at journal 11 Oct, 2025 Reviews received at journal 10 Oct, 2025 Reviewers agreed at journal 10 Oct, 2025 Reviewers agreed at journal 10 Oct, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviewers agreed at journal 23 Sep, 2025 Reviewers invited by journal 15 Sep, 2025 Editor invited by journal 08 Sep, 2025 Editor assigned by journal 17 Aug, 2025 Submission checks completed at journal 14 Aug, 2025 First submitted to journal 14 Aug, 2025 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-7332120","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":518704875,"identity":"455c52f3-3726-48a9-a0e7-38481c9fc992","order_by":0,"name":"Zixuan Ma","email":"","orcid":"","institution":"Beijing Tongren Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zixuan","middleName":"","lastName":"Ma","suffix":""},{"id":518704879,"identity":"6aaf18c6-fb7f-43dc-820f-f128917b0f36","order_by":1,"name":"Tingting Shi","email":"","orcid":"","institution":"Beijing Tongren 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06:19:43","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98913,"visible":true,"origin":"","legend":"","description":"","filename":"8d272ad0c714473c8e1860a8ae9eb4e11structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7332120/v1/c67676eba0f62a9bf23d76ed.xml"},{"id":92052641,"identity":"90684940-2079-4c4f-9163-9a3fa206954c","added_by":"auto","created_at":"2025-09-24 06:19:43","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":106453,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7332120/v1/fdae5e5acf052c2369ed8455.html"},{"id":92052626,"identity":"ee538760-edba-4eaf-9a75-b0dbfffb556b","added_by":"auto","created_at":"2025-09-24 06:19:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":405358,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the patients enrolled in our study\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7332120/v1/69eda83b8e2b2803952987e5.png"},{"id":92053970,"identity":"d44716c0-1b17-480d-9ce0-209ede51b168","added_by":"auto","created_at":"2025-09-24 06:27:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":489629,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the measurement of morphological features and dual-source CT (DECT) parameters. In (A), \"a\" represents the measurement plane, which is located 4.5 mm behind the junction of the optic nerve and the eyeball wall. In (B), \"a\", \"d\", \"f\", and \"h\" are the widths of the lateral, inferior, medial and superior rectus muscles, respectively, whereas \"b\", \"c\", \"e\", and \"g\" are their corresponding thicknesses. (C) is used to measure the effective atomic number (Z\u003csub\u003eeff\u003c/sub\u003e) and electron density (ED) of each orbital muscle. (D) Z\u003csub\u003eeff\u003c/sub\u003e and ED of the lacrimal gland were measured. (E) Z\u003csub\u003eeff\u003c/sub\u003e and ED of the optic nerve and fat were measured.\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7332120/v1/4e1d29d307029f4ccaaa8cb0.png"},{"id":92054314,"identity":"efef7e19-ff23-426b-9a63-380cb6a30b3f","added_by":"auto","created_at":"2025-09-24 06:35:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":237326,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting Graves' ophthalmopathy (GO) in patients. A nomogram was constructed using WAT, WAW and ED-OM (min).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7332120/v1/b56c90273f365d2ac2ed93a0.png"},{"id":92053971,"identity":"9ab8cd70-6cc0-4e8e-afda-f4a2314df7d5","added_by":"auto","created_at":"2025-09-24 06:27:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":638408,"visible":true,"origin":"","legend":"\u003cp\u003eThe effectiveness of the nomogram for predicting Graves' ophthalmopathy (GO) in patients. Good calibration of the nomogram is shown in both the training (A) and test (B) sets (p = 0.631, 0.364). Decision curve analysis of the nomogram for the training (C) and test (D) sets. The x-axis represents the threshold probability, and the y-axis represents the net benefit. The diagnosticperformance of the WAT, WAW, ED-OM(min), and thenomogram was assessed and compared through ROC curves in the training (E) and test (F) sets. The AUCs of the nomograms in the training and test sets were 0.81 and 0.83, respectively.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7332120/v1/a1df3f58f552e6f46c42087d.png"},{"id":92053973,"identity":"b7a52bed-804e-4c8d-bd82-114edf3d9b4a","added_by":"auto","created_at":"2025-09-24 06:27:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":817809,"visible":true,"origin":"","legend":"\u003cp\u003eA patient was suspected of having Graves' ophthalmopathy (GO). From A and B, the WAT can be calculated to be 3.57 mm, and the WAW is 7.74 mm. From C and D, ED-OM(min) is obtained as 23.1. Vertical lines of each variable were drawn in the nomogram (E) to obtain a total possible score of 130. The graph revealed that the risk of GO was \u0026gt; 80% when a vertical line was drawn on the “Total points” scale. The results of thyroid function tests revealed that the free triiodothyronine (FT3) level was 22.20 pmol/L (normal range: 3.1–6.8 pmol/L), the free thyroxine (FT4) level was 52.48 pmol/L (normal range: 12–22 pmol/L), the thyroid-stimulating hormone (TSH) level was \u0026lt;0.005 mIU/L (normal range: 0.27–4.2 mIU/L), and the thyroid-stimulating hormone receptor antibody (TRab) level was 29.8 IU/L (normal range: \u0026lt;1.75 IU/L). FT3, FT4 and TRab are higher than the normal ranges, whereas TSH is lower than the normal range. *WAT value: green line; WAW value: pink line; ED-OM (min) value: blue line\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7332120/v1/82e18d0b0e7a2d3036937fd6.png"},{"id":97178295,"identity":"695f4e08-a9b9-4e91-aa8f-4538451a207d","added_by":"auto","created_at":"2025-12-01 16:07:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3432632,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7332120/v1/235af331-4ce4-4cee-9d39-3ea02739955a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical Value of Dual-Energy CT Parameters Combined with Morphological Features in Predicting Graves’ Ophthalmopathy","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eGraves' orbitopathy (GO) is an autoimmune disorder closely associated with thyroid dysfunction and presents with a complex and diverse spectrum of clinical manifestations. It can affect one or both eyes, with symptoms such as eyelid retraction, proptosis, diplopia, restrictive strabismus, exposure keratopathy, and dysthyroid optic neuropathy. These symptoms significantly impact patients\u0026rsquo; quality of life[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Given the heterogeneous nature of clinical presentations of GO, precise diagnosis plays a pivotal role in the formulation of appropriate treatment strategies.