Chenxu Zhao, Zhiming Sun, Yang Yu, Yiwei Lou, Liyuan Liu, Ge Li, Jumei Liu, Lei Chen, Sainan Zhu, Yu Huang, Yang Zhang, Ying Gao
{"title":"基于机器学习的 IgG4 桥本氏甲状腺炎诊断模型。","authors":"Chenxu Zhao, Zhiming Sun, Yang Yu, Yiwei Lou, Liyuan Liu, Ge Li, Jumei Liu, Lei Chen, Sainan Zhu, Yu Huang, Yang Zhang, Ying Gao","doi":"10.1007/s12020-024-03889-y","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>This study aims to develop a non-invasive diagnosis model using machine learning (ML) for identifying high-risk IgG4 Hashimoto's thyroiditis (HT) patients.</p><p><strong>Methods: </strong>A 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.</p><p><strong>Results: </strong>Among 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.</p><p><strong>Conclusion: </strong>A clinical diagnosis model for IgG4 HT established by RF model might help the early recognition of the high-risk patients of IgG4 HT.</p>","PeriodicalId":11572,"journal":{"name":"Endocrine","volume":" ","pages":"672-681"},"PeriodicalIF":3.7000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A machine learning-based diagnosis modeling of IgG4 Hashimoto's thyroiditis.\",\"authors\":\"Chenxu Zhao, Zhiming Sun, Yang Yu, Yiwei Lou, Liyuan Liu, Ge Li, Jumei Liu, Lei Chen, Sainan Zhu, Yu Huang, Yang Zhang, Ying Gao\",\"doi\":\"10.1007/s12020-024-03889-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>This study aims to develop a non-invasive diagnosis model using machine learning (ML) for identifying high-risk IgG4 Hashimoto's thyroiditis (HT) patients.</p><p><strong>Methods: </strong>A 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.</p><p><strong>Results: </strong>Among 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.</p><p><strong>Conclusion: </strong>A clinical diagnosis model for IgG4 HT established by RF model might help the early recognition of the high-risk patients of IgG4 HT.</p>\",\"PeriodicalId\":11572,\"journal\":{\"name\":\"Endocrine\",\"volume\":\" \",\"pages\":\"672-681\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Endocrine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s12020-024-03889-y\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/5/29 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Endocrine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12020-024-03889-y","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/29 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"Medicine","Score":null,"Total":0}
A machine learning-based diagnosis modeling of IgG4 Hashimoto's thyroiditis.
Purpose: This study aims to develop a non-invasive diagnosis model using machine learning (ML) for identifying high-risk IgG4 Hashimoto's thyroiditis (HT) patients.
Methods: A 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.
Results: Among 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.
Conclusion: A clinical diagnosis model for IgG4 HT established by RF model might help the early recognition of the high-risk patients of IgG4 HT.
期刊介绍：
Well-established as a major journal in today’s rapidly advancing experimental and clinical research areas, Endocrine publishes original articles devoted to basic (including molecular, cellular and physiological studies), translational and clinical research in all the different fields of endocrinology and metabolism. Articles will be accepted based on peer-reviews, priority, and editorial decision. Invited reviews, mini-reviews and viewpoints on relevant pathophysiological and clinical topics, as well as Editorials on articles appearing in the Journal, are published. Unsolicited Editorials will be evaluated by the editorial team. Outcomes of scientific meetings, as well as guidelines and position statements, may be submitted. The Journal also considers special feature articles in the field of endocrine genetics and epigenetics, as well as articles devoted to novel methods and techniques in endocrinology.
Endocrine covers controversial, clinical endocrine issues. Meta-analyses on endocrine and metabolic topics are also accepted. Descriptions of single clinical cases and/or small patients studies are not published unless of exceptional interest. However, reports of novel imaging studies and endocrine side effects in single patients may be considered. Research letters and letters to the editor related or unrelated to recently published articles can be submitted.
Endocrine covers leading topics in endocrinology such as neuroendocrinology, pituitary and hypothalamic peptides, thyroid physiological and clinical aspects, bone and mineral metabolism and osteoporosis, obesity, lipid and energy metabolism and food intake control, insulin, Type 1 and Type 2 diabetes, hormones of male and female reproduction, adrenal diseases pediatric and geriatric endocrinology, endocrine hypertension and endocrine oncology.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
