[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117153-en":3,"doc-seo-117153-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},117153,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",7,"Healthcare","Radiomics-Based Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography - Article","Acute pancreatitis requires rapid and reliable diagnostic workup, especially when clinical findings remain uncertain. This study evaluates an automatic machine learning pipeline that detects acute pancreatitis using contrast-enhanced computed tomography radiomics. Patients presenting with abdominal pain and contrast-enhanced CT were included retrospectively in a single-center cohort. The pancreas was automatically segmented, radiomics features were extracted, and important features were selected via unsupervised hierarchical clustering and a Boruta-based random-forest approach. A logistic regression model integrating selected features and lipase levels achieved strong diagnostic performance, with AUC values around 0.933. The automated radiomics approach closely matched lipase-based accuracy, supporting its potential role as an additional diagnostic tool.","diagnostics  \nArticle  \nRadiomics-Based Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography  \nStefanie Bette 1, Luca Canalini 1, Laura-Marie Feitelson 1, Piotr Wo´znicki 2, Franka Risch 1, Adrian Huber 1, Josua A. Decker 1, Kartikay Tehlan 1, Judith Becker 1, Claudia Wollny 1, Christian Scheurig-Münkler 1, Thomas Wendler 1,3,4, Florian Schwarz 5 and Thomas Kroencke 1,6, *  \nCitation: Bette, S.; Canalini, L.;  \nFeitelson, L.-M.; Wo´znicki, P.; Risch, F.; Huber, A.; Decker, J.A.; Tehlan, K.; Becker, J.; Wollny, C.; et al. RadiomicsBased Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography. Diagnostics 2024, 14, 718. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/diagnostics14070718](10.3390/diagnostics14070718)  \nAcademic Editor: Jean-Francois  \nH. Geschwind  \nReceived: 2 February 2024  \nRevised: 21 March 2024  \nAccepted: 22 March 2024  \nPublished: 28 March 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg,  \n86156 Augsburg, Germany; [stefanie.bette@uk-augsburg.de](stefanie.bette@uk-augsburg.de) (S.B.); [luca.canalini@uk-augsburg.de](luca.canalini@uk-augsburg.de) (L.C.); [laura-marie.feitelson@uk-augsburg.de](laura-marie.feitelson@uk-augsburg.de) (L.-M.F.); [adrian.huber@uk-augsburg.de](adrian.huber@uk-augsburg.de) (A.H.);  \njosua.decker@uk-augsburg.de (J.A.D.); [kartikay.tehlan@uk-augsburg.de](kartikay.tehlan@uk-augsburg.de) (K.T.);  \n[judith.becker@uk-augsburg.de](judith.becker@uk-augsburg.de) (J.B.); [claudia.wollny@uk-augsburg.de](claudia.wollny@uk-augsburg.de) (C.W.);  \n[christian.scheurig@uk-augsburg.de](christian.scheurig@uk-augsburg.de) (C.S.-M.); [thomas.wendler@uk-augsburg.de](thomas.wendler@uk-augsburg.de) (T.W.)  \n2 Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, University of Würzburg, 97080 Würzburg, Germany; [piotr.a.woznicki@gmail.com](piotr.a.woznicki@gmail.com)  \n3 Institute of Digital Health, University Hospital Augsburg, Faculty of Medicine, University of Augsburg, 86356 Neusaess, Germany  \n4 Computer-Aided Medical Procedures and Augmented Reality, School of Computation, Information and Technology, Technical University of Munich, 85748 Garching bei Muenchen, Germany  \n5 Centre for Diagnostic Imaging and Interventional Therapy, Donau-Isar-Klinikum,  \n94469 Deggendorf, Germany; [florian.schwarz@donau-isar-klinikum.de](florian.schwarz@donau-isar-klinikum.de)  \n6 Centre for Advanced Analytics and Predictive Sciences (CAAPS), University of Augsburg,  \n86159 Augsburg, Germany  \n* Correspondence: [thomas.kroencke@uk-augsburg.de](thomas.kroencke@uk-augsburg.de); Tel.: +49-821-400-2441  \nAbstract: In the early diagnostic workup of acute pancreatitis (AP), the role of contrast-enhanced CTis to establish the diagnosis in uncertain cases, assess severity, and detect potential complications like necrosis, fluid collections, bleeding or portal vein thrombosis. The value of texture analysis/radiomicsof medical images has rapidly increased during the past decade, and the main focus has been ononcological imaging and tumor classification. Previous studies assessed the value of radiomics for differentiating between malignancies and inflammatory diseases of the pancreas as well as for prediction of AP severity. The aim of our study was to evaluate an automatic machine learning model for AP detection using radiomics analysis. Patients with abdominal pain and contrast-enhanced CT of the abdomen in an emergency setting were retrospectively included in this single-center study. The pancreas was automat","cbCaijIoocAdfy0k","https://ap.wps.com/l/cbCaijIoocAdfy0k","pdf",1438882,1,12,"English","en",105,"# Introduction\n## Methods\n## Feature Selection and Modeling\n## Results\n## Discussion","[{\"question\":\"What is the main goal of the radiomics-based study for acute pancreatitis?\",\"answer\":\"To evaluate an automatic machine learning model that detects acute pancreatitis using radiomics analysis from contrast-enhanced CT images.\"},{\"question\":\"How were CT images processed and radiomics features obtained?\",\"answer\":\"The pancreas was automatically segmented using TotalSegmentator, and radiomics features were extracted using PyRadiomics.\"},{\"question\":\"Which factors were used in the final diagnostic model and how well did it perform?\",\"answer\":\"Selected radiomics features and lipase levels were included in a logistic regression model, achieving excellent diagnostic accuracy with AUC values of about 0.933 when combined, and about 0.946 using lipase alone.\"}]","Radiomics-Based Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography - Article | PDF",1785674141,30,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"radiomics-based-machine-learning-model-for-diagnosis-of-acute-pancreatitis-using-computed-tomography-article","",{"@graph":36,"@context":85},[37,54,68],{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/radiomics-based-machine-learning-model-for-diagnosis-of-acute-pancreatitis-using-computed-tomography-article/117153/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the radiomics-based study for acute pancreatitis?","Question",{"text":75,"@type":76},"To evaluate an automatic machine learning model that detects acute pancreatitis using radiomics analysis from contrast-enhanced CT images.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were CT images processed and radiomics features obtained?",{"text":80,"@type":76},"The pancreas was automatically segmented using TotalSegmentator, and radiomics features were extracted using PyRadiomics.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factors were used in the final diagnostic model and how well did it perform?",{"text":84,"@type":76},"Selected radiomics features and lipase levels were included in a logistic regression model, achieving excellent diagnostic accuracy with AUC values of about 0.933 when combined, and about 0.946 using lipase alone.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":29,"slug":121},8,"Research & Report","research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]