The combination of T2-mapping value of lacrimal gland and clinical indicators can improve the stage prediction of Graves’ ophthalmopathy compared to clinical activity scores

Song, Xuefei; Zhang, Haiyang; Tao, Xiaofeng; Yang, Gongxin; Wang, Yishi; Liu, Yuting; Zhou, Huifang; Sun, Jing; Jiang, Mengda

doi:10.1007/s12020-022-03167-9

Cited by 7 publications

(4 citation statements)

References 29 publications

(23 reference statements)

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“…Another study explored the value of lacrimal gland MRI combined with clinical indicators in the stage prediction of TAO. 15 The results showed the combination of the T2-mapping value of the lacrimal gland and clinical indicators could improve the stage prediction of TAO compared with Clinical Active Score (AUC = 0.928). Ge et al 16 also evaluated the efficacy of short tau inversion recovery sequences from MRI in quantitatively staging TAO.…”

Section: Discussionmentioning

confidence: 92%

“…The built-in models preprocessed the facial images and diagnosed multiple TAO signs, with an average AUC exceeding 0.85 and an F1 score higher than 0.80. Another study explored the value of lacrimal gland MRI combined with clinical indicators in the stage prediction of TAO 15 . The results showed the combination of the T2-mapping value of the lacrimal gland and clinical indicators could improve the stage prediction of TAO compared with Clinical Active Score (AUC=0.928).…”

Section: Discussionmentioning

confidence: 99%

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Retinal Photograph-based Deep Learning System for Detection of Thyroid-Associated Ophthalmopathy

Jiang,

Dong,

Luo

et al. 2023

Journal of Craniofacial Surgery

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Background: The diagnosis of thyroid-associated ophthalmopathy (TAO) usually requires a comprehensive examination, including clinical symptoms, radiological examinations, and blood tests. Therefore, cost-effective and noninvasive methods for the detection of TAO are needed. This study aimed to establish a deep learning-based system to detect TAO based on retinal photographs. Materials and methods: The multicenter observational study included retinal photographs taken from TAO patients and normal participants in 2 hospitals in China. Forty-five-degree retinal photographs, centered on the midpoint between the optic disc and the macula, were captured by trained ophthalmologists. The authors first trained a convolutional neural network model to identify TAO using data collected from one hospital. After internal validation, the model was further evaluated in another hospital as an external validation data set. Results: The study included 1182 retinal photographs of 708 participants for model development, and 365 retinal photographs (189 participants) were obtained as the external validation data set. In the internal validation, the area under the receiver operator curve was 0.900 (95% CI: 0.889–0.910) and the accuracy was 0.860 (95% CI: 0.849–0.869). In the external data set, the model reached an area under the curve of 0.747 (95% CI: 0.728–0.763) and achieved an accuracy of 0.709 (95% CI: 0.690–0.724). Conclusions: Deep learning-based systems may be promising for identifying TAO in normal subjects using retinal fundus photographs. It may serve as a cost-effective and noninvasive method to detect TAO in the future.

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Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Retinal Photograph-based Deep Learning System for Detection of Thyroid-Associated Ophthalmopathy

Jiang,

Dong,

Luo

et al. 2023

Journal of Craniofacial Surgery

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“… 47 MRI has also been used to evaluate activity, as MRI results differ during various stages of TAO. 48 TAO has also been accurately classified using machine learning methods. 49 All of these emerging technologies, however, are expensive, especially in their use of antibodies, and their findings do not correlate directly with treatment.…”

