{"title":"预测自身免疫性肝炎患者晚期肝纤维化的探索性机器学习模型:初步研究","authors":"Qinglin Wei, Wen Li, Shubei He, Hongbo Wu, Qiaoling Xie, Ying Peng, Xingyue Zhang","doi":"10.1016/j.aohep.2024.101754","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction and objectives: </strong>Advanced fibrosis is a crucial stage in the progression of autoimmune hepatitis (AIH), where fibrosis can either regress or advance. This study aims to leverage machine learning (ML) models for the assessment of advanced liver fibrosis in AIH patients using routine clinical features.</p><p><strong>Patients and methods: </strong>A total of 233 patients diagnosed with AIH and underwent liver biopsy were included in the discovery cohort. The dataset was randomly split into training and testing sets. Patients were categorized into groups with no/minimal/moderate fibrosis and advanced fibrosis. Six ML models were employed to identify the optimal model. Subsequently, the predictive capability of the best ML model was validated in an additional cohort (n = 33) and compared with conventional noninvasive fibrosis scores.</p><p><strong>Results: </strong>Three key clinical features, including prothrombin time (PT), albumin (ALB), and ultrasound spleen thickness (UTST), were analyzed by least absolute shrinkage and selection operator (LASSO) regression. In the training set, the random forest (RF) model showed the highest diagnostic performance in predicting advanced fibrosis stage (AUC=0.951). In the testing cohort and validation cohort, the RF model maintained high accuracy (AUC = 0.863 and AUC = 0.843). Additionally, the random forest model outperformed the conventional noninvasive fibrosis scores.</p><p><strong>Conclusions: </strong>ML models, particularly the RF model, can help improve the discrimination of advanced liver fibrosis in patients with AIH.</p>","PeriodicalId":7979,"journal":{"name":"Annals of hepatology","volume":" ","pages":"101754"},"PeriodicalIF":3.7000,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An exploratory machine learning model for predicting advanced liver fibrosis in autoimmune hepatitis patients: A preliminary study.\",\"authors\":\"Qinglin Wei, Wen Li, Shubei He, Hongbo Wu, Qiaoling Xie, Ying Peng, Xingyue Zhang\",\"doi\":\"10.1016/j.aohep.2024.101754\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction and objectives: </strong>Advanced fibrosis is a crucial stage in the progression of autoimmune hepatitis (AIH), where fibrosis can either regress or advance. This study aims to leverage machine learning (ML) models for the assessment of advanced liver fibrosis in AIH patients using routine clinical features.</p><p><strong>Patients and methods: </strong>A total of 233 patients diagnosed with AIH and underwent liver biopsy were included in the discovery cohort. The dataset was randomly split into training and testing sets. Patients were categorized into groups with no/minimal/moderate fibrosis and advanced fibrosis. Six ML models were employed to identify the optimal model. Subsequently, the predictive capability of the best ML model was validated in an additional cohort (n = 33) and compared with conventional noninvasive fibrosis scores.</p><p><strong>Results: </strong>Three key clinical features, including prothrombin time (PT), albumin (ALB), and ultrasound spleen thickness (UTST), were analyzed by least absolute shrinkage and selection operator (LASSO) regression. In the training set, the random forest (RF) model showed the highest diagnostic performance in predicting advanced fibrosis stage (AUC=0.951). In the testing cohort and validation cohort, the RF model maintained high accuracy (AUC = 0.863 and AUC = 0.843). Additionally, the random forest model outperformed the conventional noninvasive fibrosis scores.</p><p><strong>Conclusions: </strong>ML models, particularly the RF model, can help improve the discrimination of advanced liver fibrosis in patients with AIH.</p>\",\"PeriodicalId\":7979,\"journal\":{\"name\":\"Annals of hepatology\",\"volume\":\" \",\"pages\":\"101754\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-12-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of hepatology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.aohep.2024.101754\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GASTROENTEROLOGY & HEPATOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of hepatology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.aohep.2024.101754","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
An exploratory machine learning model for predicting advanced liver fibrosis in autoimmune hepatitis patients: A preliminary study.
Introduction and objectives: Advanced fibrosis is a crucial stage in the progression of autoimmune hepatitis (AIH), where fibrosis can either regress or advance. This study aims to leverage machine learning (ML) models for the assessment of advanced liver fibrosis in AIH patients using routine clinical features.
Patients and methods: A total of 233 patients diagnosed with AIH and underwent liver biopsy were included in the discovery cohort. The dataset was randomly split into training and testing sets. Patients were categorized into groups with no/minimal/moderate fibrosis and advanced fibrosis. Six ML models were employed to identify the optimal model. Subsequently, the predictive capability of the best ML model was validated in an additional cohort (n = 33) and compared with conventional noninvasive fibrosis scores.
Results: Three key clinical features, including prothrombin time (PT), albumin (ALB), and ultrasound spleen thickness (UTST), were analyzed by least absolute shrinkage and selection operator (LASSO) regression. In the training set, the random forest (RF) model showed the highest diagnostic performance in predicting advanced fibrosis stage (AUC=0.951). In the testing cohort and validation cohort, the RF model maintained high accuracy (AUC = 0.863 and AUC = 0.843). Additionally, the random forest model outperformed the conventional noninvasive fibrosis scores.
Conclusions: ML models, particularly the RF model, can help improve the discrimination of advanced liver fibrosis in patients with AIH.
期刊介绍:
Annals of Hepatology publishes original research on the biology and diseases of the liver in both humans and experimental models. Contributions may be submitted as regular articles. The journal also publishes concise reviews of both basic and clinical topics.