\u003c/p\u003e\u003cp\u003eAt present, the management of GO throughout its course relies primarily on a comprehensive evaluation integrating clinical activity score(CAS) and imaging assessments. However, the CAS may be influenced by subjective factors, especially in activity assessment and treatment response, which can lead to variability in clinical decision-making. This underscores the need for objective, reproducible imaging-based parameters to support GO diagnosis and management. Among available imaging modalities, magnetic resonance imaging (MRI) is one of the most widely used tools due to its excellent soft tissue contrast and multiparametric imaging capacities. Contrast-enhanced MRI enables detailed staging of GO and comprehensively evaluate the response of hormonal therapy, as evidenced by previous studies [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, this application also has certain limitations, including limited availability, long scheduling times, contrast agent allergy, and contraindications in certain patients populations. In addition to MRI, single-photon emission computed tomography/X-ray computed tomography (SPECT/CT) is another valuable imaging modality for accessing disease activity and stage[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, owing to its high cost and significant radiation exposure limited its routine clinical application. Conventional CT is commonly used as a supplementary tool evaluating orbital muscle hypertrophy. Despite its widespread use, conventional CT lacks the capability for detailed quantitative analysis of microstructure changes in orbital tissue.\u003c/p\u003e\u003cp\u003eDual-energy computed tomography (DECT) has the ability to reflect the characteristics of tissues and lesions, thereby differentiating normal tissues from lesions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], benign from malignant tumors [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and various pathological subtypes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Previous studies have demonstrated that DECT-derived quantitative parameters, such as iodine density, spectral attenuation slope, effective atomic number (Z\u003csub\u003eeff\u003c/sub\u003e), and electron density (ED), can differentiate benign and malignant ocular tumors [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and discriminate orbital lymphoma from other orbital lymphoproliferative diseases. However, research applying DECT in the context of GO remains limited.\u003c/p\u003e\u003cp\u003eTherefore, the aim of this study was to quantitatively access changes in subtle orbital tissue using DECT-derived parameters in combined with morphological features, exploring the diagnostic value of these parameters in GO, and evaluating its feasibility as an imaging biomarker in the management of the entire disease course.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and population\u003c/h2\u003e\u003cp\u003e This retrospective study was approved by the Institutional Review Board. Patients with suspected GO identified through DECT scans between November 2023 and January 2025 were retrospectively included. The inclusion criteria were as follows: (1) orbital DECT examination; (2) no history of thyroid function treatment of other hormonal therapies; and (3) no history of trauma, ocular tumors or any other medical conditions that could potentially affect the observation and measurement of orbital tissues. The diagnostic criteria for GO [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] are as follows: if eyelid retraction is the initial symptom, the diagnosis can be made after excluding other causes and in combination with one of the following three results. (1) At least one of the indicator of thyroid function or thyroid-related antibody [free triiodothyronine, free thyroxine, total triiodothyronine, total thyroxine, thyroid stimulating hormone in serum, thyrotrophin receptor antibody] is abnormal. (2) Proptosis is present, which is defined as a disparity in exophthalmos between the two eyes exceeding 2 mm. (3) The orbital muscles (OM) are involved, manifested as regular thickening in the middle and posterior segments of one or more OMs that do not involve the tendon, as shown by orbital CT or orbital MRI. If the initial symptom is an abnormality in thyroid function or thyroid-related antibodies, the diagnosis can be made after excluding other causes and in combination with one of the following three findings. (1) Eyelide retraction; (2) proptosis; (3) OM involvement. The case screening flowchart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The sinus CT images of 30 patients with sinusitis were selected, and the width and thickness of each extraocular muscle were measured. The measured data were compared with the study of Ruili Li [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Consistent patterns indicated accurate measurements. The reference value for OM thickening was set as the mean\u0026thinsp;+\u0026thinsp;2 standard deviations, as established by previous research [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\n\u003cdiv class=\"Heading\"\u003eDECT Protocol\u003c/div\u003e\u003cp\u003eAll patients underwent DECT scans using a dual-source CT scanner (Somatom Force, Siemens Healthineers). The scan parameters were as follows: dual X-ray tube voltages of 100/150 kV (Sn), the tube currents were 195/150 mAs, the pitch was set at 0.8, the collimation width was 192\u0026times;0.6 mm, and a rotation time of 1 second. The reconstruction parameters included: the slice thickness was 0.75 mm, and the slice interval was 0.75 mm. The matrix was 512\u0026times;512, and the ADMIRE level was set at 3.\u003c/p\u003e\n\u003ch3\u003eImage analysis\u003c/h3\u003e\n\u003cp\u003eAll the images were processed and analyzed using the Siemens CT postprocessing workstation (Simens Syngo.via). Two radiologists independently analyzed the images. All the individuals involved in the observation remaining blinded to the clinical pathological results and the research design. A total of 31 parameters were measured and analyzed in this study, including 3 clinical features, 4 morphological features, and 24 DECT-derived parameters. The clinical features were obtained by consulting the hospital information system. The morphological features and DECT-derived parameters were extracted using Syngo.via workstation.