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A precise, automated method to objectively quantify residual pancreatic cancer (RPC) has been lacking. An international team developed and validated an AI segmentation model using digitized whole-slide images from 14 centers across 7 countries, training a U-Net to detect RPC and performing by-scanner internal-external cross-validation, including 528 unique H&E slides.",{"@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/artificial-intelligence-based-segmentation-of-residual-pancreatic-cancer-in-resection-specimens-following-neoadjuvant-treatment-isgpp-2-international-improvement-and-validation-study/331148/",{"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/artificial-intelligence-based-segmentation-of-residual-pancreatic-cancer-in-resection-specimens-following-neoadjuvant-treatment-isgpp-2-international-improvement-and-validation-study/331148.png","ImageObject",300,407,{"name":92,"@type":93},"Ezra","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is automated segmentation of residual pancreatic cancer (RPC) needed after neoadjuvant treatment?","Question",{"text":112,"@type":113},"Pathologists assess residual disease in resection specimens to evaluate NAT effects, but an automated scoring system to objectively quantify RPC was not available. The study aims to address this lack with an AI-based quantification approach.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How was the AI segmentation model developed in this study?",{"text":117,"@type":113},"The model was trained on digitized histopathological tissue slides with annotated regions classified as cancer, non-neoplastic pancreatic ducts, and others. A U-Net architecture was used to detect RPC.",{"name":119,"@type":110,"acceptedAnswer":120},"How did the study validate the model across different scanner types?",{"text":121,"@type":113},"Validation used by-scanner internal-external cross-validation, training and testing across scanner categories. Mean F1 scores were reported separately for Philips, Hamamatsu, 3DHistech, and Leica scanners.","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},331148,1790203188,{"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":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144,"preview_page_count":4},1099514068035,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Artificial Intelligence-based Segmentation of Residual Pancreatic Cancer in Resection Specimens Following Neoadjuvant Treatment (ISGPP-2)  \nInternational Improvement and Validation Study  \nBoris V. Janssen, BSc,*†‡ Bart Oteman, BSc,*†‡ Mahsoem Ali, BSc,‡§ Pieter A. Valkema, MD,†‡ Volkan Adsay, MD,‖ Olca Basturk, MD,¶ Deyali Chatterjee, MD,\\# Angela Chou, PhD,**†† Stijn Crobach, MD, PhD,‡‡ Michael Doukas, MD, PhD,§§ Paul Drillenburg, MD, PhD,‖‖ Irene Esposito, MD,¶¶ Anthony J. Gill, MD,**†† Seung-Mo Hong, MD, PhD,\\#\\# Casper Jansen, MD, PhD,***††† Mike Kliffen, MD, PhD,‡‡‡ Anubhav Mittal, MD, PhD,§§§ Jas Samra, MD, PhD,††§§§ Marie-Louise F. van Velthuysen, MD, PhD,§§ Aslihan Yavas, MD,¶¶ Geert Kazemier, MD, PhD,‡§ Joanne Verheij, MD, PhD,†‡ Ewout Steyerberg, PhD,‖‖‖ Marc G. Besselink, MD, PhD, MSc,*‡ Huamin Wang, MD, PhD,\\# Caroline Verbeke, MD, PhD,¶¶¶\\#\\#\\# Arantza Fariña, MD, PhD,†‡ Onno J. de Boer, PhD,†‡ for the International Study Group of Pancreatic Pathologists (ISGPP), and the Pancreatobiliary and Hepatic Artificial Intelligence Research (PHAIR) consortium  \nAbstract: Neoadjuvant therapy (NAT) has become routine inpatients with borderline resectable pancreatic cancer. Pathologists examine pancreatic cancer resection specimens to evaluate the effect of NAT. However, an automated scoring system to objectively quantify residual pancreatic cancer (RPC) is currently lacking. Herein, we developed and validated the first automated segmentation model using artificial intelligence