[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127565-en":3,"doc-seo-127565-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127565,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning models predict liver steatosis but not liver fibrosis in a prospective cohort study","Machine learning models are evaluated for predicting liver steatosis on abdominal ultrasound and intermediate-high risk advanced liver fibrosis in participants of a colorectal cancer screening program. Ultrasound was performed in 5834 patients (2006–2020) and transient elastography in 1240 patients, using echo-genicity for steatosis and liver stiffness ≥8 kPa for fibrosis. XGBoost, feed-forward neural networks, and logistic regression were trained on 2007–2016 data and prospectively tested on 2016–2020. Results show high steatosis prediction (AUC 0.87) and moderate fibrosis prediction (AUC 0.75), with no significant gain from patient self-reported outcomes and clinically relevant gender differences suggesting the need for gender-specific models.","Clinics and Research in Hepatology and Gastroenterology 47 (2023) 102181  \nContents lists available at ScienceDirect  \nClinics and Research in Hepatology and Gastroenterology  \njournal [homepage:](homepage: www.elsevier.com/locate/clinre)[ www.elsevier.com/locate/clinre](homepage: www.elsevier.com/locate/clinre)  \n| Original article\u003Cbr>Machine learning models predict liver steatosis but not liver fibrosis in a prospective cohort study |  |  |  |\n| --- | --- | --- | --- |\n| Behrooz Mamandipoora, Sarah Wernlyb, Georg Semmlerc, Maria Flammd, Christian Junge, Elmar Aignerf, Christian Datzb, Bernhard Wernlyb, d, \\#, Venet Osmanig, \\#, *\u003Cbr>a Fondazione Bruno Kessler Research Institute, Trento, Italy\u003Cbr>b Department of Internal Medicine, General Hospital Oberndorf, Teaching Hospital of the Paracelsus Medical University, Salzburg, Austria c Division of Gastroenterology and Hepatology, Department of Medicine III, Medical University of Vienna, Vienna, Austria\u003Cbr>d Institute of general practice, family medicine and preventive medicine, Paracelsus Medical University, Salzburg, Austria\u003Cbr>e Department of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, Germany f Clinic I for Internal Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria\u003Cbr>g Information School, University of Sheffield, United Kingdom |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Steatosis\u003Cbr>Liver fibrosis Machine learning Predictive modelling Gender differences\u003Cbr>Patient self-reported outcomes |  | Introduction: Screening for liver fibrosis continues to rely on laboratory panels and non-invasive tests such as FIB- 4-score and transient elastography. In this study, we evaluated the potential of machine learning (ML) methods to predict liver steatosis on abdominal ultrasound and liver fibrosis, namely the intermediate-high risk of advanced fibrosis, in individuals participating in a screening program for colorectal cancer.\u003Cbr>Methods: We performed ultrasound on 5834 patients admitted between 2006 and 2020, and transient elastography on a subset of 1240 patients. Steatosis on ultrasound was diagnosed if liver areas showed a significantly increased echogenicity compared to the renal parenchyma. Liver fibrosis was defined as a liver stiffness measurement ≥8 kPa in transient elastography. We evaluated the performance of three algorithms, namely Extreme Gradient Boosting, Feed-Forward neural network and Logistic Regression, deriving the models using data from patients admitted from January 2007 up to January 2016 and prospectively evaluating on the data of patients admitted from January 2016 up to March 2020. We also performed a performance comparison with the standard clinical test based on Fibrosis-4 Index (FIB-4).\u003Cbr>Results: The mean age was 58±9 years with 3036 males (52%). Modelling laboratory parameters, clinical parameters, and data on eight food types/dietary patterns, we achieved high performance in predicting liver steatosis on ultrasound with AUC of 0.87 (95% CI [0.87–0.87]), and moderate performance in predicting liver fibrosis with AUC of 0.75 (95% CI [0.74–0.75]) using XGBoost machine learning algorithm. Patient-reported variables did not significantly improve predictive performance. Gender-specific analyses showed significantly higher performance in males with AUC of 0.74 (95% CI [0.73–0.74]) in comparison to female patients with AUCof 0.66 (95% CI [0.65–0.66]) in prediction of liver fibrosis. This difference was significantly smaller in prediction of steatosis with AUC of 0.85 (95% CI [0.83–0.87]) in female patients, in comparison to male patients with AUCof 0.82 (95% CI [0.80–0.84]).\u003Cbr>Conclusion: ML based on point-prevalence laboratory and clinical information predicts liver steatosis with high accuracy and liver fibrosis with moderate accuracy. The observed gender differences suggest the need to develop gender-specific models. |  |\n\nIntrodu","cbCaiuvIaOl03xwc","https://ap.wps.com/l/cbCaiuvIaOl03xwc","pdf",4003139,1,10,"English","en",105,"# Introduction\n## Non-alcoholic fatty liver disease and need for screening\n## Rationale for predicting steatosis and fibrosis\n# Methods\n## Study population and imaging assessments\n## Algorithms and training/testing design\n## Comparison with standard clinical testing\n# Results\n## Model performance for steatosis and fibrosis\n## Impact of patient-reported variables\n## Gender-specific performance\n# Conclusion","[{\"question\":\"What clinical problem does the study address?\",\"answer\":\"The study targets improved screening prediction for liver steatosis and liver fibrosis, where current approaches rely on laboratory panels and non-invasive tests such as FIB-4 and transient elastography.\"},{\"question\":\"How were steatosis and liver fibrosis defined in the analysis?\",\"answer\":\"Steatosis on ultrasound was diagnosed by significantly increased liver echogenicity compared with renal parenchyma. Liver fibrosis was defined as liver stiffness measurement ≥8 kPa on transient elastography.\"},{\"question\":\"Which machine learning method performed best and what were the key results?\",\"answer\":\"XGBoost achieved high accuracy for steatosis prediction (AUC 0.87) and moderate performance for fibrosis prediction (AUC 0.75). Patient self-reported variables did not significantly improve predictive performance.\"}]","Machine learning models predict liver steatosis but not liver fibrosis in a prospective cohort study | PDF",1785940006,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-models-predict-liver-steatosis-but-not-liver-fibrosis-in-a-prospective-cohort-study","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-models-predict-liver-steatosis-but-not-liver-fibrosis-in-a-prospective-cohort-study/127565/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What clinical problem does the study address?","Question",{"text":76,"@type":77},"The study targets improved screening prediction for liver steatosis and liver fibrosis, where current approaches rely on laboratory panels and non-invasive tests such as FIB-4 and transient elastography.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were steatosis and liver fibrosis defined in the analysis?",{"text":81,"@type":77},"Steatosis on ultrasound was diagnosed by significantly increased liver echogenicity compared with renal parenchyma. Liver fibrosis was defined as liver stiffness measurement ≥8 kPa on transient elastography.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning method performed best and what were the key results?",{"text":85,"@type":77},"XGBoost achieved high accuracy for steatosis prediction (AUC 0.87) and moderate performance for fibrosis prediction (AUC 0.75). Patient self-reported variables did not significantly improve predictive performance.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":21,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]