\u003c/p\u003e\u003cp\u003eOn the Syngo.via workstation, oblique coronal images perpendicular to the optic nerve were reconstructed, with the slice thickness of 2 mm. The morphological features of the OM were measured 4.5 mm posterior to the junction of the optic nerve and the ocular wall (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). For the medial and lateral rectus muscles, the vertical diameter was used to represent their width, and the horizontal diameter was used to represent their thickness. For the superior and inferior rectus muscles, the vertical diameter represents their thickness, and the horizontal diameter represents their width. The vertical diameter was measured as the distance between the uppermost and lowermost edges of the muscle belly, and the horizontal diameter was measured as the distance between the innermost and outermost edges of the muscle belly (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Typically, the level 4.5 mm posterior to the eyeball is the plane where the maximum diameter of the orbital muscle belly of healthy individuals is located [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, the thickening of the OM is not necessarily confined to this plane. Therefore, if significant thickening of the OM is detected, the plane with the thickest part of the muscle is selected for measurement. For the widths and thicknesses of the eight OMs on both sides, the bilateral data of each orbital muscle are combined. The larger value of each pair of corresponding OMs is retained, and the combined data are used to calculate the average thickness (AT), weighted average thickness (WAT), average width (AW), and weighted average width (WAW). The calculation method for the WAT is as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{W}\\text{A}\\text{T}=\\frac{\\sum\\:_{\\text{i}=0}^{\\text{n}}{\\text{t}\\text{h}\\text{i}\\text{c}\\text{k}\\text{n}\\text{e}\\text{s}\\text{s}\\:\\text{o}\\text{f}\\:\\text{t}\\text{h}\\text{e}\\:\\text{O}\\text{M}}_{\\text{i}}\\times\\:{\\text{r}\\text{e}\\text{f}\\text{e}\\text{r}\\text{e}\\text{n}\\text{c}\\text{e}\\:\\text{v}\\text{a}\\text{l}\\text{u}\\text{e}\\text{s}\\:\\text{f}\\text{o}\\text{r}\\:\\text{t}\\text{h}\\text{e}\\:\\text{t}\\text{h}\\text{i}\\text{c}\\text{k}\\text{n}\\text{e}\\text{s}\\text{s}\\:\\text{o}\\text{f}\\:\\text{O}\\text{M}}_{\\text{i}}}{\\sum\\:_{\\text{i}=0}^{\\text{n}}{\\text{r}\\text{e}\\text{f}\\text{e}\\text{r}\\text{e}\\text{n}\\text{c}\\text{e}\\:\\text{v}\\text{a}\\text{l}\\text{u}\\text{e}\\text{s}\\:\\text{f}\\text{o}\\text{r}\\:\\text{t}\\text{h}\\text{e}\\:\\text{t}\\text{h}\\text{i}\\text{c}\\text{k}\\text{n}\\text{e}\\text{s}\\text{s}\\:\\text{o}\\text{f}\\:\\text{O}\\text{M}}_{\\text{i}}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTwenty-four DECT-derived parameters, including the maximum, minimum, and average values of Z\u003csub\u003eeff\u003c/sub\u003e and ED of the OM, lacrimal glands (LG), optic nerves (ON), and fat (F), were used. To obtain morphological features, all OMs were manually delineated to cover their areas as completely as possible (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Oblique transverse images parallel to the ON were reconstructed, with the slice thickness of 2 mm. On the oblique transverse image where the LG is most prominently displayed, the bilateral LG is manually delineated to cover their areas as completely as possible (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). On the oblique transverse image where the ON is most prominently shown, the Z\u003csub\u003eeff\u003c/sub\u003e and ED of the bilateral ON and F surrounding the ON were measured. Circular regions of interest with areas of 5 mm\u0026sup2; were delineated at the middle and posterior thirds of the intraorbital segment of the ON and manually delineated F to cover their areas as completely as possible (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe data were statistically analyzed using R (version 4.4.2) software. The intraclass correlation coefficient (ICC) with two-way random effects was used to assess the agreement between two radiologists. Values\u0026thinsp;\u0026ge;\u0026thinsp;0.75 were considered indicative of excellent reliability, and average processing was performed for the two groups of data. Data are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations for normally distributed data and medians (interquartile ranges) for non-normally distributed data. Student\u0026rsquo;s t-test or the Mann\u0026ndash;Whitney U test was used to compare the differences in quantitative parameters between the two groups according to the distribution type of the data.\u003c/p\u003e\u003cp\u003eAll patients were randomly divided into a training dataset and a test dataset in a 1:1 ratio. Univariable logistic regression was applied to the training dataset to screen the relevant features (univariable p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for predicting GO. Backward-stepwise multivariable logistic regression was then used to narrow down the final features from the univariable predictors and construct the predictive nomogram in the training dataset. The receiver operating characteristic (ROC) curve was generated, and the area under the curve (AUC), sensitivity, and specificity were calculated to quantify model performance in the training dataset and test dataset. The AUC, calibration, and decision curve were analyzed for model discrimination, calibration, and clinical application. Comparisons of AUCs were conducted using the DeLong test. The Hosmer\u0026ndash;Lemeshow test was used for calibration analysis, where a p value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 was considered to indicate good model fit and was regarded as statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eGeneral Information and Interobserver Consistency\u003c/h2\u003e\u003cp\u003eThe ICCs for the measurement data between the two radiologists were greater than 0.75.\u003c/p\u003e\u003cp\u003eAs measured from the sinus CT images, the widths (in mm) of the medial rectus muscle, superior rectus muscle, lateral rectus muscle and inferior rectus muscle were 8.930\u0026thinsp;\u0026plusmn;\u0026thinsp;1.069, 7.775\u0026thinsp;\u0026plusmn;\u0026thinsp;1.062, 9.833\u0026thinsp;\u0026plusmn;\u0026thinsp;0.894, and 7.010\u0026thinsp;\u0026plusmn;\u0026thinsp;1.003, respectively, and the thicknesses (in mm) were 3.305\u0026thinsp;\u0026plusmn;\u0026thinsp;0.514, 3.117\u0026thinsp;\u0026plusmn;\u0026thinsp;0.686, 2.995\u0026thinsp;\u0026plusmn;\u0026thinsp;0.603, and 3.658\u0026thinsp;\u0026plusmn;\u0026thinsp;0.692, respectively. These measurements are consistent with the research pattern of Li Ruili [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eComparison of Morphological and DECT Parameters between the GO + and GO − Groups\u003c/h3\u003e\n\u003cp\u003eThere were no significant differences in age or sex between patients in the GO\u0026thinsp;+\u0026thinsp;group and the GO- group (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All morphological features, including AT, AW, WAT and WAW, were significantly greater in the GO\u0026thinsp;+\u0026thinsp;group than in the GO- group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In terms of the DECT parameters, the minimum and average ED values of OM (ED-OM(min), ED-OM(avg)) and LG (ED-LG(min), ED-LG(avg)) were significantly lower in the GO\u0026thinsp;+\u0026thinsp;group than in the GO- group. There were no significant differences in the remaining ED parameters or in any of the Z\u003csub\u003eeff\u003c/sub\u003e parameters between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eClinical characteristics and dual-energy CT (DECT) parameters of patients with and without Graves\u0026rsquo; orbitopathy\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGO+\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;134)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGO-\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;72)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical features\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.22\u0026thinsp;\u0026plusmn;\u0026thinsp;13.