Texture analysis of orbital magnetic resonance imaging for monitoring and predicting treatment response to glucocorticoids in patients with thyroid-associated ophthalmopathy

Application of Quantitative MRI in Thyroid Eye Disease: Imaging Techniques and Clinical Practices

Thyroid eye disease (TED) is a complex autoimmune disorder that impairs various orbital structures, leading to cosmetic damage and vision loss. Magnetic resonance imaging (MRI) is a fundamental diagnostic tool utilized in clinical settings of TED, for its accurate demonstration of orbital lesions and indication of disease conditions. The application of quantitative MRI has brought a new prospect to the management and research of TED, offering more detailed information on morphological and functional changes in the orbit. Therefore, many researchers concentrated on the implementation of different quantitative MRI techniques on TED for the exploration of clinical practices. Despite the abundance of studies utilizing quantitative MRI in TED, there remain considerable barriers and disputes on the best exploitation of this tool. This could possibly be attributed to the complexity of TED and the fast development of MRI techniques. It is necessary that clinical and radiological aspects of quantitative MRI in TED be better integrated into comprehensive insights. Hence, this review traces back 30 years of publications regarding quantitative MRI utilized in TED and elucidates this promising application in the facets of imaging techniques and clinical practices. We believe that a deeper understanding of the application of quantitative MRI in TED will enhance the efficacy of the multidisciplinary management of TED. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 3.

Prediction models of intravenous glucocorticoids therapy response in thyroid eye disease

Background

Thyroid eye disease (TED) is an autoimmune orbital disease, with intravenous glucocorticoid (IVGC) therapy as the first-line treatment. Due to uncertain response rates and possible side effects, various prediction models have been developed to predict IVGC therapy outcomes.

Methods

A thorough search was conducted in PubMed, Embase, and Web of Science databases. Data extraction included publication details, prediction model content, and performance. Statistical analysis was performed using R software, including heterogeneity evaluation, publication bias, subgroup analysis, and sensitivity analysis. Forest plots were utilized for result visualization.

Results

Of the 12 eligible studies, 47 prediction models were extracted. All included studies exhibited a low-to-moderate risk of bias. The pooled area under the receiver operating characteristic curve (AUC) and the combined sensitivity and specificity for the models were 0.81, 0.75, and 0.79, respectively. In view of heterogeneity, multiple meta-regression and subgroup analysis were conducted, which showed that marker and modeling types may be the possible causes of heterogeneity (P < 0.001). Notably, imaging metrics alone (AUC = 0.81) or clinical characteristics combined with other markers (AUC = 0.87), incorporating with multivariate regression (AUC = 0.84) or radiomics analysis (AUC = 0.91), yielded robust and reliable prediction outcomes.

Conclusion

This meta-analysis comprehensively reviews the predictive models for IVGC therapy response in TED. It underscores that integrating clinical characteristics with laboratory or imaging indicators and employing advanced techniques like multivariate regression or radiomics analysis significantly enhance the efficacy of prediction. Our research findings offer valuable insights that can guide future studies on prediction models for IVGC therapy in TED.

Radiomics in ophthalmology: a systematic review

BACKGROUND

Radiomics holds great potential in medical image analysis for various ophthalmic diseases. In recent times, there have been numerous endeavors in this area of research. This systematic review aims to provide a comprehensive assessment of the strengths and limitations of radiomics in ophthalmology.

METHOD

Conforming to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, we conducted a systematic review with a pre-registered protocol (PROSPERO: CRD42023446317). We explored the PubMed, Embase, and Cochrane databases for original studies on this topic and made a comprehensive descriptive integration. Furthermore, the included studies underwent quality assessment by the radiomics quality score (RQS).

RESULTS

A total of 41 articles from an initial search of 227 studies were finally selected for further analysis. These articles included research across five disease categories and covered seven imaging modalities. The radiomics models demonstrated robust performance, with area under the curve (AUC) values mostly falling within 0.7-1.0. The moderate RQS (mean score: 11.17/36) indicated that most studies were retrospectively, single-center analyses without external validation.

CONCLUSIONS

Radiomics holds promising utility in the field of ophthalmology, assisting diagnosis, early-stage screening, and prognostication of treatment response. Artificial intelligence algorithms significantly contribute to the construction of radiomics models in ophthalmology. This study highlights the strengths and challenges of radiomics in ophthalmology and suggests potential avenues for future improvement.

CLINICAL RELEVANCE STATEMENT

Radiomics represents a valuable approach for generating innovative imaging markers, enhancing efficiency in clinical diagnosis and treatment, and aiding decision-making in clinical contexts of many ophthalmic diseases, thereby improving overall patient prognosis.

KEY POINTS

Radiomics has attracted extensive attention in the field of ophthalmology. Articles included five disease categories over seven imaging modalities, consistently yielding AUCs mostly above 0.7. Current research has few prospective and multi-center studies, underlining the necessity for future high-quality studies.

Advances in artificial intelligence in thyroid-associated ophthalmopathy

Thyroid-associated ophthalmopathy (TAO), also referred to as Graves' ophthalmopathy, is a medical condition wherein ocular complications arise due to autoimmune thyroid illness. The diagnosis of TAO, reliant on imaging, typical ocular symptoms, and abnormalities in thyroid function or thyroid-associated antibodies, is generally graded and staged. In recent years, Artificial intelligence(AI), particularly deep learning(DL) technology, has gained widespread use in the diagnosis and treatment of ophthalmic diseases. This paper presents a discussion on specific studies involving AI, specifically DL, in the context of TAO, highlighting their applications in TAO diagnosis, staging, grading, and treatment decisions. Additionally, it addresses certain limitations in AI research on TAO and potential future directions for the field.

