Quantitative analysis of orbital soft tissue for the detection of dysthyroid optic neuropathy in patients with thyroid-associated ophthalmopathy, based on three-dimensional cube fast spin-echo Flex

Introduction

Thyroid-associated ophthalmopathy (TAO) is an inflammatory autoimmune disease that can cause significant visual impairment (1,2). Dysthyroid optic neuropathy (DON) is the most severe complication associated with TAO, occurring in approximately 3–7% of these patients (3). The exact mechanisms underlying the development of DON remain unclear, although it is believed that compression injury to the optic nerve caused by orbital tip compression may be the primary cause (4,5). Without prompt intervention, DON can result in permanent vision loss. Therefore, early identification and appropriate treatment are crucial for reversing visual impairment. The primary etiology of orbital apical squeeze is attributed to edema and an augmented volume of the orbital soft tissues (6). Consequently, a quantitative evaluation of edema and volumetric alterations in the orbital soft tissues could potentially enhance our understanding of the pathogenesis and facilitate early detection of DON.

The precision of volumetric measurements is contingent upon accurately discerning the signal discrepancy between the region of interest (ROI) and its adjacent tissue. Magnetic resonance imaging (MRI) exhibits excellent differentiation of soft tissue and is well-suited for assessing orbital lesions (7,8). The three-dimensional (3D) Cube fast spin-echo (FSE)-Flex sequence represents an innovative MRI technique, employing a single-layer 3D water-fat separation sequence, which yields isotropic images that are particularly conducive to quantifying orbital soft tissue volume (9). Moreover, the implementation of the Cube FSE-Flex sequence’s water-fat separation technique guarantees precise demarcation of ROIs during semi-automatic segmentation (9). This is achieved through the provision of diverse contrast data sets encompassing Inphase, Outphase, Water phase, and Fat phase. Additionally, the inclusion of water and fat phase images enables direct image-based quantification of water content, thereby facilitating the evaluation of inflammatory edema within the orbital soft tissues. Consequently, this sequence emerges as a reliable approach for evaluating both structural and functional alterations in the orbital soft tissues (10).

Current DON diagnosis primarily relies on clinical signs (e.g., visual acuity decline, color vision defects) and structural optic nerve changes on imaging. However, these manifestations often occur late in the disease course, when irreversible nerve damage may already be established (11). Conventional orbital imaging (CT/MRI) focuses on anatomical crowding at the orbital apex, but lacks standardized quantitative metrics to objectively evaluate early edema and volumetric changes in orbital soft tissues—key drivers of optic nerve compression (12). This creates a critical diagnostic gap where early intervention opportunities may be missed. The 3D Cube FSE-Flex sequence addresses these limitations by enabling precise quantification of tissue volume and water fraction (WF), providing objective biomarkers of inflammatory edema before irreversible visual loss occurs.

The objective of this study was to examine alterations in orbital soft tissue volume and WF in patients with TAO, with and without DON, utilizing the 3D CUBE FSE-Flex technique. The purpose was to ascertain whether changes in tissue WF and volume of orbital soft tissue could serve as a potential biomarker for DON. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-935/rc).

Methods

Study subjects

This retrospective cross-sectional study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Tongji Hospital (No. ChiECRCT-20170087), and informed consent was waived due to the retrospective nature of the study.

A total of 27 patients diagnosed with DON and 33 age and sex matched individuals with TAO without DON were recruited from the Department of Endocrinology at Tongji Hospital, Huazhong University of Science and Technology (Wuhan, China) between August 2018 and March 2019. The diagnosis of all patients was made collaboratively by ophthalmologists and endocrinologists possessing intermediate or higher qualifications. The diagnostic criteria employed are as follows: (I) the diagnosis of TAO relies on the diagnostic criteria established by Bartley et al. in 1995 (13); (II) patients who have been diagnosed with TAO may also be diagnosed with DON if they fulfill a minimum of two of the subsequent criteria: Unexplained decline in visual acuity (VA), loss of color perception, visual field impairments, abnormalities in visual evoked potentials, swelling of the optic disc, relative afferent pupillary defects, and imaging indications of orbital apical extrusion.