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.88\u0026thinsp;\u0026plusmn;\u0026thinsp;17.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.480\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.312\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\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\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47(35.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23(31.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87(64.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49(68.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShape features\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.28(3.79,4.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.29(3.14,3.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.80(8.50,9.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.33(8.08,8.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.15(3.91,4.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.44(3.21,4.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWAW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.84(8.65,9.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.55(8.18,9.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDECT parameters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED-OM(max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43.91\u0026thinsp;\u0026plusmn;\u0026thinsp;3.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.46\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED-OM(avg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.75(28.24,37.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.58(40.07,40.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED-OM(min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.10(32.94,38.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.62(36.29,39.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED-LG(max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.42(50.42,56.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.45(50.98,56.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.461\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED-LG(avg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.10(34.00,47.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.70(47.60,54.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED-LG(min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.40(31.50,46.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.88(44.73,53.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED-ON(max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.20(31.60,42.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.20(29.10,40.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.482\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED-ON(avg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.25\u0026thinsp;\u0026plusmn;\u0026thinsp;8.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.21\u0026thinsp;\u0026plusmn;\u0026thinsp;5.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.575\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED-ON(min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.80(13.60,27.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.20(20.50,27.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.270\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED-F(max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-49.40(-58.10, -39.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-52.00(-57.60, -47.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED-F(avg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-54.34\u0026thinsp;\u0026plusmn;\u0026thinsp;9.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-57.21\u0026thinsp;\u0026plusmn;\u0026thinsp;2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED-F(min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-60.64\u0026thinsp;\u0026plusmn;\u0026thinsp;7.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-59.78\u0026thinsp;\u0026plusmn;\u0026thinsp;2.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.522\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZ\u003csub\u003eeff\u003c/sub\u003e-OM(max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.84(7.71,8.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.83(7.69,8.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.689\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZ\u003csub\u003eeff\u003c/sub\u003e-OM(avg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.646\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZ\u003csub\u003eeff\u003c/sub\u003e-OM(min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.30(7.23,7.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.34(7.22,7.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.629\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZ\u003csub\u003eeff\u003c/sub\u003e-LG(max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.38(7.30,7.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.35(7.25,7.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.308\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZ\u003csub\u003eeff\u003c/sub\u003e-LG(avg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.867\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZ\u003csub\u003eeff\u003c/sub\u003e-LG(min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.10(6.98,7.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.19(7.04,7.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZ\u003csub\u003eeff\u003c/sub\u003e-ON(max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.80(7.64,8.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.71(7.59,7.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZ\u003csub\u003eeff\u003c/sub\u003e-ON(avg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.430\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZ\u003csub\u003eeff\u003c/sub\u003e-ON(min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.28(7.09,7.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.30(7.18,7.