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Uses a prospectively maintained surgical database of resected IPMN patients with 18 demographic, clinical, and imaging inputs. Trained and validated on a 4:1 split, then tested with receiver operating characteristics. Reports 77.4% accuracy and 83% sensitivity with 72% specificity for low-grade prediction, suggesting complementarity to existing guidelines.",{"@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/ipmn-learn-a-linear-support-vector-machine-learning-model-for-predicting-low-grade-intraductal-papillary-mucinous-neoplasms/381144/",{"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/ipmn-learn-a-linear-support-vector-machine-learning-model-for-predicting-low-grade-intraductal-papillary-mucinous-neoplasms/381144.png","ImageObject",300,407,{"name":92,"@type":93},"Miles","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does IPMN-LEARN address in IPMN management?","Question",{"text":112,"@type":113},"It targets the difficulty of distinguishing low-risk from high-risk IPMNs to reduce unnecessary surgical resection, which carries morbidity and potential mortality.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How was the machine learning model built and evaluated?",{"text":117,"@type":113},"A linear SVM model was trained using a prospectively maintained surgical database. Eighteen demographic, clinical, and imaging variables were used, and performance was assessed with receiver operating characteristics on a testing setup with a 4:1 training/validation-to-testing split.",{"name":119,"@type":110,"acceptedAnswer":120},"What performance did the model achieve for predicting low-grade disease?",{"text":121,"@type":113},"On the validation set, the model achieved 77.4% accuracy, 83% sensitivity, 72% specificity, and a positive predictive value of 83%, with an area under the curve of 0.82 for low-grade prediction.","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},381144,1791273963,{"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":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":29,"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},13056703019404,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","pISSN: 2508-5778 ㆍeISSN: 2508-5859  \nAnn Hepatobiliary Pancreat Surg 2023;27:195-200 [https://doi.org/10.14701/ahbps.22-107](https://doi.org/10.14701/ahbps.22-107)  \nAHBPS  \nAnnals of Hepato-Biliary-Pancreatic Surgery  \nOriginal Article  \nIPMN-LEARN: A linear support vector machine learning model for predicting low-grade intraductal papillary mucinous neoplasms  \nYasmin Genevieve Hernandez-Barco1,*, Dania Daye2,*, Carlos F. Fernandez-del Castillo3, Regina F. Parker4, Brenna W. Casey2, Andrew L. Warshaw3, Cristina R. Ferrone3, Keith D. Lillemoe3, Motaz Qadan3  \n1Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, United States,  \n2Division of Radiology, Massachusetts General Hospital, Boston, MA, United States, 3Department of Surgical Oncology, Massachusetts General Hospital, Boston, MA, United States, 4Harvard Medical School, Boston, MA, United States  \nBackgrounds/Aims: We aimed to build a machine learning tool to help predict low-grade intraductal papillary mucinous neoplasms (IPMNs) in order to avoid unnecessary surgical resection. IPMNs are precursors to pancreatic cancer. Surgical resection remains the only recognized treatment for IPMNs yet carries some risks of morbidity and potential mortality. Existing clinical guidelines are imperfect in distinguishing low-risk cysts from high-risk cysts that warrant resection.  \nMethods: We built a linear support vector machine (SVM) learning model using a prospectively maintained surgical database of patients with resected IPMNs. Input variables included 18 demographic, clinical, and imaging characteristics. The outcome variable was the presence of low-grade or high-grade IPMN based on post-operative pathology results. Data were divided into a training/validation set and a testing set at a ratio of 4:1. Receiver operating characteristics analysis was used to assess classification performance.  \nResults: A total of 575 patients with resected IPMNs were identified. Of them, 53.4% had low-grade disease on final pathology. After classifier training and testing, a linear SVM-based model (IPMN-LEARN) was applied on the validation set. It achieved an accuracy of 77.4%, with a positive predictive value of 83%, a specificity of 72%, and a sensitivity of 83% in predicting low-grade disease in patients with IPMN. The model predicted low-grade lesions with an area under the curve of 0.82.  \nConclusions: A linear SVM learning model can identify low-grade IPMNs with good sensitivity and specificity. It may be used as a complement to existing guidelines to identify patients who could avoid unnecessary surgical resection.  \nKey Words: Intraductal papillary mucinous neoplasm; Machine learning; Pancreatic neoplasm; Pancreatic cyst  \nReceived: November 7, 2022, Revised: January 7, 2023, Accepted: January 16, 2023, Published online: April 3, 2023  \nCorresponding author: Motaz Qadan, MD, PhD  \nDepartment of Surgical Oncology, Massachusetts General Hospital, 55 Fruit Street, Wang 460, Boston, MA 02114, United States  \nTel: +1-617-643-5153, Fax: +1-617-643-5274, [E-mail: mqadan@mgh.harvard.edu](E-mail: mqadan@mgh.harvard.edu)[ ](E-mail: mqadan@mgh.harvard.edu)ORCID: [https://orcid.org/0000-0001-6951-8308](https://orcid.org/0000-0001-6951-8308)  \n*These authors contributed equally to this study.  \nCopyright Ⓒ The Korean Association of Hepato-Biliary-Pancreatic Surgery  \nThis is an Open Access article distributed under the terms of the Creative Commons Attri  \nbution Non-Commercial License ([http://creativecommons.org/licenses/by-nc/4.0](http://creativecommons.org/licenses/by-nc/4.0)) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.  \nINTRODUCTION  \nIntraductal papillary mucinous neoplasms (IPMN) is a known precursor to pancreatic ductal adenocarcinoma, the third leading cause of cancer death with an overall five-year survival of 10%[1]. It has been estimated that up to 49.1% of individuals who h","cbCaioHZktEo7Cjc","https://ap.wps.com/l/cbCaioHZktEo7Cjc","pdf",576309,"English","# Background/Aims\n# Methods\n# Results\n# Conclusions\n# Introduction","[{\"question\":\"What problem does IPMN-LEARN address in IPMN management?\",\"answer\":\"It targets the difficulty of distinguishing low-risk from high-risk IPMNs to reduce unnecessary surgical resection, which carries morbidity and potential mortality.\"},{\"question\":\"How was the machine learning model built and evaluated?\",\"answer\":\"A linear SVM model was trained using a prospectively maintained surgical database. Eighteen demographic, clinical, and imaging variables were used, and performance was assessed with receiver operating characteristics on a testing setup with a 4:1 training/validation-to-testing split.\"},{\"question\":\"What performance did the model achieve for predicting low-grade disease?\",\"answer\":\"On the validation set, the model achieved 77.4% accuracy, 83% sensitivity, 72% specificity, and a positive predictive value of 83%, with an area under the curve of 0.82 for low-grade prediction.\"}]","IPMN-LEARN - A linear support vector machine learning model for predicting low-grade intraductal papillary mucinous neoplasms | PDF",1790243786,15]