The exclusion criteria encompassed the following: (I) the presence of other neurological or ophthalmic disorders that could impact visual function, such as cataract, glaucoma, neuritis of ischemic or inflammatory origin, multiple sclerosis, diabetic retinopathy, uncorrected high astigmatism, and myopia; (II) the manifestation of severe corneal exposure symptoms, including diffuse punctate keratopathy, ulcers, abscesses, and white spots; (III) a medical history involving steroid therapy, radiation therapy, or surgical decompression; and (IV) insufficient image quality (defined as: visible motion artifacts affecting >10% of orbital slices; incomplete coverage of orbital apex; severe water-fat separation failures).

Clinical materials

Upon admission, pertinent medical history data were gathered, encompassing sex, age, duration of Graves’ disease, history of radioactive iodine-131 (¹³¹I) treatment, and smoking habits. Two ophthalmologists evaluated the clinical activity scores (CAS) for patients with DON and TAO. Additionally, measurements of thyroid-stimulating hormone (TSH), free triiodothyronine (FT3), free thyroxine (FT4), and thyrotropin receptor antibody (TRAb) levels were documented. The CAS and thyroid function tests were performed within 48 hours before MRI acquisition to ensure temporal correlation. All participants were subjected to a thorough ophthalmic examination encompassing evaluations of VA, color vision, refraction, intraocular pressure, slit lamp microscopy, and assessment of relative afferent pupillary defect.

MR image acquisition and analysis

Orbital MRI was conducted using a 3.0 T MRI scanner (Signa HDxt; GE Healthcare, Chicago, IL, USA) equipped with a standard head coil (8-channel, HD Brain Coil, GE Healthcare). To mitigate the impact of motion artifacts, patients were given instructions to close their eyes and maintain immobility throughout the MRI scan. The resultant images encompass four distinct data sets, namely water-only, fat-only, Inphase, and Outphase images, which were acquired through the utilization of a post-processing algorithm.

The measurements were performed using the GE HDAW v. 4.4 workstation (GE Healthcare), which incorporates image analysis software. Two head and neck radiologists, each possessing more than 5 years of experience, analyzed all orbital data in a blinded manner, without knowledge of the participants’ clinical condition. Initially, the volume of orbital soft tissue was measured. Subsequently, the bony orbital (BO) volume was determined by outlining the boundaries of the bony orbit on the Inphase image, utilizing the technique described by Higashiyama et al. (14). Following this, the complete trajectory of the orbit was delineated on each axial Inphase image to establish the ROI encompassing the entire orbit. Subsequently, the three-dimensional orbit was isolated from the overall orbital structure, and the total volume of the orbit (referred to as Whole Orbit, WO) was computed automatically (14). The boundaries of the entire orbit on the Inphase image were then replicated on the fat-only image. Through the application of a threshold adjustment, solely the adipose tissue within the orbit was retained, enabling the quantification of orbital fat (OF) volume (15). Additional measurements were conducted to determine the volumes of the globe (GO), lacrimal gland, and optic nerve using the manual segmentation method. To obtain the volumes of these structures, manual outlining was performed on reconstructed axial and coronal water-only images. The volume of the extraocular muscles (EOM) was calculated by subtracting the volumes of the eyeball, lacrimal gland, optic nerve, and OF from the total orbital volume, as depicted in Figure 1.

Figure 1 Flow chart of orbital segmentation based on Cube FSE-Flex image. (A) Obtain the WO by manual segmentation; (B) manual segmentation of the entire BO. (C-E) The volume of GO, LG and ON was obtained, respectively. (F) The volume of orbital fat was obtained by filtering the EOM, GO, ON and LG of the fat-only image using MDT method. BO, bony orbit; EOM, extraocular muscle; GO, globe; LG, lacrimal gland; MDT, multi-dimensional threshold; ON, optic nerve; WO, whole orbit.

The process of determining the WF in orbital soft tissue entailed the identification of the cross-sectional area exhibiting the largest dimensions for each EOM in the coronal position of the Water image. The contour of the EOM was delineated and documented as the ROI, subsequently transferred onto the corresponding Inphase image to record the signal intensity (SI). With meticulous attention to anatomical boundaries, ROIs were delineated within the four quadrants of OF at the coronal level of the Water and Inphase images, and the corresponding SI were documented. The WF of the EOMs and OF was determined and computed as the quotient of SI_water to SI_Inphase. The WF analysis utilized anatomically defined ROIs without SI thresholds, as the Cube FSE-Flex sequence’s inherent noise reduction and our manual ROI placement strategy (encompassing full anatomical structures while avoiding edge partial volume effects) ensured measurement reliability.