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.322\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZ\u003csub\u003eeff\u003c/sub\u003e-F(max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.67(6.56,6.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.59(6.50,6.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.355\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZ\u003csub\u003eeff\u003c/sub\u003e-F(avg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZ\u003csub\u003eeff\u003c/sub\u003e-F(min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.31(6.20,6.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.27(6.21,6.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.587\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eGO+/-\u003c/em\u003e patients with/without Graves\u0026rsquo; orbitopathy, \u003cem\u003eAT\u003c/em\u003e average thickness, \u003cem\u003eAW\u003c/em\u003e average width, \u003cem\u003eWAT\u003c/em\u003e weighted average thickness, \u003cem\u003eWAW\u003c/em\u003e weighted average width, \u003cem\u003eED\u003c/em\u003e electron density, \u003cem\u003eZ\u003c/em\u003e\u003csub\u003e\u003cem\u003eeff\u003c/em\u003e\u003c/sub\u003e effective atomic number, \u003cem\u003eOM\u003c/em\u003e orbital muscle, \u003cem\u003eLG\u003c/em\u003e lacrimal gland, \u003cem\u003eON\u003c/em\u003e optic nerve, \u003cem\u003eF\u003c/em\u003e fat\u003c/p\u003e\n\u003ch3\u003eUnivariate and Multivariate Logistic Regression: Model Development and Evaluation\u003c/h3\u003e\n\u003cp\u003eThe univariable and multivariable logistic regressions were based on these characteristics, which selected the DECT parameters with a p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These highly correlated factors were subsequently analyzed using backward-stepwise multivariable logistic regression to establish a model with p\u0026thinsp;=\u0026thinsp;1.61WAT\u0026thinsp;+\u0026thinsp;0. 79WAW \u0026minus;\u0026thinsp;0.31ED-OM(min)\u0026thinsp;\u0026minus;\u0026thinsp;3.13 based on the training set, which was used to test the diagnostic consistency and efficiency of the test set in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The combined model is presented as a nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The Hosmer\u0026ndash;Lemeshow test yielded p values of 0.631 and 0.364 for the training and test sets, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Decision curve analysis and ROC curve analysis for three highly correlated factors and the model in the training and test sets are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The AUC, cutoff value, sensitivity, specificity, precision, and accuracy of the highest correlation factors and the model for differential diagnosis are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The AUC, sensitivity, specificity, and accuracy of the model in the training and test datasets were 0.812, 92.1%, 59.4%, and 74.5% and 0.825, 82.9%, 71.9%, and 77.6%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Sample cases of the diagnostic use of the nomogram are provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\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\u003eUnivariate and multivariate logistic regression analyses of the characteristics for predicting Graves\u0026rsquo; orbitopathy in the training cohort (n\u0026thinsp;=\u0026thinsp;103)\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=\"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\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUnivariate logistic regression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"9\" rowspan=\"10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eMultivariate logistic regression\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e156.889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.808-4593.773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.780-12.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.720\u0026ndash;4.481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.337\u0026ndash;3.674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWAW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.223\u0026ndash;2.948\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.066\u0026ndash;2.941\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED-OM(avg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.102\u0026ndash;0.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED-OM(min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.691\u0026ndash;0.876\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.711\u0026ndash;0.907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED-LG(avg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.748\u0026ndash;0.909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED-LG(min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.869\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.794\u0026ndash;0.932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\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\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNotably, the results were based on the training cohort.\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 and combined model of DECT for Graves\u0026rsquo; orbitopathy (n\u0026thinsp;=\u0026thinsp;206)\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCutoff\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSpe\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePPV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNPV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePre\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAcc\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraining set(n\u0026thinsp;=\u0026thinsp;103)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.739(0.653\u0026ndash;0.824)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.715\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.873\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.745\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWAW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.660(0.568\u0026ndash;0.752)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.664\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED-OM(min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.687(0.596\u0026ndash;0.779)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.270\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCombined model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.812(0.740\u0026ndash;0.884)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.921\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.659\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.745\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest