Evidence map›Paper›PMID 41039313›Full record

ArticleBMC ophthalmology2025

A novel nomogram based on orbital MRI parameters for early prediction of dysthyroid optic neuropathy in thyroid-associated ophthalmopathy.

Xianzhi Zhao, Yongran Li, Yanling Lu, Miaozhi Liu, Zeyi Li

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Article in BMC ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Xianzhi Zhao *Joint Shantou International Eye Center of Shantou University, The Chinese University of HongKong, Shantou, 515000, China.
Yongran Li *Joint Shantou International Eye Center of Shantou University, The Chinese University of HongKong, Shantou, 515000, China.
Yanling LuJoint Shantou International Eye Center of Shantou University, The Chinese University of HongKong, Shantou, 515000, China.
Miaozhi LiuDepartment of Radiology, Second Affiliated Hospital of Shantou University Medical College, Shantou, 515000, China.
Zeyi LiJoint Shantou International Eye Center of Shantou University, The Chinese University of HongKong, Shantou, 515000, China. lizeyi@jsiec.org.

Funding

Shantou Science and Technology Project 200629155261470the Key Research Platforms and Projects of Guangdong Provincial Universities 2023KCXTD013
6 · The paper itself

Abstract

purposeThis study aimed to assess orbital magnetic resonance imaging (MRI) differences across various severity levels of thyroid-associated ophthalmopathy (TAO) and compare them to healthy controls. Additionally, it investigated the correlation and diagnostic potential of multiple MRI parameters for dysthyroid optic neuropathy (DON).

methodsPatients with TAO were classified according to the EUGOGO severity scale into mild TAO (66 orbits), moderate-to-severe TAO (59 orbits), and DON (21 orbits). A control group of 25 healthy orbits was also included. Patients underwent comprehensive ophthalmic and orbital MRI examinations. Generalized estimating equation (GEE) was used to analyze the clinical and MRI data, comparing differences across groups. Univariate and multivariate binary logistic regression identified independent risk factors for DON. The predictive value of MRI parameters for DON was further evaluated using a nomogram, receiver operating characteristic curve (ROC), and decision curve analysis (DCA).

resultsThe extraocular muscle area significantly differed between the control and mild TAO groups (P < 0.05), as well as between both groups and the DON group (P < 0.05). Significant variations in angle between the medial rectus and optic nerve (AMR-ON) and angle between the lateral rectus and optic nerve (ALR-ON) were observed across the control, mild TAO, and moderate-to-severe TAO groups compared to the DON group (P < 0.05). Regression analysis revealed superior rectus muscle area (SRA) and AMR-ON as independent predictors of DON (P < 0.05). The SRA + AMR-ON nomogram demonstrated high predictive performance for DON, with an AUC of 0.906 (P < 0.001), sensitivity of 90.5%, and specificity of 82.4%. DCA further confirmed that the SRA + AMR-ON model outperformed Muscle Index (MI) and Crowding Index (CI) in predictive efficacy for DON.

conclusionsA reduction in AMR-ON and ALR-ON correlates with increased TAO severity, while an enlargement in extraocular muscle area, MI, and CI reflects worsening TAO. SRA and AMR-ON serve as independent predictors for DON, and the predictive model combining these parameters is highly effective in forecasting DON onset.

Indexed as

Graves OphthalmopathyMagnetic Resonance ImagingNomogramsOptic Nerve DiseasesOrbitAdultFemaleHumansMaleMiddle AgedOculomotor MusclesPredictive Value of TestsRetrospective StudiesROC CurveSeverity of Illness IndexDysthyroid optic neuropathyExtraocular musclesMagnetic resonance imagingOrbitThyroid-associated ophthalmopathy

Identifiers

PMID41039313
PMCPMC12490157

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.