Section: Discussionmentioning

confidence: 99%

A TSHR-Targeting Aptamer in Monocytes Correlating with Clinical Activity in TAO

Cao,

Zhang,

Chen

et al. 2024

IJN

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Background Manifestations of thyroid-associated ophthalmopathy (TAO) vary greatly. Few tools and indicators are available to assess TAO, restricting personalized diagnosis and treatment. Aim To identify an aptamer targeting thyroid-stimulating hormone receptor (TSHR) and utilize this aptamer to evaluate clinical activity in patients with TAO. Methods An aptamer targeting TSHR was developed by exponential enrichment and systematic evaluation of TSHR ligands. After truncation and optimization, the affinity, equilibrium dissociation constant, and serum stability of this aptamer were evaluated. The affinity of the TSHR-targeting aptamer to isolated fibrocytes was assessed, as was aptamer internalization by fibrocytes. The mechanism of binding was determined by molecular docking. The correlation between disease manifestations and the percentage of TSHR-positive cells was assessed by correlation analysis. Results The aptamer TSHR-21-42 was developed to bind to TSHR, with the equilibrium dissociation constant being 71.46 Kd. Isolated fibrocytes were shown to bind TSHR-21-42 through TSHR, with its affinity maintained at various temperatures and ion concentrations. TSHR-21-42 could compete with anti-TSHR antibody, both for binding site to TSHR and uptake by cells after binding. In addition, TSHR-21-42 could bind to leukocytes in peripheral blood, with this binding differing in patients with TAO and healthy control subjects. The percentage of TSHR-positive monocytes, as determined by binding of TSHR-21-42, correlated positively with clinical activity score in patients with TAO, indicating that TSHR-21-42 binding could assess the severity of TAO. Conclusion This aptamer targeting TSHR may be used to objectively assess disease activity in patients with TAO, by evaluating the percentages of TSHR positive cells in peripheral blood.

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“…However, the potential of radiomics for TED therapy response prediction could be further enhanced. Despite EOMs, other vital structures in the orbit, such as lacrimal gland (LG) [ 19 ], orbital fat (OF) [ 20 ], and optic nerve (ON) [ 21 ], also undergo distinct changes in the pathogenesis of TED. The predictive value of these structures has been confirmed in several imaging studies [ 22 – 24 ].…”

Section: Introductionmentioning

confidence: 99%

Whole-orbit radiomics: machine learning-based multi- and fused- region radiomics signatures for intravenous glucocorticoid response prediction in thyroid eye disease

Zhang,

Jiang,

Chan

et al. 2024

J Transl Med

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Background Radiomics analysis of orbital magnetic resonance imaging (MRI) shows preliminary potential for intravenous glucocorticoid (IVGC) response prediction of thyroid eye disease (TED). The current region of interest segmentation contains only a single organ as extraocular muscles (EOMs). It would be of great value to consider all orbital soft tissues and construct a better prediction model. Methods In this retrospective study, we enrolled 127 patients with TED that received 4·5 g IVGC therapy and had complete follow-up examinations. Pre-treatment orbital T2-weighted imaging (T2WI) was acquired for all subjects. Using multi-organ segmentation (MOS) strategy, we contoured the EOMs, lacrimal gland (LG), orbital fat (OF), and optic nerve (ON), respectively. By fused-organ segmentation (FOS), we contoured the aforementioned structures as a cohesive unit. Whole-orbit radiomics (WOR) models consisting of a multi-regional radiomics (MRR) model and a fused-regional radiomics (FRR) model were further constructed using six machine learning (ML) algorithms. Results The support vector machine (SVM) classifier had the best performance on the MRR model (AUC = 0·961). The MRR model outperformed the single-regional radiomics (SRR) models (highest AUC = 0·766, XGBoost on EOMs, or LR on OF) and conventional semiquantitative imaging model (highest AUC = 0·760, NaiveBayes). The application of different ML algorithms for the comparison between the MRR model and the FRR model (highest AUC = 0·916, LR) led to different conclusions. Conclusions The WOR models achieved a satisfactory result in IVGC response prediction of TED. It would be beneficial to include more orbital structures and implement ML algorithms while constructing radiomics models. The selection of separate or overall segmentation of orbital soft tissues has not yet attained its final optimal result.

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The combination of T2-mapping value of lacrimal gland and clinical indicators can improve the stage prediction of Graves’ ophthalmopathy compared to clinical activity scores

Cited by 7 publications

References 29 publications

Retinal Photograph-based Deep Learning System for Detection of Thyroid-Associated Ophthalmopathy

Retinal Photograph-based Deep Learning System for Detection of Thyroid-Associated Ophthalmopathy

A TSHR-Targeting Aptamer in Monocytes Correlating with Clinical Activity in TAO

Whole-orbit radiomics: machine learning-based multi- and fused- region radiomics signatures for intravenous glucocorticoid response prediction in thyroid eye disease

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