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Compute TSR from segmentations and assess its association with six-month survival. Results: Report segmentation performance using Dice metrics and evaluate TSR prognostic utility via AUC. Conclusion: Findings support feasibility of computational biomarker discovery for patient outcome prediction and personalized management.",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/automatic-quantification-of-tumor-stroma-ratio-as-a-prognostic-marker-for-pancreatic-cancer/333390/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/automatic-quantification-of-tumor-stroma-ratio-as-a-prognostic-marker-for-pancreatic-cancer/333390.png","ImageObject",300,407,{"name":42,"@type":43},"Aurora","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-23","2026-09-21",true,{"@type":52,"interactionType":53,"userInteractionCount":33},"InteractionCounter",{"@type":54},"ViewAction",{"@type":56,"mainEntity":57},"FAQPage",[58,64,68],{"name":59,"@type":60,"acceptedAnswer":61},"What is the goal of the study on tumor-stroma ratio (TSR)?","Question",{"text":62,"@type":63},"The study introduces an automatic multi-step pipeline to quantify TSR as a prognostic marker for pancreatic cancer, aiming to address shortcomings of current staging and the lack of widely used prognostic biomarkers.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"How does the proposed method quantify TSR?",{"text":67,"@type":63},"The approach uses deep learning to segment tumor epithelial cells, tumor bulk, and stroma from whole-slide images. TSR is then computed from these segmented components.",{"name":69,"@type":60,"acceptedAnswer":70},"How was the model evaluated and what outcome was tested?",{"text":71,"@type":63},"Models were trained with five-fold cross-validation and evaluated on an independent external test set. The predictive value of TSR for six-month survival on the external dataset was assessed.","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},333390,1790688010,{"code":4,"msg":81,"data":82},"success",[83,87,91,95,100,105,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":84,"show_sort_weight":85,"slug":86},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":88,"show_sort_weight":89,"slug":90},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":92,"show_sort_weight":93,"slug":94},"Exam",70,"exam",{"id":96,"doc_module":4,"doc_module_name":25,"category_name":97,"show_sort_weight":98,"slug":99},5,"Comic",60,"comic",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},6,"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":107,"show_sort_weight":108,"slug":109},7,"Healthcare",40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":112,"slug":113},8,30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":96,"slug":129},19,"General","general",{"code":4,"msg":81,"data":131},{"doc_id":78,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":111,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":33,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":145,"preview_page_count":4},4810365810221,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","PLOS ONE  \nOPEN ACCESS  \nCitation: Vendittelli P, Bokhorst J-M, Smeets EMM, Kryklyva V, Brosens LAA, Verbeke C, et al. (2024) Automatic quantification of tumor-stroma ratio as a prognostic marker for pancreatic cancer. PLoSONE 19(5): e0301969 . [https://doi.org/10.1371/](https://doi.org/10.1371/)[ ](https://doi.org/10.1371/)[journal.pone.0301969](journal.pone.0301969)  \nEditor: Shuai Ren, Affiliated Hospital of Nanjing University of Chinese Medicine: Jiangsu Province Academy of Traditional Chinese Medicine, CHINA  \nReceived: October 16, 2023  \nAccepted: March 26, 2024  \nPublished: May 21, 2024  \nPeer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here:  \n[https://doi.org/10.1371/journal.pone.0301969](https://doi.org/10.1371/journal.pone.0301969)  \n[Copyright:](Copyright:) © [2024 Vendittelli](2024 Vendittelli) et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: Data from dataset AB and C cannot be shared publicly and without limitation because of the type of agreement signed,  \nRESEARCH ARTICLE  \nAutomatic quantification of tumor-stroma ratio as a prognostic marker for pancreatic cancer  \nPierpaolo Vendittelli1 *, John-Melle Bokhorst1, Esther M. M. Smeets1,  \nValentyna Kryklyva1, Lodewijk A. A. Brosens1, Caroline Verbeke2, Geert Litjens1  \n1 Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands, 2 Department of Pathology, Oslo University Hospital, Oslo, Norway  \n* [pierpaolo.vendittelli@radboudumc.nl](pierpaolo.vendittelli@radboudumc.nl)  \nAbstract  \nPurpose  \nThis study aims to introduce an innovative multi-step pipeline for automatic tumor-stroma ratio (TSR) quantification as a potential prognostic marker for pancreatic cancer, addressing the limitations of existing staging systems and the lack of commonly used prognostic biomarkers.  \nMethods  \nThe proposed approach involves a deep-learning-based method for the automatic segmentation of tumor epithelial cells, tumor bulk, and stroma from whole-slide images (WSIs) . Models were trained using five-fold cross-validation and evaluated on an independent external test set. TSR was computed based on the segmented components. Additionally, TSR’s predictive value for six-month survival on the independent external dataset was assessed.  \nResults  \nMedian Dice (inter-quartile range (IQR)) of 0 .751(0 . 15) and 0 .726(0 .25) for tumor epithelium segmentation on internal and external test sets, respectively. Median Dice of 0 .76(0 . 11) and 0.863(0 . 17) for tumor bulk segmentation on internal and external test sets, respectively. TSR was evaluated as an independent prognostic marker, demonstrating a cross-validation AUC of 0.61±0.12 for predicting six-month survival on the external dataset.  \nConclusion  \nOur pipeline for automatic TSR quantification offers promising potential as a prognostic marker for pancreatic cancer. The results underscore the feasibility of computational biomarker discovery in enhancing patient outcome prediction, thus contributing to personalized patient management.  \nbut researchers who meet the criteria for access to confidential data could access the data upon request. All Data from dataset D are available from the National Cancer Institute database (accession number phs000178, link [https://portal.gdc.cancer](https://portal.gdc.cancer). gov/projects/TCGA-PAAD) . Requests about data access can be sent to Valerie Dechering at: valerie. [dechering@radboudumc.nl](dechering@radboudumc.nl).  \nFunding: P.V. has received funding from the European Union’s Horizon 2020 research and innovation progr","cbCaioiG8jn0Py1A","https://ap.wps.com/l/cbCaioiG8jn0Py1A","pdf",4542283,13,"English","# Abstract\n## Purpose\n## Methods\n## Results\n## Conclusion\n# Introduction\n## Clinical need for prognostic biomarkers in pancreatic ductal adenocarcinoma\n## Limitations of histopathology and TNM staging","[{\"question\":\"What is the goal of the study on tumor-stroma ratio (TSR)?\",\"answer\":\"The study introduces an automatic multi-step pipeline to quantify TSR as a prognostic marker for pancreatic cancer, aiming to address shortcomings of current staging and the lack of widely used prognostic biomarkers.\"},{\"question\":\"How does the proposed method quantify TSR?\",\"answer\":\"The approach uses deep learning to segment tumor epithelial cells, tumor bulk, and stroma from whole-slide images. TSR is then computed from these segmented components.\"},{\"question\":\"How was the model evaluated and what outcome was tested?\",\"answer\":\"Models were trained with five-fold cross-validation and evaluated on an independent external test set. The predictive value of TSR for six-month survival on the external dataset was assessed.\"}]","Automatic quantification of tumor-stroma ratio as a prognostic marker for pancreatic cancer | PDF",1790002226,33]