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Early neoplastic and preneoplastic lesions are crucial because timely surgery can improve prognosis. This study synthesizes evidence from PubMed and other sources to assess how noninvasive approaches combined with artificial intelligence can predict pancreatic cancer from early signs, including AI-driven analysis of imaging and electronic medical records for potential screening models integrating biomarkers and multimodal data.",{"@graph":69,"@context":123},[70,84,106],{"@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/artificial-intelligence-as-a-noninvasive-tool-for-pancreatic-cancer-prediction-and-diagnosis/376659/",{"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-as-a-noninvasive-tool-for-pancreatic-cancer-prediction-and-diagnosis/376659.png","ImageObject",300,407,{"name":92,"@type":93},"Emma Wilson","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is early detection of pancreatic cancer important?","Question",{"text":113,"@type":114},"Early neoplastic lesions enable surgery and offer the best prognosis. 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Screening models should combine biomarkers with medical and imaging information from multiple sources.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},376659,1790450035,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146},3848291630094,"https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","| W | J | G | World Journal of\u003Cbr>Gastroenterology |\n| --- | --- | --- | --- |\n\nSubmit a Manuscript: [https://www.f6publishing.com](https://www.f6publishing.com) World J Gastroenterol 2023 March 28; 29(12): 1811-1823  \nDOI: 10.3748/wjg.v29.i12.1811 ISSN 1007-9327 (print) ISSN 2219-2840 (online)  \nMINIREVIEWS  \nArtificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosis  \nAlexandra Corina Faur, Daniela Cornelia Lazar, Laura Andreea Ghenciu  \nSpecialty type: Gastroenterology and hepatology  \nProvenance and peer review:  \nInvited article; Externally peer reviewed.  \nPeer-review model: Single blind  \nPeer-review report’s scientific quality classification  \nGrade A (Excellent): 0  \nGrade B (Very good): B  \nGrade C (Good): C  \nGrade D (Fair): D  \nGrade E (Poor): 0  \nP-Reviewer: Charalampopoulou A, Italy; Chen G, China; Yang Y, China  \nReceived: November 15, 2022  \nPeer-review started: November 15, 2022  \nFirst decision: December 11, 2022  \nRevised: December 23, 2022  \nAccepted: March 15, 2023  \nArticle in press: March 15, 2023  \nPublished online: March 28, 2023  \nAlexandra Corina Faur, Department of Anatomy and Embriology,“Victor Babeș” University of Medicine and Pharmacy Timișoara, Timișoara 300041, Timiș, Romania  \nDaniela Cornelia Lazar, Department V of Internal Medicine I, Discipline of Internal Medicine IV, University of Medicine and Pharmacy “Victor Babes” Timișoara, Timișoara 300041, Timiș, Romania  \nLaura Andreea Ghenciu, Department III, Discipline of Pathophysiology,“Victor Babeș”University of Medicine and Pharmacy, Timișoara 300041, Timiș, Romania  \nCorresponding author: Alexandra Corina Faur, PhD, Additional Professor, Department of Anatomy and Embriology,“Victor Babeș” University of Medicine and Pharmacy Timișoara, [Eftimie Murgu Sq. no. 2](Eftimie Murgu Sq. no. 2), Timișoara 300041, Timiș, [Romania.](Romania. faur.alexandra@umft.ro)[ faur.alexandra@umft.ro](Romania. faur.alexandra@umft.ro)  \nAbstract  \nPancreatic cancer (PC) has a low incidence rate but a high mortality, with patients often in the advanced stage of the disease at the time of the first diagnosis. If detected, early neoplastic lesions are ideal for surgery, offering the best prognosis. Preneoplastic lesions of the pancreas include pancreatic intraepithelial neoplasia and mucinous cystic neoplasms, with intraductal papillary mucinous neoplasms being the most commonly diagnosed. Our study focused on predicting PC by identifying early signs using noninvasive techniques and artificial intelligence (AI) . A systematic English literature search was conducted on the PubMed electronic database and other sources. We obtained a total of 97 studies on the subject of pancreatic neoplasms. The final number of articles included in our study was 44, 34 of which focused on the use of AI algorithms in the early diagnosis and prediction of pancreatic lesions. AI algorithms can facilitate diagnosis by analyzing massive amounts of data in a short period of time. Correlations can be made through AI algorithms by expanding image and electronic medical records databases, which can later be used as part of a screening program for the general population. AI-based screening models should involve a combination of biomarkers and medical and imaging data from different sources. This requires large numbers of resources, collaboration between medical practitioners, and investment in medical infrastructures.  \nKey Words: Pancreatic cancer; Early pancreatic lesions; Pancreatic neoplasia; Artificial intelligence; Deep learning; Machine learning; Radiomics; Diagnosis; Pancreas  \n WJG  [https://www.wjgnet.com](https://www.wjgnet.com) 1811 March 28, 2023  Volume 29  Issue 12   \nFaur AC et al. PC prediction using AI  \n©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.  \nCore Tip: To improve the clinical management and prognosis for patients with pancreatic cancer (PC), new diagnostic methods should be developed to identify ","cbCaitLTFxUBQDIi","https://ap.wps.com/l/cbCaitLTFxUBQDIi","pdf",1404667,14,"English","# MINIREVIEWS\n## Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosis\n## Abstract\n## Key Words","[{\"question\":\"Why is early detection of pancreatic cancer important?\",\"answer\":\"Early neoplastic lesions enable surgery and offer the best prognosis. Many patients are diagnosed at advanced stages, so improving early detection is critical.\"},{\"question\":\"How did the study approach the evidence on AI for pancreatic cancer?\",\"answer\":\"A systematic English literature search was conducted on PubMed and other sources, yielding studies focused on noninvasive prediction and early diagnosis using AI algorithms.\"},{\"question\":\"What data types can AI use for pancreatic cancer screening or prediction?\",\"answer\":\"AI can analyze large volumes of heterogeneous data, including imaging data and electronic medical records. Screening models should combine biomarkers with medical and imaging information from multiple sources.\"}]","Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosis | PDF",1790220486,35]