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Artificial Intelligence (AI) offers a promising approach by integrating high-dimensional multimodal data, including clinical history, radiomics, and pathology, to improve survival predictions and enable more personalized care. Current barriers remain in routine adoption, driven by reliance on limited retrospective cohorts without external validation and by unresolved issues around algorithmic transparency and ethical oversight. This review assesses the state of AI prognostic modeling and the pathway to dependable clinical implementation.",{"@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/predicting-the-unpredictable-ai-driven-prognosis-in-pancreatic-neuroendocrine-neoplasms/354941/",{"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/predicting-the-unpredictable-ai-driven-prognosis-in-pancreatic-neuroendocrine-neoplasms/354941.png","ImageObject",300,407,{"name":92,"@type":93},"Lucas Martin","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-09-22",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},"Why are accurate risk profiling and treatment standardization difficult in Pan-NENs?","Question",{"text":112,"@type":113},"Pan-NENs show intrinsic variability and strong biological and clinical heterogeneity, leading to challenges in consistent risk profiling and protocol standardization.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does AI aim to improve prognostic predictions for Pan-NEN patients?",{"text":117,"@type":113},"AI tools integrate and process high-dimensional multimodal datasets, such as clinical history, radiomics, and pathology, to refine survival forecasts and support personalized medicine.",{"name":119,"@type":110,"acceptedAnswer":120},"What limits the routine clinical adoption of AI-driven prognostic models?",{"text":121,"@type":113},"Adoption is hindered by the use of limited retrospective cohorts without external validation, alongside unresolved concerns about algorithmic transparency and ethical governance.","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},354941,1790759949,{"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},8796095360427,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","Review  \nPredicting the Unpredictable: AI-Driven Prognosis in Pancreatic Neuroendocrine Neoplasms  \nElettra Merola 1, *, Emanuela Pirino 1, Stefano Marcucci 2, Franca Chierichetti 3, Andrea Michielan 4, Laura Bernardoni 4, Armando Gabbrielli 4,5, Maria Pina Dore 1,6, Giuseppe Fanciulli 1  \nand Alberto Brolese 2  \nAcademic Editor: Kay-Uwe Wagner  \nReceived: 16 December 2025  \nRevised: 14 January 2026  \nAccepted: 16 January 2026  \nPublished: 19 January 2026  \nCopyright: © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.  \n1 Dipartimento di Medicina, Chirurgia e Farmacia, University of Sassari, Viale San Pietro 43, 07100 Sassari, Italy; [mpdore@uniss.it](mpdore@uniss.it) (M.P.D.); [gfanciu@uniss.it](gfanciu@uniss.it) (G.F.)  \n2 Hepato-Pancreato-Biliary (HPB) Unit, Department of General Surgery, Santa Chiara Hospital, Azienda Provinciale per i Servizi Sanitari (APSS), 38122 Trento, Italy; [stefano.marcucci@apss.tn.it](stefano.marcucci@apss.tn.it) (S.M.); [alberto.brolese@apss.tn.it](alberto.brolese@apss.tn.it) (A.B.)  \n3 Department of Nuclear Medicine, Santa Chiara Hospital, Azienda Provinciale per i Servizi Sanitari (APSS), 38122 Trento, Italy  \n4 Gastroenterology and Digestive Endoscopy Unit, Santa Chiara Hospital, Azienda Provinciale per i Servizi  \nSanitari (APSS), 38122 Trento, Italy; [andrea.michielan@apss.tn.it](andrea.michielan@apss.tn.it) (A.M.); [laura.bernardoni@apss.tn.it](laura.bernardoni@apss.tn.it) (L.B.); [armando.gabbrielli@apss.tn.it](armando.gabbrielli@apss.tn.it) (A.G.)  \n5 Center for Medical Sciences (CISMed), University of Trento, 38122 Trento, Italy  \n6 Baylor College of Medicine, One Baylor Plaza Blvd, Houston, TX 77030, USA  \n* Correspondence: [emerola@uniss.it](emerola@uniss.it)  \nSimple Summary  \nPancreatic Neuroendocrine Neoplasms (Pan-NENs) represent a unique challenge in oncology due to their varied nature, making accurate risk stratification and treatment selection challenging. Artificial Intelligence (AI) has emerged as a powerful tool capable of mining extensive clinical and diagnostic data to refine predictions regarding patient survival and metastatic spread. Despite enthusiastic early results, the field faces hurdles, including ethical dilemmas and a lack of large-scale, validated studies. This review investigates the current landscape of AI-based predictive tools and their effectiveness in forecasting clinical outcomes for Pan-NEN patients.  \nAbstract  \nThe clinical management of Pancreatic Neuroendocrine Neoplasms (Pan-NENs) is complicated by the disease’s intrinsic variability, which creates significant hurdles for accurate risk profiling and the standardization of treatment protocols. Recently, Artificial Intelligence (AI) has offered a promising avenue to address these challenges. By integrating and processing high-dimensional multimodal datasets (encompassing clinical history, radiomics, and pathology), these computational tools can refine survival forecasts and support the development of personalized medicine. However, the transition from experimental success to routine clinical use is currently obstructed by reliance on limited, retrospective cohorts that lack external validation, alongside unresolved concerns regarding algorithmic transparency and ethical governance. This review evaluates the current landscape of AI-driven prognostic modeling for Pan-NENs and critically examines the pathway towards their reliable integration into clinical practice.  \nKeywords: pancreatic neuroendocrine neoplasms; artificial intelligence; prognostic models; survival; clinical outcomes  \n1. Introduction  \nPancreatic neuroendocrine neoplasms (Pan-NENs) constitute a rare category of pancreatic malignancies, representing roughly 1–5% of all pancreatic tumors. Despite their rarity, epidemiological data derived from population-based registries indicate a steady increase ","cbCailrBlVBfMmmo","https://ap.wps.com/l/cbCailrBlVBfMmmo","pdf",1026028,13,"English","# Simple Summary\n# Abstract\n# Keywords\n# 1. Introduction\n## Disease burden and epidemiology\n## Clinical and biological heterogeneity\n## Treatment landscape and management complexity\n## Prognostic determinants and modeling approaches","[{\"question\":\"Why are accurate risk profiling and treatment standardization difficult in Pan-NENs?\",\"answer\":\"Pan-NENs show intrinsic variability and strong biological and clinical heterogeneity, leading to challenges in consistent risk profiling and protocol standardization.\"},{\"question\":\"How does AI aim to improve prognostic predictions for Pan-NEN patients?\",\"answer\":\"AI tools integrate and process high-dimensional multimodal datasets, such as clinical history, radiomics, and pathology, to refine survival forecasts and support personalized medicine.\"},{\"question\":\"What limits the routine clinical adoption of AI-driven prognostic models?\",\"answer\":\"Adoption is hindered by the use of limited retrospective cohorts without external validation, alongside unresolved concerns about algorithmic transparency and ethical governance.\"}]","Predicting the Unpredictable - AI-Driven Prognosis in Pancreatic Neuroendocrine Neoplasms | PDF",1790114935,33]