set(n\u0026thinsp;=\u0026thinsp;103)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.778(0.657\u0026ndash;0.899)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.715\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.806\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWAW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.631(0.494\u0026ndash;0.769)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.687\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED-OM(min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.695(0.561\u0026ndash;0.828)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCombined model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.825(0.724\u0026ndash;0.926)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.776\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eAUC\u003c/em\u003e, area under the curve; \u003cem\u003ecutoff\u003c/em\u003e cutoff value; \u003cem\u003eSen\u003c/em\u003e, sensitivity; \u003cem\u003eSpe\u003c/em\u003e, specificity; \u003cem\u003ePPV\u003c/em\u003e, positive predictive value; \u003cem\u003eNPV\u003c/em\u003e, negative predictive value; \u003cem\u003ePre\u003c/em\u003eprecision; \u003cem\u003eAcc\u003c/em\u003e, accuracy. \u003cem\u003eThe\u003c/em\u003e combined model combines the most powerful DECT-derived parameters (WAT, WAW, and ED-OM(min)) and is based on training set\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study developed a model that combines morphological features and dual-energy CT parameters to predict the risk of Graves' ophthalmopathy. The model demonstrated excellent predictive ability in both the training and test sets, with AUCs of 0.812 and 0.825, respectively, and high diagnostic accuracies of 74.5% and 77.6%, respectively. Through univariate and multivariate logistic regression analyses, the most predictive features-namely, WAW, WAT, and ED-OM (min)-were identified. During the model construction process, these features were shown to have a significant correlation with the risk of GO. These DECT-derived quantitative parameters can not only be directly applied to diagnose GO but also serve as promising novel biomarkers that may enhance the precision and objectivity of clinical assessments.\u003c/p\u003e\u003cp\u003eAll morphological features, including AT, AW, WAT and WAW, were significantly greater in the GO\u0026thinsp;+\u0026thinsp;group than in the GO- group, indicating that the OMs of patients with GO are significantly thicker than those of the normal population. This finding is consistent with the conclusions of Bartalena L. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Univariate logistic regression analysis revealed that these four features were closely related to the risk of GO. After verification by multivariate logistic regression analysis, WAW and WAT were found to be more significant than AW and AT in predicting the risk of GO. This is because WAW and WAT calculation designs incorporate reference values of OM thickness and width for weighted calculation. This fully account for the weights of thicknesses and widths of different OMs overall, keenly capturing the impact of their individual changes, and avoids omitting relatively minor changes.\u003c/p\u003e\u003cp\u003eIn terms of DECT parameters, ED mainly describes the distribution probability of electrons in space [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], while Z\u003csub\u003eeff\u003c/sub\u003e reflects the absorption characteristics of substances to X-rays [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The results of this study revealed significant differences in ED-OM (min), ED-OM (avg), ED-LG (min), and ED-LG (avg) between patients in the GO\u0026thinsp;+\u0026thinsp;and GO- groups. This is because during the development of GO, the autoimmune response affects tissues through lymphocyte infiltration and stimulation of inflammatory factors [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. On one hand, lymphocyte infiltration increases the number of cells in affected tissues. These cells contain numerous biological macromolecules, such as proteins and nucleic acids, and the electron cloud distribution in these molecules is relatively complex and dense [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The stimulation of inflammatory factors leads to edema of muscle tissues, and many causing many water molecules to enter the intercellular spaces [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], which disrupts the originally relatively uniform electron cloud distribution state of the affected tissues and alters ED. On the other hand, the inflammatory response damages the original microstructure of the affected tissues, such as the arrangement of muscle fibers and the tissue structure [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], disrupting the regular distribution of the electron cloud. Although the microstructure and ED of the affected tissues have altered, there is no significant change in the elemental composition, which makes the macroscopic characteristics of the substance's absorption of X-rays relatively stable. Therefore, there are no significant differences in Z\u003csub\u003eeff\u003c/sub\u003e between the GO\u0026thinsp;+\u0026thinsp;group and the GO- group. The multivariate logistic regression results revealed that the most severely affected part in GO patients was the OM, which is consistent with the conclusions of previous research by Schuh A [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Currently, ED and Z\u003csub\u003eeff\u003c/sub\u003e are primarily used in research related to cartilage, ligaments and tumors [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] but rarely for predicting GO. The study by Goo HW indicates that the errors in calculating ED and Z\u003csub\u003eeff\u003c/sub\u003e through DECT are extremely small, i.e., 1.7% and 4.1%, respectively[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and both have high accuracy in evaluating tissue components. ED has the ability to capture changes in the microstructure and chemical composition of tissues. It can directly observe a series of pathological changes in GO, such as lymphocyte infiltration and tissue edema caused by the autoimmune response, making it an effective indicator for predicting GO.\u003c/p\u003e\u003cp\u003eBy comparing the AUCs of single parameters with those of the combined model, it can be concluded that the combined model has more advantages in predicting GO. The results of the Hosmer\u0026ndash;Lemeshow test and DCA both indicate that this model can provide more practical benefits to patients and can be used for subsequent clinical analysis and decision-making. Zhao W constructed a model combining morphological and functional features[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], which also showed good results in predicting the occult metastasis pattern of central cervical lymph nodes in papillary thyroid carcinoma patients. These findings verify that the modeling method adopted in this study has broad applicability and effectiveness and is expected to play an important role in the diagnosis and prediction of more diseases.