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This study evaluates agreement between two open-source radiomics software tools for contrast-enhanced CT and contrast-enhanced MRI in lung cancer patients, aiming to identify radiomic features stable across imaging modalities. Thirty-five non-small cell lung cancer cases were segmented and preprocessed with three methods, and Image Biomarker Standardisation Initiative-compliant features were extracted and compared across platforms and techniques.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/ct-and-mri-radiomic-features-of-lung-cancer-nsclc-comparison-and-software-consistency/332153/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/ct-and-mri-radiomic-features-of-lung-cancer-nsclc-comparison-and-software-consistency/332153.png","ImageObject",300,407,{"name":92,"@type":93},"Violet","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the goal of comparing CT and MRI radiomic features in this study?","Question",{"text":112,"@type":113},"The study evaluates agreement between two open-source radiomics software tools for contrast-enhanced CT and contrast-enhanced MRI and explores whether certain radiomic features remain stable across both imaging techniques.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were the CT and MRI images processed and analyzed?",{"text":117,"@type":113},"Contrast-enhanced CT and MRI images from 35 NSCLC patients were manually segmented and preprocessed using three methods, and 66 IBSI-compliant features common to the two platforms (PyRadiomics and LIFEx) were extracted for comparison.",{"name":119,"@type":110,"acceptedAnswer":120},"What did the results show about software agreement and cross-modality stability?",{"text":121,"@type":113},"LIFEx and PyRadiomics showed maximum statistically significant correlations around 94% for CT features and 95% for MRI features, while cross-modality comparisons using the same software indicated higher significant correspondence for about 11% of features, with overall features largely non-redundant but some shape and texture features stable across modalities.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},332153,1790168062,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":41,"preview_page_count":4},4398048950312,"https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908","Bortolotto et al.  \nEuropean Radiology Experimental (2024) 8:71 [https://doi.org/10.1186/s41747-024-00468-8](https://doi.org/10.1186/s41747-024-00468-8)  \nORIGINAL ARTICLE Open Access  \nCT and MRI radiomic features of lung cancer  (NSCLC): comparison and software consistency  \nChandra Bortolotto 1,2, Alessandra Pinto 1*, Francesca Brero3,4, Gaia Messana 1, Raffaella Fiamma Cabini4,5*, Ian Postuma4, Agnese Robustelli Test3,4* , Giulia Maria Stella6,7, Giulia Galli6,7, Manuel Mariani3, Silvia Figini8, Alessandro Lascialfari3,4, Andrea Riccardo Filippi2,9, Olivia Maria Bottinelli2 and Lorenzo Preda 1,2  \nAbstract  \nBackground Radiomics is a quantitative approach that allows the extraction of mineable data from medical images. Despite the growing clinical interest, radiomics studies are affected by variability stemming from analysis choices. We aimed to investigate the agreement between two open-source radiomics software for both contrast-enhanced computed tomography (CT) and contrast-enhanced magnetic resonance imaging (MRI) of lung cancers and to preliminarily evaluate the existence of radiomic features stable for both techniques.  \nMethods Contrast-enhanced CT and MRI images of 35 patients affected with non-small cell lung cancer (NSCLC) were manually segmented and preprocessed using three different methods. Sixty-six Image Biomarker Standardisation Initiative-compliant features common to the considered platforms, PyRadiomics and LIFEx, were extracted. The correlation among features with the same mathematical definition was analyzed by comparing PyRadiomicsand LIFEx (at fixed imaging technique), and MRI with CT results (for the same software) .  \nResults When assessing the agreement between LIFEx and PyRadiomics across the considered resampling, the maximum statistically significant correlations were observed to be 94% for CT features and 95% for MRI ones. When examining the correlation between features extracted from contrast-enhanced CT and MRI using the same software, higher significant correspondences were identified in 11% of features for both software.  \nConclusions Considering NSCLC,(i) for both imaging techniques, LIFEx and PyRadiomics agreed on average for 90% of features, with MRI being more affected by resampling and (ii) CT and MRI contained mostly non-redundant information, but there are shape features and, more importantly, texture features that can be singled out by both techniques.  \nRelevance statement Identifying and selecting features that are stable cross-modalities may be one of the strategies to pave the way for radiomics clinical translation.  \nKey points  \n• More than 90% of LIFEx and PyRadiomics features contain the same information.  \n• Ten percent of features (shape, texture) are stable among contrast-enhanced CT and MRI.  \nChandra Bortolotto and Francesca Brero contributed equally to this work.  \n*Correspondence:  \nAlessandra Pinto  \nalessandra.pinto01@universitadipavia.it Raffaella Fiamma Cabini  \nraffaellafiamm.cabini01@universitadipavia.it Agnese Robustelli Test  \nagnese. robustellitest01@universitadipavia.it  \nFull list of author information is available at the end of the article  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [h","cbCaiiUnbf0Z496G","https://ap.wps.com/l/cbCaiiUnbf0Z496G","pdf",1745499,12,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusions\n## Relevance statement\n## Key points\n# Keywords\n# Graphical Abstract\n## Background","[{\"question\":\"What is the goal of comparing CT and MRI radiomic features in this study?\",\"answer\":\"The study evaluates agreement between two open-source radiomics software tools for contrast-enhanced CT and contrast-enhanced MRI and explores whether certain radiomic features remain stable across both imaging techniques.\"},{\"question\":\"How were the CT and MRI images processed and analyzed?\",\"answer\":\"Contrast-enhanced CT and MRI images from 35 NSCLC patients were manually segmented and preprocessed using three methods, and 66 IBSI-compliant features common to the two platforms (PyRadiomics and LIFEx) were extracted for comparison.\"},{\"question\":\"What did the results show about software agreement and cross-modality stability?\",\"answer\":\"LIFEx and PyRadiomics showed maximum statistically significant correlations around 94% for CT features and 95% for MRI features, while cross-modality comparisons using the same software indicated higher significant correspondence for about 11% of features, with overall features largely non-redundant but some shape and texture features stable across modalities.\"}]","CT and MRI radiomic features of lung cancer (NSCLC) - comparison and software consistency | PDF",1789997079]