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Computed tomography and magnetic resonance imaging approaches to Graves' ophthalmopathy: a narrative review.

ABSTRACT: Graves' ophthalmopathy (GO) affects up to 50% of patients with Graves' disease (GD) ranging from mild ocular irritation to vision loss. The initial diagnosis is based on clinical findings and laboratory tests. Orbital imaging, such as magnetic resonance imaging (MRI) and computed tomography (CT), is an important tool to assess orbital changes, being also useful for understanding disease progression and surgical planning. In this narrative review, we included 92 studies published from 1979 to 2020 that used either MRI and/or CT to diagnose and investigate GO, proposing new methods and techniques. Most of the methods used still need to be corroborated and validated, and, despite the different methods and approaches for thyroid eye disease (TED) evaluation, there is still a lack of standardization of measurements and outcome reports; therefore, additional studies should be performed to include these methods in clinical practice, facilitating the diagnosis and approach for the treatment of TED.

SUBMITTER: Luccas R

PROVIDER: S-EPMC10801040 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Computed tomography and magnetic resonance imaging approaches to Graves' ophthalmopathy: a narrative review.

Luccas Rafael R Riguetto Cinthia Minatel CM Alves Monica M Zantut-Wittmann Denise Engelbrecht DE Reis Fabiano F

Frontiers in endocrinology 20240108

Graves' ophthalmopathy (GO) affects up to 50% of patients with Graves' disease (GD) ranging from mild ocular irritation to vision loss. The initial diagnosis is based on clinical findings and laboratory tests. Orbital imaging, such as magnetic resonance imaging (MRI) and computed tomography (CT), is an important tool to assess orbital changes, being also useful for understanding disease progression and surgical planning. In this narrative review, we included 92 studies published from 1979 to 202 ...[more]

PMID: 38260158

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Project description:BackgroundThe effect of diagnosing Graves' ophthalmopathy (GO) through traditional measurement and observation in medical imaging is not ideal. This study aimed to develop and validate deep learning (DL) models that could be applied to the diagnosis of GO based on magnetic resonance imaging (MRI) and compare them to traditional measurement and judgment of radiologists.MethodsA total of 199 clinically verified consecutive GO patients and 145 normal controls undergoing MRI were retrospectively recruited, of whom 240 were randomly assigned to the training group and 104 to the validation group. Areas of superior, inferior, medial, and lateral rectus muscles and all rectus muscles on coronal planes were calculated respectively. Logistic regression models based on areas of extraocular muscles were built to diagnose GO. The DL models named ResNet101 and Swin Transformer with T1-weighted MRI without contrast as input were used to diagnose GO and the results were compared to the radiologist's diagnosis only relying on MRI T1-weighted scans.ResultsAreas on the coronal plane of each muscle in the GO group were significantly greater than those in the normal group. In the validation group, the areas under the curve (AUCs) of logistic regression models by superior, inferior, medial, and lateral rectus muscles and all muscles were 0.897 [95% confidence interval (CI): 0.833-0.949], 0.705 (95% CI: 0.598-0.804), 0.799 (95% CI: 0.712-0.876), 0.681 (95% CI: 0.567-0.776), and 0.905 (95% CI: 0.843-0.955). ResNet101 and Swin Transformer achieved AUCs of 0.986 (95% CI: 0.977-0.994) and 0.936 (95% CI: 0.912-0.957), respectively. The accuracy, sensitivity, and specificity of ResNet101 were 0.933, 0.979, and 0.869, respectively. The accuracy, sensitivity, and specificity of Swin Transformer were 0.851, 0.817, and 0.898, respectively. The ResNet101 model yielded higher AUC than models of all muscles and radiologists (0.986 vs. 0.905, 0.818; P<0.001).ConclusionsThe DL models based on MRI T1-weighted scans could accurately diagnose GO, and the application of DL systems in MRI may improve radiologists' performance in diagnosing GO and early detection.

Project description:The aim of this study was to analyze the computed tomography (CT) and magnetic resonance imaging (MRI) findings of dermatofibrosarcoma protuberans (DFSP), with a view to improving the diagnosis of this kind of tumor. A total of 27 cases of histopathologically confirmed DFSP were analyzed retrospectively. Of these, 18 patients underwent a CT scan and 9 patients underwent an MRI. All patients underwent unenhanced and contrast-enhanced examinations; 1 patient underwent multiphrase CT enhancement examination. Imaging characteristics, including location, shape, size, number, edge, and attenuation or intensity of each lesion, both unenhanced and contrast enhanced, were analyzed. Of the 27 cases, 24 were solitary, 2 had 2 nodules, and 1 had multiple confluent tumors. The lesion with multiple confluent tumors was ill defined and irregular; the other lesions were oval or round, well-defined nodules or masses. The unenhanced CT images showed 19 homogenous isodense lesions. There was no calcification in any of the patients. The contrast-enhanced CT images showed intermediate and marked nonhomogeneous enhancement in 13 lesions, intermediate homogeneous enhancement in 4 lesions, and a mild heterogeneous enhancement in 2 lesions. MR T1-weighted images revealed 1 ill-defined and 9 well-defined homogeneous isointense lesions. T2-weighted images showed homogeneous hyperintensity to the muscles in 6 lesions, 3 mild hyperintense lesions with hypointense lesions, and 1 mixed, mild hyperintense and isointense lesion. Contrast-enhanced T1-weighted images demonstrated intermediate and marked nonhomogeneous enhancement in 9 lesions and intermediate homogeneous enhancement in 1 lesion. DFSP is characterized by a subcutaneous well-defined soft tissue nodule or mass on plain CT/MR scans, and shows intermediate-to-marked enhancement on contrast-enhanced CT/MR scans. The imaging findings for DFSP are nonspecific, but may help to define the diagnosis in an appropriate clinical setting.

Dataset Information

Computed tomography and magnetic resonance imaging approaches to Graves' ophthalmopathy: a narrative review.

Publications

Computed tomography and magnetic resonance imaging approaches to Graves' ophthalmopathy: a narrative review.

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