Predictive markers for anti-inflammatory treatment response in thyroid eye disease

Anti-inflammatory treatment is the primary and vital therapeutic approach for active, moderate-to-severe thyroid eye disease (TED). Accurate pretreatment prediction of treatment response is of paramount importance for the prognosis of patients. However, relying solely on the clinical activity score asa determinant of activity has led to unsatisfactory treatment outcomes. In recent years, significant advancements have been made in identifying predictive markers for anti-inflammatory treatment response in TED, clinical markers, body fluid biomarkers and imaging biomarkers. Several clinical studies have developed prediction models based on these markers. However, there is still a lack of comprehensive elucidation or comparison between the different markers. Therefore, this review aims to provide a detailed analysis of the definition, characteristics, and application of predictive markers for anti-inflammatory treatment response in TED. Through detailed literature search, 26 articles applying anti-inflammatory treatment effect prediction with a total of 1948 TED patients were used for analysis and discussion. By gaining a better understanding of the current research on predictive markers, we can accelerate and guide the exploration of treatment prediction strategies, leading us towards an era of precise therapy for TED.

Roles of four targets in the pathogenesis of graves' orbitopathy

Graves' orbitopathy (GO) is an autoimmune disease that involves complex immune systems. The mainstays of clinical management for this disease are surgery, targeted drugs therapy, and no-targeted drugs drug therapy. targeted drugs can improve therapeutic efficacy and enhance the quality of life for GO patients. However, as a second-line treatment for GO, targeted drugs such as tocilizumab and rituximab have very limited therapeutic effects and may be accompanied by side effects. The introduction of Teprotumumab, which targets IGF-IR, has made significant progress in the clinical management of GO. The pathophysiology of GO still remains uncertain as it involves a variety of immune cells and fibroblast interactions as well as immune responses to relevant disease targets of action. Therfore, learning more about immune response feedback pathways and potential targets of action will assist in the treatment of GO. In this discussion, we explore the pathogenesis of GO and relevant work, and highlight four potential targets for GO: Interleukin-23 receptor (IL-23 R), Leptin receptor (LepR), Orbital fibroblast activating factors, and Plasminogen activator inhibitor-1 (PAI-1). A deeper understanding of the pathogenesis of GO and the role of potential target signaling pathways is crucial for effective treatment of this disease.

T2 -Weighted MR Imaging-Derived Radiomics for Pretreatment Determination of Therapeutic Response to Glucocorticoid in Patients With Thyroid-Associated Ophthalmopathy: Comparison With Semiquantitative Evaluation

BACKGROUND

MR imaging has been applied to determine therapeutic response to glucocorticoid (GC) before treatment in thyroid-associated ophthalmopathy (TAO), while the performance was still poor.

PURPOSE

To investigate the value of T₂ -weighted imaging (T₂ WI)-derived radiomics for pretreatment determination of therapeutic response to GC in TAO patients, and compare its diagnostic performance with that of semiquantitative parameters.

STUDY TYPE

Retrospective.

POPULATION

A total of 110 patients (49 ± 12 years; male/female, n = 48/62; responsive/unresponsive, n = 62/48), divided into training (n = 78) and validation (n = 32) cohorts.

FIELD STRENGTH/SEQUENCE

3.0 T, T₂ -weighted fast spin echo.

ASSESSMENT

W.C. and H.H. (6 and 10 years of experience, respectively) performed the measurements. Maximum, mean, and minimum signal intensity ratios (SIRs) of extraocular muscle (EOM) bellies were collected to construct a semiquantitative imaging model. Radiomics features from volumes of interest covering EOM bellies were extracted and three machine learning-based (logistic regression [LR]; decision tree [DT]; support vector machine [SVM]) models were built.

STATISTICAL TESTS

The diagnostic performances of models were evaluated using receiver operating characteristic curve analyses, and compared using DeLong test. Two-sided P < 0.05 was considered statistically significant.

RESULTS

The responsive group showed higher minimum signal intensity ratio (SIR_min ) of EOMs than the unresponsive group (training: 1.46 ± 0.34 vs. 1.18 ± 0.39; validation: 1.44 ± 0.33 vs. 1.19 ± 0.20). In both cohorts, LR-based radiomics model demonstrated good diagnostic performance (area under the curve [AUC] = 0.968, 0.916), followed by DT-based (AUC = 0.933, 0.857) and SVM-based models (AUC = 0.919, 0.855). All three radiomics models outperformed semiquantitative imaging model (SIR_min : AUC = 0.805) in training cohort. In validation cohort, only LR-based radiomics model outperformed that of SIR_min (AUC = 0.745). The nomogram integrating LR-based radiomics signature and disease duration further elevated the diagnostic performance in validation cohort (AUC: 0.952 vs. 0.916, P = 0.063).

DATA CONCLUSION

T₂ WI-derived radiomics of EOMs, together with disease duration, provides a promising noninvasive approach for determining therapeutic response before GC administration in TAO patients.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 4.