\u003c/p\u003e\u003cp\u003eNevertheless, the current study has several limitations. First, this study indirectly demonstrates the feasibility of using DECT for disease management, but does not directly compare it with the CAS in activity assessment and treatment response. Additionally, this study was conducted at a single institution, which may limit the generalizability of the findings to different populations and clinical presentations of GO.\u003c/p\u003e\u003cp\u003eThis study provides a preliminary exploration of the utility of DECT quantitative parameters combined with morphological features for diagnosing GO, and validated the feasibility of using DECT as a biomarker for disease management in GO. Future studies are warranted to further investigate the associations of these DECT parameters with disease activity and specific complications, such as dysthyroid optic neuropathy. Such extended analyses could enhance the clinical applicability of DECT imaging, providing deeper insights into disease progression and facilitating more targeted therapeutic interventions.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn conclusion, the parameters derived from DECT (ED-OM(min)) and morphological features (WAW, WAT) collectively serve as robust predictive indicators for diagnosing GO. The DECT parameters have specific relevance for GO.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDECT = energy computed tomography; GO = Graves\u0026apos; orbitopathy; MRI = magnetic resonance imaging; Z\u003csub\u003eeff\u0026nbsp;\u003c/sub\u003e= effective atomic number; ED = electron density; OM = orbital muscles; LG = lacrimal glands; ON = optic nerves; F = fat; AT = average thickness; WAT = weighted average thickness; AW = average width; WAW = weighted average width; AUC = Area under the curve\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics committee of the Beijing Tongren Hospital of Capital Medical University(TRECKY2016-003). All methods were carried out in accordance with relevant guidelines and regulations. Informed consent to participate was obtained from all participants prior to their inclusion in the study\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work has not received any funding.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e \u003c/p\u003e\n\u003cp\u003eZM: conceptualization, data curation, methodology, formal analysis, resources, writing\u0026mdash;original draft, visualization. TS: formal analysis, data curation, writing\u0026mdash;review and editing. WL: methodology, formal analysis. XL: visualization, writing\u0026mdash;review and editing.LY: resources, conceptualization.YZ :resources, software. YN: writing\u0026mdash;review and editing, project administration. DL:writing\u0026mdash;review and editing,conceptualization, data curation. All authors read and approved the final manuscript.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOculoplastic and Orbital Disease Group of Chinese Ophthalmological Society of Chinese Medical Association, Thyroid Group of Chinese Society of Endocrinology of Chinese Medical Association (2022) Chinese guideline on the diagnosis and treatment of thyroid-associated ophthalmopathy. \u003cem\u003eChinese Journal of Ophthalmology\u003c/em\u003e 2022;58(9):646-668\u003c/li\u003e\n\u003cli\u003eSong C, Luo Y, Yu G, Chen H, Shen J, Current insights of applying MRI in Graves\u0026apos; ophthalmopathy. \u003cem\u003eFront Endocrinol \u003c/em\u003e2022;13:991588\u003c/li\u003e\n\u003cli\u003eWu D, Zhu H, Hong S, Li B, Zou M, Ma X, et al. Utility of multiparametric quantitative magnetic resonance imaging of the lacrimal gland for diagnosing and staging Graves\u0026apos; ophthalmopathy. \u003cem\u003eEur J Radiol\u003c/em\u003e 2021;141:109815\u003c/li\u003e\n\u003cli\u003eDas T, Roos JCP, Patterson AJ, Graves MJ, Murthy R. T2-relaxation mapping and fat fraction assessment to objectively quantify clinical activity in thyroid eye disease: an initial feasibility study. \u003cem\u003eEye \u003c/em\u003e2019;33(2):235-243\u003c/li\u003e\n\u003cli\u003eZou M, Wu D, Zhu H, Huang X, Zhao X, Zhao J, et al. Multiparametric quantitative MRI for the evaluation of dysthyroid optic neuropathy. \u003cem\u003eEur Radiol\u003c/em\u003e 2022;32(3):1931-1938\u003c/li\u003e\n\u003cli\u003eMei T, Xue Z,Yang L, He S, Shu Y, Xin Y, et al. The application of \u003csup\u003e99\u003c/sup\u003eTc \u003csup\u003em\u003c/sup\u003e-DTPA orbital SPECT/CT in staging evaluation of thyroid associated ophthalmopathy. \u003cem\u003eChinese Journal of Ophthalmology\u003c/em\u003e 2021;57(11):830-836\u003c/li\u003e\n\u003cli\u003eOchmann A, Winder M, Nalewajka-Kołodziejczak J, Chudek J. Current imaging methods for assessing Graves` orbitopathy activity with particular emphasis on FDG-PET. \u003cem\u003eFront Endocrinol\u003c/em\u003e 2023;14:1138569\u003c/li\u003e\n\u003cli\u003eWang P, Tang Z, Xiao Z, Wu L, Hong R, Duan F, et al. Dual-energy CT in predicting Ki-67 expression in laryngeal squamous cell carcinoma. \u003cem\u003eEur J Radiol\u003c/em\u003e 2021;140:109774\u003c/li\u003e\n\u003cli\u003eKim C, Kim W, Park SJ, Lee YH, Hwang SH, Yong HS, et al. Application of Dual-Energy Spectral Computed Tomography to Thoracic Oncology Imaging. \u003cem\u003eKorean J Radiol\u003c/em\u003e 2020;21(7):838-850\u003c/li\u003e\n\u003cli\u003eLi M, Zheng X, Li J, Yang Y, Lu C, Xu H, et al. Dual-energy computed tomography imaging of thyroid nodule specimens: comparison with pathologic findings. \u003cem\u003eInvest Radiol\u003c/em\u003e 2012;47(1):58-64\u003c/li\u003e\n\u003cli\u003eLuo S, Sha Y, Wu J, Lin N, Pan Y, Zhang F, et al. Differentiation of malignant from benign orbital tumors using dual-energy CT. \u003cem\u003eClin Radiol\u003c/em\u003e 2022;77(4):307-313\u003c/li\u003e\n\u003cli\u003eRui L, Shuang X, Jian W, Feng S, Ji Q. MRI study of the thickness and width of the extraocular muscles in normal subjects. \u003cem\u003eChinese Journal of Ophthalmology\u003c/em\u003e 2015;51(6):434-438\u003c/li\u003e\n\u003cli\u003eKoizumi T, Tanaka T, Umeda K, Komiyama D, Obata H. Correlation between extraocular muscle enlargement and thyroid autoantibodies in thyroid eye disease. \u003cem\u003eJpn J Ophthalmol\u003c/em\u003e 2024;68(3):250-258\u003c/li\u003e\n\u003cli\u003eBartalena L, Kahaly GJ, Baldeschi L, Dayan CM, Eckstein A, Marcocci C, et al. The 2021 European Group on Graves\u0026apos; orbitopathy (EUGOGO) clinical practice guidelines for the medical management of Graves\u0026apos; orbitopathy. \u003cem\u003eEur J Endocrinol\u003c/em\u003e 2021; 27;185(4):G43-G67\u003c/li\u003e\n\u003cli\u003eHua CH, Shapira N, Merchant TE, Klahr P, Yagil Y. Accuracy of electron density, effective atomic number, and iodine concentration determination with a dual-layer dual-energy computed tomography system. \u003cem\u003eMed Phys\u003c/em\u003e 2018;45(6):2486-2497\u003c/li\u003e\n\u003cli\u003eMoledina M, Damato EM, Lee V. The changing landscape of thyroid eye disease: current clinical advances and future outlook. \u003cem\u003eEye\u003c/em\u003e 2024;38(8):1425-1437\u003c/li\u003e\n\u003cli\u003eHai YP, Lee ACH, Frommer L, Diana T, Kahaly GJ. Immunohistochemical analysis of human orbital tissue in Graves\u0026apos; orbitopathy.