[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125734-en":3,"doc-seo-125734-105":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":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},125734,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Radiomics and Machine Learning for Skeletal Muscle Injury Recovery Prediction","Radiomics, a quantitative approach to medical imaging, enables artificial intelligence to extract and analyze complex imaging biomarkers. This study develops and evaluates multiple machine learning regression models to predict skeletal muscle injury recovery over time in rats using radiomics derived from contrast-enhanced CT. Ten regression algorithms are trained and compared by testing optimal radiomics combinations across different CT imaging parameter settings. The best ensemble model, trained on a 70 kVp and 100 mA dataset, achieves a mean absolute error of 1.22, supporting radiomics-based recovery volume prediction. Results also indicate CT acquisition parameters meaningfully affect predictive performance.","Radiomics and Machine Learning for Skeletal Muscle Injury Recovery Prediction  \nVasileios Eleftheriadis, José Raul Herance Camacho, Valentina Paneta, Bruno Paun, Carolina Aparicio , Vanesa Venegas, Mario Marotta, Marc Masa, George Loudos, and Panagiotis Papadimitroulas  \nAbstract—Radiomics as a novel quantitative approach to medical imaging is an emerging area in the field of radiology. Artificial intelligence offers promising tools for exploiting and analyzing radiomics. The objective of the present study is to propose a methodology for the design, development, and evaluation of machine learning (ML) models for the prediction of the recovery progress of skeletal muscle injury over time in rats using radiomics. Radiomics were extracted from contrast enhanced computed tomography (CT) data and ML algorithms were trained and compared for their predictive value based on different CT imaging parameters. Ten different ML regression algorithms were tested and the optimal combination of radiomics for each algorithm and CT imaging parameter settings combination was studied. The best ensemble learning model, trained on the 70 kVp, 100 mA imaging parameter dataset, achieved a mean absolute error score of 1.22. The results suggest that radiomics extracted from CT images can be used as input in ML regression algorithms to predict the volume of a skeletal muscle injury in rats. Moreover, the results show that CT imaging settings impact the predictive performance of the ML regression models, indicating that lower values of tube current and peak kilovoltage contribute to more accurate predictions.  \nIndex Terms—Computed tomography (CT), machine learning (ML), muscle injury, preclinical imaging, prediction model, radiomics, recovery.  \nManuscript received 16 May 2023; accepted 28 June 2023 . Date of publication 4 July 2023; date of current version 3 November 2023 . This work was supported by the European Union’s Horizon Research and Innovation Program under Grant 761031 . (Corresponding author: Panagiotis Papadimitroulas.)  \nThis work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by the Vall d’Hebron Animal Ethics Committee under Application No. 52/17, and performed in line with the Spanish (Real Decreto 53/2013) and European (2010/63/UE) legislation.  \nVasileios Eleftheriadis, Valentina Paneta, George Loudos, and Panagiotis Papadimitroulas are with the R&D Department, Bioemission Technology Solutions, 15343 Athens, Greece (e-mail: vasilis.eleftheriadis@ [bioemtech.com](bioemtech.com); [vpaneta@bioemtech.com](vpaneta@bioemtech.com); [george@bioemtech.com](george@bioemtech.com); panpap@  \n[bioemtech.com](bioemtech.com)) .  \nJosé Raul Herance Camacho, Bruno Paun, and Carolina Aparicio are with the Medical Molecular Imaging Group, Vall d’Hebron Research Institute, CIBER-BBN, CIBBIM-Nanomedicine, ISCIII, Hospital Universitari Valld’Hebron, Universitat Autònoma de Barcelona, 08035 Barcelona, Spain (e-mail: [raul.herance@vhir.org](raul.herance@vhir.org); [brunopaun@gmail.com](brunopaun@gmail.com); carolina.aparicio@  \n[vhir.org](vhir.org)).  \nVanesa Venegas and Mario Marotta are with the Health & Biomedicine Department, Leitat Technological Center, 08225 Barcelona, Spain, and also with the Bioengineering, Cell therapy and Surgery in Congenital Malformations Laboratory, Vall d’Hebron Research Institute, CIBBIMNanomedicine, Hospital Universitari Vall d’Hebron, Universitat Autònomade Barcelona, 08035 Barcelona, Spain ([e-mail: mmarotta@leitat.org](e-mail: mmarotta@leitat.org)).  \nMarc Masa is with the Health & Biomedicine Department, Leitat Technological Center, 08225 Barcelona, Spain ([e-mail: mmasa@leitat.org](e-mail: mmasa@leitat.org)).  \nThis article has supplementary material provided by the authors and color versions of one or more figures available at [https://doi.org/10.1109/](https://doi.org/10.1109/)[ ](https://doi.org/10.1109/)[TRPMS.2023.3291848.](TRPMS.2023.32","cbCail3ukxcEDr3m","https://ap.wps.com/l/cbCail3ukxcEDr3m","pdf",1507062,1,9,"English","en",105,"# Introduction\n## Radiomics concept and quantitative imaging features\n## From radiomics extraction to machine learning inputs","[{\"question\":\"What is the study trying to predict and in what setting?\",\"answer\":\"The study predicts the recovery progress of skeletal muscle injury over time in rats. Prediction targets the volume of the injury derived from radiomics-informed machine learning regression models.\"},{\"question\":\"How were radiomics features obtained for the machine learning models?\",\"answer\":\"Radiomics were extracted from contrast-enhanced computed tomography (CT) data. The models use these radiomics as input features to estimate injury recovery volume.\"},{\"question\":\"How do CT imaging parameters influence prediction performance?\",\"answer\":\"CT acquisition settings significantly affect predictive performance. Lower tube current and peak kilovoltage are associated with more accurate predictions in the reported results.\"}]","Radiomics and Machine Learning for Skeletal Muscle Injury Recovery Prediction | PDF",1785900922,23,{"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-and-machine-learning-for-skeletal-muscle-injury-recovery-prediction","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/radiomics-and-machine-learning-for-skeletal-muscle-injury-recovery-prediction/125734/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the study trying to predict and in what setting?","Question",{"text":75,"@type":76},"The study predicts the recovery progress of skeletal muscle injury over time in rats. Prediction targets the volume of the injury derived from radiomics-informed machine learning regression models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were radiomics features obtained for the machine learning models?",{"text":80,"@type":76},"Radiomics were extracted from contrast-enhanced computed tomography (CT) data. The models use these radiomics as input features to estimate injury recovery volume.",{"name":82,"@type":73,"acceptedAnswer":83},"How do CT imaging parameters influence prediction performance?",{"text":84,"@type":76},"CT acquisition settings significantly affect predictive performance. 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