techniques to objectively quantify RPC. Digitized histopathological tissue slides were included from resected pancreatic cancer specimens from 14 centers in 7 countries in Europe, North America,  \nAustralia, and Asia. Four different scanner types were used: Philips (56%), Hamamatsu (27%), 3DHistech (10%), and Leica (7%) . Regions of interest were annotated and classified as cancer, non-neoplastic pancreatic ducts, and others. A U-Net model was trained to detect RPC. Validation consisted of by-scanner internal-external cross-validation. Overall, 528 unique hematoxylin and eosin (H & E) slides from 528 patients were included. In the individual Philips, Hamamatsu, 3DHistech, and Leica scanner cross-validations, mean F1 scores of 0.81 (95% CI, 0.77-0.84), 0.80 (0.78-0.83), 0.76 (0.65-0.78), and 0.71 (0.65-0.78) were achieved, respectively. In the meta-analysis of the cross-vali-  \nFrom the Departments of *Surgery; †Pathology, Amsterdam UMC, location University of Amsterdam; ‡Cancer Center Amsterdam; §Department of Surgery, Amsterdam UMC, location Vrije Universiteit;‖‖Department of Pathology, OLVG, Amsterdam; Departments of ‡‡Pathology; ‖‖‖Biomedical Data Sciences, Leiden University Medical Center, Leiden; §§Department of Pathology, Erasmus Medical Center; ‡‡‡Department of Pathology, Maasstad ziekenhuis, Rotterdam; ***Laboratorium Pathologie Oost-Nederland, Hengelo;†††Department of Pathology, Medisch Spectrum Twente, Enschede, The Netherlands; ‖Department of Pathology, Koc University and KUTTAM Research Center, Istanbul, Turkey; ¶Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY;  \n\\#Department of Anatomical Pathology, University of Texas MD Anderson Cancer Center, Houston, TX; **Cancer Diagnosis and Pathology Group, Kolling Institute of Medical Research, Royal North Shore Hospital, St Leonards, NSW, Australia; ††University of Sydney, Sydney, NSW, Australia; ¶¶Institute of Pathology, HeinrichHeine-University and University Hospital of Duesseldorf, Duesseldorf, Germany; \\#\\#Department of Pathology, Asan Medical Center, Seoul, Republic of Korea; §§§Department of Surgery of Medical Research, Royal North Shore Hospital, St Leonards, NSW, Australia;  \n¶¶¶Department of Pathology, Institute of Clinical Medicine, University of Oslo; and \\#\\#\\#Department of Pathology, Oslo University Hospital, Oslo, Norway.  \nA.F. and O.J.d.B. share senior authorship.  \nConflicts of Interest and Source of Funding: The authors have disclosed that they have no ","cbCaibN5RryLi4kE","https://ap.wps.com/l/cbCaibN5RryLi4kE","pdf",740041,"English","# Abstract\n## Background and Need\n## Model Development\n## Validation Strategy\n## Performance Results\n## Implications","[{\"question\":\"Why is automated segmentation of residual pancreatic cancer (RPC) needed after neoadjuvant treatment?\",\"answer\":\"Pathologists assess residual disease in resection specimens to evaluate NAT effects, but an automated scoring system to objectively quantify RPC was not available. The study aims to address this lack with an AI-based quantification approach.\"},{\"question\":\"How was the AI segmentation model developed in this study?\",\"answer\":\"The model was trained on digitized histopathological tissue slides with annotated regions classified as cancer, non-neoplastic pancreatic ducts, and others. A U-Net architecture was used to detect RPC.\"},{\"question\":\"How did the study validate the model across different scanner types?\",\"answer\":\"Validation used by-scanner internal-external cross-validation, training and testing across scanner categories. Mean F1 scores were reported separately for Philips, Hamamatsu, 3DHistech, and Leica scanners.\"}]","Artificial Intelligence-based Segmentation of Residual Pancreatic Cancer in Resection Specimens Following Neoadjuvant Treatment (ISGPP-2) - International Improvement and Validation Study | PDF",1789991952,23]