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A multicenter cohort of 243 patients with SR reconstruction enabled feature extraction from SR-DWI and SR-T2WI tumor, peri-3mm, and peri-5mm regions. LASSO and mRMR selected features, and logistic regression built DWI_T2WI_INTRA, DWI_T2WI_IntraPeri3mm, and DWI_T2WI_InterPeri5mm models, integrating an independent clinical risk-factor model for improved discrimination and calibration.",{"@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":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/generative-adversarial-networks-multiparametric-multiregion-superresolution-mri-in-predicting-lymph-node-metastasis-in-rectal-cancer/443413/",{"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/generative-adversarial-networks-multiparametric-multiregion-superresolution-mri-in-predicting-lymph-node-metastasis-in-rectal-cancer/443413.png","ImageObject",300,407,{"name":92,"@type":93},"Levi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the study objective?","Question",{"text":112,"@type":113},"To develop a preoperative mesorectal lymph node metastasis prediction model for rectal cancer using intratumoral and multiregional peritumoral radiomics from super-resolution multiparametric MRI.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were radiomic features extracted and selected?",{"text":117,"@type":113},"Radiomic features were extracted from tumor, peri-3mm, and peri-5mm regions on SR-DWI and SR-T2WI images, then selected using LASSO and mRMR for dimensionality reduction.",{"name":119,"@type":110,"acceptedAnswer":120},"Which model performed best for lymph node metastasis prediction?",{"text":121,"@type":113},"The DWI_T2WI_IntraPeri3mm radiomic model showed the optimal predictive performance across training, validation, and test cohorts, and the combined model integrating radiomics with independent clinical risk factors achieved the highest AUC values.","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},443413,1791096046,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"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":145,"preview_page_count":4},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Wu et al. Insights into Imaging (2026)17:1 [https://doi.org/10.1186/s13244-025-02173-5](https://doi.org/10.1186/s13244-025-02173-5)  \nORIGINAL ARTICLE Open Access  \nGenerative adversarial networks: multiparametric, multiregion superresolution MRI in predicting lymph node metastasis in rectal cancer  \nYupeng Wu 1, Tao Jiang2, Han Liu 1, Shengming Shi 1, Apekshya Singh 1, Yuhang Wang 1, Jiayi Xie 1 and Xiaofu Li 1*   \nAbstract  \nObjective Development of a preoperative mesorectal lymph node metastasis (LNM) prediction model for rectal cancer (RC) based on intratumoral and multiregional peritumoral radiomics features extracted from super-resolution multiparametric MRI.  \nMaterials and methods This multicenter study included preoperative MRI data from 243 rectal cancer patients (194 from center A, 49 from center B) with SR reconstruction and scoring. Radiomic features were extracted from tumor, peri-3mm and peri-5mm on SR-DWI and SR-T2WI images. The least absolute shrinkage and selection operator (LASSO) and the maximum relevance minimum redundancy (mRMR) were used for feature selection and dimensionality reduction. DWI_T2WI_INTRA, DWI_T2WI_IntraPeri3mm, DWI_T2WI_InterPeri5mm models were developed employing Logistic regression. Independent clinical risk factors identified through univariate and multivariate stepwise regression analyses were used to construct a clinical model. The optimal IntraPeri model integrated with clinical model design the combined model. Predictive performance was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA) .  \nResults Qualitative evaluation demonstrated superior scores for SR-T2WI across five metrics compared to original images (all p \u003C 0 . 001) . For DWI, SR images achieved significant improvements in all parameters (p \u003C 0 . 001), except lesion conspicuity [median (IQR): 3 (1) vs. 3 (1)] . Comparative analysis revealed the DWI_T2WI_IntraPeri3mm model’s optimal predictive performance in training, validation, and test cohorts (AUCs: 0.880, 0.735, and 0.714, respectively). The AUC of the combined model, integrating radiomic (DWI_T2WI_IntraPeri3mm) model with clinical risk factors, was 0. 933, 0 . 829, and 0 . 867 in each cohort, all exceeding those of the clinical and radiomic models.  \nConclusion Using GANs-based 3D-SR of multi-sequence MRI, our multiregional prediction model for preoperative mesorectal LNM in RC demonstrated good diagnostic performance.  \nCritical relevance statement The integration of super-resolution-based tumor and peritumoral 3-mm predictive model with clinical risk factors enables performance in predicting mesorectal LNM, potentially aiding clinical therapeutic decision-making.  \n*Correspondence: Xiaofu Li  \n[drxiaofuli@hrbmu.edu.cn](drxiaofuli@hrbmu.edu.cn)  \n1Department of Magnetic Resonance Imaging Diagnostic, The Second Affiliated Hospital of Harbin Medical University, Harbin, China  \n2Medical Imaging Department (MRI), The Fifth Affiliated Hospital of Harbin Medical University, Daqing, China  \n© The Author(s) 2025 . 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 [http://creativecommons.org/](http://creativecommons.org/)[ ](http://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/.)[.](licenses/by/4","cbCaioZoKuG0fMdP","https://ap.wps.com/l/cbCaioZoKuG0fMdP","pdf",1717488,15,"English","# Abstract\n# Objective\n# Materials and methods\n## Study cohort\n## Radiomic feature extraction and selection\n## Model building and evaluation\n# Results\n# Conclusion\n# Critical relevance statement\n# Key Points\n# Introduction","[{\"question\":\"What is the study objective?\",\"answer\":\"To develop a preoperative mesorectal lymph node metastasis prediction model for rectal cancer using intratumoral and multiregional peritumoral radiomics from super-resolution multiparametric MRI.\"},{\"question\":\"How were radiomic features extracted and selected?\",\"answer\":\"Radiomic features were extracted from tumor, peri-3mm, and peri-5mm regions on SR-DWI and SR-T2WI images, then selected using LASSO and mRMR for dimensionality reduction.\"},{\"question\":\"Which model performed best for lymph node metastasis prediction?\",\"answer\":\"The DWI_T2WI_IntraPeri3mm radiomic model showed the optimal predictive performance across training, validation, and test cohorts, and the combined model integrating radiomics with independent clinical risk factors achieved the highest AUC values.\"}]","Generative adversarial networks: multiparametric, multiregion superresolution MRI in predicting lymph node metastasis in rectal cancer | PDF",1790703938,38]