Statistical analysis

The data were analyzed utilizing IBM SPSS Statistics version 28.0 (IBM, Armonk, NY, USA) and R software (version 4.2.3). The interclass correlation coefficient (ICC) was computed to assess the consistency of measurements between the two neuroradiologists for the MRI parameters. To compare the DON and without-DON groups, chi-square tests, independent samples t-tests, and Mann-Whitney U-tests were employed for categorical, normally distributed, and non-normally distributed continuous data, respectively. A logistic regression analysis was performed on variables with P values less than 0.10 in the intergroup analysis to ascertain the risk factors associated with DON, adjusting for age, sex, thyroid function parameters, and CAS. The diagnostic efficacy of imaging parameters in predicting DON was evaluated through receiver operating characteristic (ROC) curve analysis. Binary logistic regression was employed to jointly analyze the imaging parameters, and the resulting variable probabilities were fitted to the joint ROC curve. The significance level for all statistical tests was set at 0.05, and a two-sided P value less than 0.05 was deemed statistically significant.

Results

Clinical and demographic information

A total of sixty patients diagnosed with TAO were included in the study, with 27 patients in the DON group (16 males, 11 females; mean age 52.1±9.3 years) and 33 patients in the non-DON group (15 males, 18 females; mean age 49.5±7.6 years). A total of 116 orbits were analyzed, with 53 orbits exhibiting DON and 63 orbits without DON. The baseline characteristics of the patients are presented in Table 1. Statistical analysis revealed no significant differences between the two groups in terms of age, gender, thyroid function status, or duration of disease (all P>0.05).

Group differences in orbit soft tissue parameters

Compared to the group without DON, patients with DON demonstrated significantly higher WO volume (WOV) (42.7±4.6 vs. 40.6±3.9 cm³, P=0.010), EOM volume (EOMV) (14.6±3.1 vs. 11.5±2.2 cm³, P<0.001), EOM water fraction (EOM-WF) (85.7%±5.0% vs. 80.6%±6.8%, P<0.001), and OF water fraction (OF-WF) (25.0%±4.8% vs. 20.9%±3.6%, P<0.001). No statistically significant differences were observed between the two groups in terms of BO, GO, optic nerve (ON), or OF volume (all P>0.05) (Table 2, Figure 2A,2B).

Figure 2 Quantitative MRI parameters for distinguishing DON in patients with TAO. Violin plots comparing the distribution of orbital soft tissue volumes (A) and water fractions (B) between patients with DON and those with TAO without DON. ROC curves illustrating the diagnostic performance of EOMV and OF-WF in distinguishing patients with DON from those without (C). ns, P>0.05; *, P<0.05; ****, P<0.0001. BO, bony orbit; DON, dysthyroid optic neuropathy; EOM-WF, water fraction of extraocular muscles; EOMV, extraocular muscle volume; MRI, magnetic resonance imaging; OF-WF, water fraction of orbital fat; ROC, receiver operating characteristic; TAO, thyroid-associated ophthalmopathy; WO, whole orbit.

Diagnostic accuracy of quantitative MRI parameters

Multifactorial logistic analysis showed that EOMV and OF-WF were independent predictive parameters of DON (P<0.001, P=0.002, respectively). Increased EOMV [odds ratio (OR): 1.555; 95% confidence interval (CI): 1.250, 1.934], elevated OF-WF (OR: 1.190; 95% CI: 1.064, 1.332) were associated with greater probability of DON (Table 3). The ROC analysis revealed that EOMV exhibited the highest level of diagnostic accuracy in distinguishing patients with DON from those without DON [area under the curve (AUC) =0.804; 95% CI: 0.727, 0.881]. Additionally, OF-WF demonstrated commendable diagnostic performance (AUC =0.776; 95% CI: 0.691, 0.860). Notably, a logistic regression model that integrated EOMV and OF-WF yielded enhanced diagnostic accuracy (AUC =0.843, 95% CI: 0.775, 0.912) (Table 4, Figure 2C).

Correlation of quantitative MRI parameters and clinical variables

Significant positive correlations were observed between EOMV and CAS (r=0.333, P=0.015), as well as between OF-WF and CAS (r=0.308, P=0.025) (Figure 3).

