[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124821-en":3,"doc-seo-124821-105":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},124821,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Radiomics-based machine learning approach for the prediction of grade and stage in upper urinary tract urothelial carcinoma - a step towards virtual biopsy","Machine learning models based on radiomics features are developed to predict tumor grade and stage in upper tract urothelial carcinoma using protocol-based CT urogram data. Three-dimensional visualization and digital segmentation of tumors are used to extract textural radiomics features, which are then classified with 11 predictive models and evaluated against histopathological findings from radical nephroureterectomy specimens. The reported performance includes 84% sensitivity and 93% specificity for grading and an 83% sensitivity and 76% specificity for staging, supporting a virtual biopsy direction for clinical decision-making.","University of Dundee  \nRadiomics-based machine learning approach for the prediction of grade and stage in upper urinary tract urothelial carcinoma  \nAlqahtani, Abdulsalam; Bhattacharjee, Sourav; Almopti, Abdulrahman; Li, Chunhui; Nabi, Ghulam  \nDOI:  \n10.1097/JS9.0000000000001483  \nPublication date:  \n2024  \nLicence: CC BY  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication in Discovery Research Portal  \nCitation for published version (APA):  \nAlqahtani, A. , Bhattacharjee, S. , Almopti, A. , Li, C. , & Nabi, G. (2024) . Radiomics-based machine learning approach for the prediction of grade and stage in upper urinary tract urothelial carcinoma: a step towards virtual biopsy. International journal of surgery (London, England) , 110(6), 3258-3268.  \n[https://doi.org/10.1097/JS9.0000000000001483](https://doi.org/10.1097/JS9.0000000000001483)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in Discovery Research Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 04. Jul. 2024  \nDownloaded from [http://journals.lww.com/international-journal-of-surgery by BhDMf5ePHKav1zEoum1tQfN4](http://journals.lww.com/international-journal-of-surgery by BhDMf5ePHKav1zEoum1tQfN4)[ ](http://journals.lww.com/international-journal-of-surgery by BhDMf5ePHKav1zEoum1tQfN4)a+kJLh EZgbs I Ho4XMi 0hCywCX 1AWnYQp/ I l Qr HD3i 3 D0OdRyi7TvSFl4Cf3VC1y0abggQZXdgGj2MwlZLeI= on 06/12/2024  \n’Diagnostic Study  \nRadiomics-based machine learning approach for the prediction of grade and stage in upper urinary tract urothelial carcinoma: a step towards virtual biopsy  \nAbdulsalam Alqahtani, MSca,d , Sourav Bhattacharjee, MBBS, PhDc , Abdulrahman Almopti, MSca , Chunhui Li, PhDb , Ghulam Nabi, PhDa,*  \nObjectives: Upper tract urothelial carcinoma (UTUC) is a rare, aggressive lesion, with early detection a key to its management. This study aimed to utilise computed tomographic urogram data to develop machine learning models for predicting tumour grading and staging in upper urothelial tract carcinoma patients and to compare these predictions with histopathological diagnosis used as reference standards.  \nMethods: Protocol-based computed tomographic urogram data from 106 patients were obtained and visualised in 3D. Digital segmentation of the tumours was conducted by extracting textural radiomics features. They were further classified using 11 predictive models. The predicted grades and stages were compared to the histopathology of radical nephroureterectomy specimens. Results: Classifier models worked well in mining the radiomics data and delivered satisfactory predictive machine learning models. The multilayer panel showed 84% sensitivity and 93% specificity while predicting UTUC grades. The Logistic Regression model showed a sensitivity of 83% and a specificity of 76% while staging. Similarly, other classifier algorithms [e.g. Support Vector classifier (SVC)] provided a highly accurate prediction while grading UTUC compared to clinical features alone or ureteroscopic biopsy histopathology.  \nConclusion: Data mining tools could handle medical imaging datasets from small ( \u003C 2 cm) tumours for UTUC. The radiomics-based machine learning algorithms provide a potential tool to model tumour grading and staging with implications for clinical practice and the upgradation of current paradigms in cancer diagnostics.  \nClinical Relevance: Machine learning based on radiomics features can predict upper tract urothelial cancer grading and staging with significant improvement over ureteroscopic histopathology. The study showcased the prowess of such emerging tools in th","cbCaidt5xAkg7Dkz","https://ap.wps.com/l/cbCaidt5xAkg7Dkz","pdf",1206035,1,12,"English","en",105,"# Objectives\n# Methods\n## Data acquisition and segmentation\n## Model training and classification\n# Results\n# Conclusion\n# Clinical Relevance\n# Keywords\n# Introduction","[{\"question\":\"What is the goal of using radiomics-based machine learning in this study?\",\"answer\":\"The study aims to predict UTUC tumor grade and stage from CT urogram data and compare model predictions with histopathology as the reference standard.\"},{\"question\":\"How were the model inputs generated for classification?\",\"answer\":\"CT urogram data from 106 patients were visualized in 3D, tumors were digitally segmented, and textural radiomics features were extracted for model training.\"},{\"question\":\"What predictive performance did the models show?\",\"answer\":\"For grading, a multilayer panel achieved 84% sensitivity and 93% specificity. For staging, Logistic Regression reported 83% sensitivity and 76% specificity, with other classifiers also providing accurate predictions.\"}]","Radiomics-based machine learning approach for the prediction of grade and stage in upper urinary tract urothelial carcinoma - a step towards virtual biopsy | PDF",1785894832,30,{"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-approach-for-the-prediction-of-grade-and-stage-in-upper-urinary-tract-urothelial-carcinoma-a-step-towards-virtual-biopsy","",{"@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-based-machine-learning-approach-for-the-prediction-of-grade-and-stage-in-upper-urinary-tract-urothelial-carcinoma-a-step-towards-virtual-biopsy/124821/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the goal of using radiomics-based machine learning in this study?","Question",{"text":75,"@type":76},"The study aims to predict UTUC tumor grade and stage from CT urogram data and compare model predictions with histopathology as the reference standard.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the model inputs generated for classification?",{"text":80,"@type":76},"CT urogram data from 106 patients were visualized in 3D, tumors were digitally segmented, and textural radiomics features were extracted for model training.",{"name":82,"@type":73,"acceptedAnswer":83},"What predictive performance did the models show?",{"text":84,"@type":76},"For grading, a multilayer panel achieved 84% sensitivity and 93% specificity. For staging, Logistic Regression reported 83% sensitivity and 76% specificity, with other classifiers also providing accurate predictions.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"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"]