Graves Disease, Graves Ophthalmopathy, Thyroid Associated Ophthalmopathies
Conditions
Keywords
TED
Brief summary
The goal of this observational study is to prospectively validate the efficacy of an AI multimodal model constructed based on multi - sequence orbital MRI in the diagnosis, activity and severity assessment, and prognosis prediction of thyroid - associated ophthalmopathy (TAO) in real - world clinical scenarios. The main questions it aims to answer are: Can AI models accurately assess the presence, activity, and severity of Thyroid-Associated Orbitopathy (TAO)? Can AI models predict the prognosis of TAO? Researchers will compare the diagnostic accuracy of the AI model for TAO patients and healthy subjects to evaluate its diagnostic performance. Participants will undergo a standardized, study - specific multimodal orbital MRI scan (sequences include T1WI, T2WI, STIR, and research sequences such as Magic, IDEAL - IQ, DWI, ASL, CEST). And will systematically acquire ocular ultrasound images from TED patients (active and inactive stages), non-TED ophthalmic disease controls, and healthy volunteers. AI-driven deep learning techniques (convolutional neural networks) will be applied to achieve automatic segmentation of key structures (extraocular muscles, optic nerve, lacrimal gland, and retrobulbar soft tissue). High-throughput radiomic features encompassing morphological parameters and gray-level texture patterns will be extracted. Machine learning algorithms will then be employed to construct objective prediction models for TED screening and activity staging, with MRI findings and CAS scores serving as the reference standards for external validation.
Interventions
DIAGNOSTIC_TESTMRI scan，ocular ultrasound
Participants will undergo a standardized, study - specific multimodal orbital MRI scan (sequences include T1WI, T2WI, STIR, and research sequences such as Magic, IDEAL - IQ, DWI, ASL, CEST).
Sponsors
Shanghai Changzheng Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 75 Years
Healthy volunteers
Yes
Inclusion criteria
* TAO patient group * Age between 18 and 75 years old, regardless of gender. * Patients with thyroid eye disease who meet the disease staging and grading criteria specified in diagnostic guidelines such as those of Bartley, CAS, and EUGOGO. * The patient has planned or received thyroid-related treatment, and the condition is relatively stable. * Voluntarily participate in this study and sign the informed consent form. Control group: ① Healthy volunteers matched with the TAO group in terms of age and gender. ② Voluntarily participate in this study and sign the informed consent form.
Exclusion criteria
* TAO patient group: * Pregnant or lactating women. * Patients with other orbital lesions. ③ Having contraindications for MRI examination (such as non - compatible metal implants in the body, claustrophobia, etc.). ④ The patient has previously received orbital radiotherapy or orbital surgery (except for patients who need to collect tissue specimens). ⑤ Comorbid with other severe systemic diseases (such as uncontrolled heart failure, liver and kidney failure) or mental illnesses, and unable to cooperate to complete the study. ⑥ Participated in other clinical trials that may interfere with the results of this study within 3 months. Control group: Same as the
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic accuracy (AUC) of the AI model for TED | From enrollment to the end of follow-up at 18 months | using comprehensive clinical diagnosis (Bartley criteria) as the gold standard. |
| Classification accuracy of the AI model for distinguishing active versus inactive TED | From enrollment to the end of follow-up at 18 months | — |
| Classification accuracy of the AI model for distinguishing mild versus moderate to severe TED | From enrollment to the end of follow-up at 18 months | — |
Secondary
| Measure | Time frame |
|---|---|
| Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the AI model. | From enrollment to the end of follow-up at 18 months |
| Inter-observer agreement (Kappa coefficient) between the AI model and clinical experts. | From enrollment to the end of follow-up at 18 months |
| Correlation (Pearson/Spearman correlation) between imaging features and pathological indicators . | From enrollment to the end of follow-up at 18 months |
| Predictive performance of the AI model for treatment response, assessed by comparing baseline and follow-up ultrasound data. | From enrollment to the end of follow-up at 18 months |
Countries
China
Contacts
CONTACTTuo Li, Vice Professor
Outcome results
None listed