\u003cem\u003e J Endocrinol Invest\u003c/em\u003e 2020;43(2):123-137\u003c/li\u003e\n\u003cli\u003eLanzolla G, Marin\u0026ograve; M, Menconi F. Graves disease: latest understanding of pathogenesis and treatment options. \u003cem\u003eNat Rev Endocrinol\u003c/em\u003e 2024;20(11):647-660\u003c/li\u003e\n\u003cli\u003eSchuh A, Ayvaz G, Baldeschi L, Baretić M, Bechtold D, Boschi A, et al. Presentation of Graves\u0026apos; orbitopathy within European Group On Graves\u0026apos; Orbitopathy (EUGOGO) centers from 2012 to 2019 (PREGO III). \u003cem\u003eBr J Ophthalmol \u003c/em\u003e2024;29;108(2):294-300\u003c/li\u003e\n\u003cli\u003eRajiah P, Sundaram M, Subhas N. Dual-Energy CT in Musculoskeletal Imaging: What Is the Role Beyond Gout? \u003cem\u003eAJR Am J Roentgenol\u003c/em\u003e 2019;213(3):493-505\u003c/li\u003e\n\u003cli\u003eAgostini A, Borgheresi A, Mari A, Floridi C, Bruno F, Carotti M, et al. Dual-energy CT: theoretical principles and clinical applications. \u003cem\u003eRadiol Med\u003c/em\u003e 2019;124(12):1281-1295\u003c/li\u003e\n\u003cli\u003eGoo HW, Goo JM. Dual-Energy CT: New Horizon in Medical Imaging. \u003cem\u003eKorean J Radiol\u003c/em\u003e 2017;18(4):555-569\u003c/li\u003e\n\u003cli\u003eZhao W, Shen S, Ke T, Jiang J, Wang Y, Xie X, et al. Clinical value of dual-energy CT for predicting occult metastasis in central neck lymph nodes of papillary thyroid carcinoma. \u003cem\u003eEur Radiol\u003c/em\u003e 2024;34(1):16-25\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Graves' ophthalmopathy, Dual-energy CT, Morphological features, Nomogram, Electron density","lastPublishedDoi":"10.21203/rs.3.rs-7332120/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7332120/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eTo investigate the diagnostic value of dual-energy computed tomography (DECT)\u0026ndash;derived quantitative parameters combined with morphological features for assessing subtle orbital tissue changes in Graves\u0026rsquo; orbitopathy (GO), and to evaluate their feasibility as imaging biomarkers throughout the disease course.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eData from patients suspected of having GO were retrospectively collected. All these patients underwent DECT scans and had no history of thyroid function treatment or other medical history, which may have affected the measurement of orbital tissues. Three clinical features, four morphological features, and twenty-four DECT parameters were measured. The overall data were divided into training and test cohorts. Univariate and multivariate analyses were applied to select relevant parameters and construct a nomogram.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong the 206 patients suspected of having GO, 134 patients were diagnosed as positive for GO (GO+), and 72 patients were diagnosed as negative (GO-) according to relevant diagnostic criteria. (1) The average thickness, average width, weighted average thickness and weighted average width of orbital muscles significantly differed between the GO\u0026thinsp;+\u0026thinsp;and GO- groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). (2) The minimum and average values of electron density in orbital muscles and lacrimal glands were significantly different (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). (3) A nomogram was constructed to predict the risk of GO, and the area under the curve, sensitivity, and specificity in the training and test cohorts were 0.812, 92.1%, and 59.5% and 0.825, 82.9%, and 71.9%, respectively.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eDECT parameters combined with morphological features not only can be used as robust predictive indicators for diagnosing GO but also represent promising novel biomarkers that may increase the precision and objectivity of clinical assessments.\u003c/p\u003e","manuscriptTitle":"Clinical Value of Dual-Energy CT Parameters Combined with Morphological Features in Predicting Graves’ Ophthalmopathy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-24 06:19:38","doi":"10.21203/rs.3.rs-7332120/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-16T03:45:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-14T20:00:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-13T19:33:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182675438846236213425770040270449889188","date":"2025-10-11T21:43:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24516098424050295261013972254787559823","date":"2025-10-11T20:55:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-10T21:27:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"71038822693676690225484004445113974377","date":"2025-10-10T21:23:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197594281138791794665106411966677738084","date":"2025-10-10T18:14:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"208606306335893850169685595060503308341","date":"2025-10-10T03:37:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311150261486283043467717588881707545855","date":"2025-10-10T01:38:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175748685127922470074936167280007576138","date":"2025-09-23T04:49:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-15T20:46:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-08T18:48:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-17T17:44:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-15T01:29:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2025-08-15T01:26:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9d5de6c3-bd7c-4b45-9100-49a6173bb489","owner":[],"postedDate":"September 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:00:40+00:00","versionOfRecord":{"articleIdentity":"rs-7332120","link":"https://doi.org/10.1186/s12880-025-02044-x","journal":{"identity":"bmc-medical-imaging","isVorOnly":false,"title":"BMC Medical Imaging"},"publishedOn":"2025-11-28 15:57:20","publishedOnDateReadable":"November 28th, 2025"},"versionCreatedAt":"2025-09-24 06:19:38","video":"","vorDoi":"10.1186/s12880-025-02044-x","vorDoiUrl":"https://doi.org/10.1186/s12880-025-02044-x","workflowStages":[]},"version":"v1","identity":"rs-7332120","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7332120","identity":"rs-7332120","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}