ArticleEndocrine2024
A machine learning-based diagnosis modeling of IgG4 Hashimoto's thyroiditis.
Article in Endocrine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Ultrasound Radiomics for Hashimoto's Thyroiditis: State of the Art and Future Perspectives.Journal of clinical ultrasound : JCU · 2026Review
- Artificial intelligence-based ultrasound diagnosis of Hashimoto's thyroiditis: a systematic review and meta-analysis.Thyroid research · 2026Review
- Usefulness of core needle biopsy of thyroid for the diagnosis of IgG4 Hashimoto's thyroiditis.Journal of translational internal medicine · 2026Article
- Prediction of Seronegative Hashimoto's thyroiditis using machine learning models based on ultrasound radiomics: a multicenter study.BMC immunology · 2025Article
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Authors and funding
12 authors.
Funding
Abstract
purposeThis study aims to develop a non-invasive diagnosis model using machine learning (ML) for identifying high-risk IgG4 Hashimoto's thyroiditis (HT) patients.
methodsA retrospective cohort of 93 HT patients and a prospective cohort of 179 HT patients were collected. According to the immunohistochemical and pathological results, the patients were divided into IgG4 HT group and non-IgG4 HT group. Serum TgAb IgG4 and TPOAb IgG4 were detected by ELISAs. A logistic regression model, support vector machine (SVM) and random forest (RF) were used to establish a clinical diagnosis model for IgG4 HT.
resultsAmong these 272 patients, 40 (14.7%) were diagnosed with IgG4 HT. Patients with IgG4 HT were younger than those with non-IgG4 HT (P < 0.05). Serum levels of TgAb IgG4 and TPOAb IgG4 in IgG4 HT group were significantly higher than those in non-IgG4 HT group (P < 0.05). There were no significant differences in gender, disease duration, goiter, preoperative thyroid function status, preoperative TgAb or TPOAb levels, and thyroid ultrasound characteristics between the two groups (all P > 0.05). The accuracy, sensitivity, and specificity were 57%, 78%, and 79% for logistic regression model of IgG4 HT, 80 ± 7%, 84.7% ± 2.6%, and 75.4% ± 9.6% for the RF model and 78 ± 5%, 89.8% ± 5.7%, and 64.7% ± 5.7% for the SVM model. The RF model works better than SVM. The area under the ROC curve of RF ranged 0.87 to 0.92.
conclusionA clinical diagnosis model for IgG4 HT established by RF model might help the early recognition of the high-risk patients of IgG4 HT.
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