Figure 3 The correlation analysis of MRI parameters and clinical variables. CAS, clinical activity scores; EOM-WF, water fraction of extraocular muscles; EOMV, extraocular muscle volume; MRI, magnetic resonance imaging; OF-WF, water fraction of orbital fat; VA, visual acuity.

Discussion

In this study, 3D Cube FSE-Flex MRI sequences were utilized to evaluate the changes in orbital soft tissue volume and aqueous fraction in patients with DON. The results showed that the WO, EOMV, EOM-WF, and OF-WF were significantly higher in patients with DON compared with those with TAO alone. Multifactorial logistic regression analysis further confirmed that EOMV and OF-WF were independent risk factors for optic neuropathy.

The increased EOMV observed in DON patients likely reflects inflammatory edema, a finding consistent with previous studies (16-18). The positive correlation between EOMV and CAS supports the hypothesis that muscle swelling plays a central role in the pathophysiology of DON. This swelling may contribute to increased orbital pressure, which can compromise optic nerve function through direct compression or by impairing vascular supply (4,19). The elevated EOM-WF further corroborates the presence of edema, suggesting that the degree of water accumulation within the muscles could serve as a marker of disease activity and severity.

Similarly, the elevated OF-WF in DON patients suggests that OF tissue is also involved in the inflammatory process. Adipose tissue is known to be highly susceptible to inflammatory stimuli, which can lead to edema and increased water content (20,21). The edema of the EOMs and fat tissue may exacerbate the compression of the optic nerve at the orbital apex, thereby increasing the risk of DON. The significant correlation between OF-WF and CAS underscores the potential role of OF in the disease process and highlights the importance of comprehensive orbital assessment in patients with TAO.

The diagnostic efficacy of EOMV and OF-WF as independent predictors of DON, as demonstrated by ROC analysis, underscores their potential as biomarkers for early detection and risk stratification. The combination of these two parameters further enhances diagnostic performance, suggesting that they provide complementary information about different aspects of orbital pathology. This finding is of particular clinical significance, as early identification of high-risk patients could facilitate timely intervention and potentially prevent the progression of DON (22).

Recent advances have identified multiple potential MRI biomarkers for DON. Apical crowding indices demonstrate 64.6% sensitivity in multi-ethnic cohorts, while quantitative apparent diffusion coefficient measurements show superior diagnostic performance (AUC =0.844) (23). Our focus on volumetric and WF analysis complements these approaches by: (I) providing quantitative edema measurement through Dixon-based water-fat separation, and (II) enabling 3D assessment of structural remodeling beyond single-slice apical measurements. While we did not acquire diffusion-weighted imaging sequences for ADC analysis due to protocol standardization priorities, future studies combining volumetric, diffusion, and apical crowding parameters could yield a comprehensive biomarker panel.

Despite the promising results, this study has several limitations that should be acknowledged. First, the retrospective design may introduce selection bias. Second, the relatively small sample size, while sufficient for primary analyses, warrants validation in larger cohorts. Third, the cross-sectional nature precludes causal inferences, necessitating longitudinal studies to evaluate these parameters as predictive biomarkers. Fourth, the lack of ethnic diversity (predominantly Han Chinese) may limit generalizability to other populations. Fifth, the semi-automated segmentation method, while effective, required substantial time investment, highlighting the need for more efficient automated techniques. Finally, incomplete smoking status documentation prevented its inclusion in multivariate analyses.

Conclusions

In conclusion, this study highlights the role of orbital soft tissues in the pathogenesis of DON, with increased EOMV and OF-WF showing strong associations with DON and reflecting the characteristic pathological changes of optic nerve compression. These quantitative MRI parameters demonstrate potential as investigational biomarkers for DON detection that warrant validation in prospective studies before clinical application. Future research should focus on validating these findings in larger, prospective cohorts and exploring the underlying mechanisms linking orbital tissue changes to optic neuropathy. Further research should explore optimal imaging protocols to maximize the reproducibility of these quantitative MRI measurements.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-935/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-935/dss

Funding: This study was funded by the National Natural Science Foundation of China (No. 82471967).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-935/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Tongji Hospital (No. ChiECRCT-20170087), and informed consent was waived due to the retrospective nature of the study.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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