[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128024-en":3,"doc-seo-128024-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128024,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Enhancing mortality prediction in patients with spontaneous intracerebral hemorrhage - Radiomics and supervised machine learning on non-contrast computed tomography","Developing accurate tools to estimate in-hospital mortality risk in spontaneous intracerebral hemorrhage (sICH) is the goal of this study. A retrospective analysis uses a prospectively collected clinical registry from a single comprehensive stroke center, spanning January 2016 to April 2018, and extracts 105 radiomic features from 105 patients’ non-contrast CT. Multiple supervised classifiers are trained with stratified splits and tenfold cross-validation, evaluating feature selection and hyperparameter optimization strategies to classify mortality versus survival during admission.","European Journal of Radiology Open 13 (2024) 100618  \nContents lists available at ScienceDirect  \nEuropean Journal of Radiology Open  \njournal [homepage:](homepage: www.elsevier.com/locate/ejro)[ www.elsevier.com/locate/ejro](homepage: www.elsevier.com/locate/ejro)  \n| Enhancing mortality prediction in patients with spontaneous intracerebral   hemorrhage: Radiomics and supervised machine learning on non-contrast computed tomography\u003Cbr>Antonio L´opez-Rueda a,b,*,1,2, María-´Angeles Rodríguez-S´anchez c, Elena Serrano d, Javier Moreno b, Alejandro Rodríguez e, Laura Llulle, Sergi Amaroe, Laura Oleagab\u003Cbr>a Clinical Informatics Department, Hospital Clínic de Barcelona, Barcelona, Spain b Radiology Department, Hospital Clínic de Barcelona, Barcelona, Spain\u003Cbr>c Valencian Research Institute for Artificial Intelligence (VRAIN), Universitat Polit`ecnica de Val`encia, Valencia, Spain d Radiology Department, Hospital Universitario de Bellvitge, Barcelona, Spain\u003Cbr>e Neurology Department, Hospital Clínic de Barcelona, Barcelona, Spain |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Radiomics\u003Cbr>Machine learning Intracerebral hemorrhage Computed tomography Stroke |  | Purpose: This study aims to develop a Radiomics-based Supervised Machine-Learning model to predict mortality in patients with spontaneous intracerebral hemorrhage (sICH).\u003Cbr>Methods: Retrospective analysis of a prospectively collected clinical registry of patients with sICH consecutively admitted at a single academic comprehensive stroke center between January-2016 and April-2018. We conducted an in-depth analysis of 105 radiomic features extracted from 105 patients. Following the identification and handling of missing values, radiomics values were scaled to 0–1 to train different classifiers. The sample was split into 80–20 % training-test and validation cohort in a stratified fashion. Random Forest(RF), K-Nearest Neighbor(KNN), and Support Vector Machine(SVM) classifiers were evaluated, along with several feature selection methods and hyperparameter optimization strategies, to classify the binary outcome of mortality or survival during hospital admission. A tenfold stratified cross-validation method was used to train the models, and average metrics were calculated.\u003Cbr>Results: RF, KNN, and SVM, with the \"DropOut+SelectKBest\" feature selection strategy and no hyperparameter optimization, demonstrated the best performances with the least number of radiomic features and the most simplified models, achieving a sensitivity range between 0.90 and 0.95 and AUC range from 0.97 to 1 on the validation dataset. Regarding the confusion matrix, the SVM model did not predict any false negative test (negative predicted value 1).\u003Cbr>Conclusion: Radiomics-based Supervised Machine Learning models can predict mortality during admission inpatients with sICH. SVM with the \"DropOut+SelectKBest\" feature selection strategy and no hyperparameter optimization was the best simplified model to detect mortality during admission in patients with sICH. |\n\n1. Introduction  \nCerebrovascular disease is the second leading cause of death worldwide. After ischemic stroke, spontaneous intracerebral hemorrhage (sICH) is the second most common subtype accounting for 10–20 % of all cases [1,2].  \nNon-contrast head computed tomography (NCCT) is the first-line diagnostic test for the emergency evaluation of acute stroke [3]. Multiple radiological signs, representing irregularity and/or heterogeneity of the hematoma, had been defined on NCCT as predictors of expansion or clinical outcome of the patients with sICH [4–10].The limitations of these radiological signs include their low inter- and intra-observer  \n* Correspondence to: 170 Villarroel Street, Barcelona 08036, Spain.  \nE-mail address: [alrueda81@hotmail.com](alrueda81@hotmail.com) (A. L´opez-Rueda).  \n1 Twitter: @AntonioLR81  \n2 ORCID: 0000–0001-7914–9948  \n[https://doi.org/10.1016/j.ejro.2024.100618](https://doi.or","cbCaietikdPVFed2","https://ap.wps.com/l/cbCaietikdPVFed2","pdf",4110629,3,1,7,"English","en",105,"# Abstract\n# Introduction\n# Materials and methods\n## Study design","[{\"question\":\"What is the study’s main purpose for spontaneous intracerebral hemorrhage patients?\",\"answer\":\"To develop radiomics-based supervised machine learning models that predict in-hospital mortality risk in patients with spontaneous intracerebral hemorrhage using non-contrast CT images.\"},{\"question\":\"How were the radiomic features and datasets prepared?\",\"answer\":\"Radiomic features were extracted for each of 105 patients, missing values were handled, values were scaled to 0–1, and the dataset was split into stratified 80–20 training-test and validation cohorts.\"},{\"question\":\"Which machine learning approach performed best in the results?\",\"answer\":\"Random Forest, KNN, and SVM combined with the “DropOut+SelectKBest” feature selection strategy (without hyperparameter optimization) showed the best validation performance, with very high sensitivity and AUC values.\"}]","Enhancing mortality prediction in patients with spontaneous intracerebral hemorrhage - Radiomics and supervised machine learning on non-contrast computed tomography | PDF",1785944038,18,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"enhancing-mortality-prediction-in-patients-with-spontaneous-intracerebral-hemorrhage-radiomics-and-supervised-machine-learning-on-non-contrast-computed-tomography","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/enhancing-mortality-prediction-in-patients-with-spontaneous-intracerebral-hemorrhage-radiomics-and-supervised-machine-learning-on-non-contrast-computed-tomography/128024/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","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 is the study’s main purpose for spontaneous intracerebral hemorrhage patients?","Question",{"text":76,"@type":77},"To develop radiomics-based supervised machine learning models that predict in-hospital mortality risk in patients with spontaneous intracerebral hemorrhage using non-contrast CT images.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the radiomic features and datasets prepared?",{"text":81,"@type":77},"Radiomic features were extracted for each of 105 patients, missing values were handled, values were scaled to 0–1, and the dataset was split into stratified 80–20 training-test and validation cohorts.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning approach performed best in the results?",{"text":85,"@type":77},"Random Forest, KNN, and SVM combined with the “DropOut+SelectKBest” feature selection strategy (without hyperparameter optimization) showed the best validation performance, with very high sensitivity and AUC values.","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":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]