ArticleFrontiers in endocrinology2023
Factors related to steroid treatment responsiveness in thyroid eye disease patients and application of SHAP for feature analysis with XGBoost.
Article in Frontiers in endocrinology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Prediction models of intravenous glucocorticoids therapy response in thyroid eye disease.European thyroid journal · 2024Pooled it
- A Deep Learning Framework for Predicting Teprotumumab Treatment Response in Thyroid Eye Disease.Ophthalmology science · 2026Article
- Status of and factors influencing paternal fatigue in children with retinoblastoma: a cross-sectional study.BMC cancer · 2025Article
- Oculoplastics and Augmented Intelligence: A Literature Review.Journal of clinical medicine · 2025Article
- Interpretable machine learning models for survival prediction in prostate cancer bone metastases.Scientific reports · 2025Article
- Insight into the metabolic shifts in Graves' hyperthyroidism: a study of acylcarnitine and lipid profiles.European thyroid journal · 2025Article
- Predicting Visual Acuity after Retinal Vein Occlusion Anti-VEGF Treatment: Development and Validation of an Interpretable Machine Learning Model.Journal of medical systems · 2025Article
- Construction of a machine learning-based interpretable prediction model for acute kidney injury in hospitalized patients.Scientific reports · 2025Observational
- Construction of machine learning-based models for screening the high-risk patients with gastric precancerous lesions.Chinese medicine · 2025Article
- Assessment of disease activity in Graves' orbitopathy.Frontiers in ophthalmology · 2025Review
- Multimodal MRI radiomics-clinical fusion model predicts intravenous glucocorticoid response in thyroid eye disease.Frontiers in endocrinology · 2025Article
- Development of a prognostic nomogram for ocular melanoma: a SEER population-based study (2000-2021).Frontiers in medicine · 2025Article
- The role of mycophenolate mofetil in the therapy of thyroid eye disease.Therapeutic advances in endocrinology and metabolism · 2025Review
- Age-dependent diagnostic and treatment response prediction of TBII and TSI in Graves' orbitopathy: integration with orbital MRI biomarkers.Frontiers in immunology · 2025Article
- A Comprehensive Review of Thyroid Eye Disease Pathogenesis: From Immune Dysregulations to Novel Diagnostic and Therapeutic Approaches.International journal of molecular sciences · 2024Review
- Prediction of Post-Treatment Visual Acuity in Age-Related Macular Degeneration Patients With an Interpretable Machine Learning Method.Translational vision science & technology · 2024Article
- Thyroid stimulating immunoglobulin concentration is associated with disease activity and predicts response to treatment with intravenous methylprednisolone in patients with Graves' orbitopathy.Frontiers in endocrinology · 2024Article
- Advances in artificial intelligence in thyroid-associated ophthalmopathy.Frontiers in endocrinology · 2024Review
- Predictive markers for anti-inflammatory treatment response in thyroid eye disease.Frontiers in endocrinology · 2023Review
- Exploring factors affecting patient satisfaction in online healthcare: A machine learning approach grounded in empathy theory.Digital healthArticle
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: The primary treatment for active thyroid eye disease (TED) is immunosuppressive therapy with intravenous steroids. In this study, we attempted to predict responsiveness to steroid treatment in TED patients using eXtreme Gradient Boosting (XGBoost). Factors associated with steroid responsiveness were also statistically evaluated. Methods: Clinical characteristics and laboratory results of 89 patients with TED who received steroid treatment were retrospectively reviewed. XGBoost was used to explore responsiveness to steroid treatment, and the diagnostic performance was evaluated. Factors contributing to the model output were investigated using the SHapley Additive exPlanation (SHAP), and the treatment response was investigated statistically using SPSS software. Results: The eXtra Gradient Boost model showed high performance, with an excellent accuracy of 0.861. Thyroid-stimulating hormone, thyroid-stimulating immunoglobulin (TSI), and low-density lipoprotein (LDL) cholesterol had the highest impact on the model. Multivariate logistic regression analysis showed that less extraocular muscle limitation and high TSI levels were associated with a high risk of poor intravenous methylprednisolone treatment response. As a result of analysis through SHAP, TSH, TSI, and LDL had the highest impact on the XGBoost model. Conclusion: TSI, extraocular muscle limitation, and LDL cholesterol levels may be useful in predicting steroid treatment response in patients with TED. In terms of machine learning, XGBoost showed relatively robust and reliable results for small datasets. The machine-learning model can assist in decision-making for further treatment of patients with TED.
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