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Biomarkers, Omics and Artificial Intelligence for Early Detection of Pancreatic Cancer

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Author
Murray, Kate
Oldfield, Lucy
Stefanova, Irena
Gentiluomo, Manuel
Aretini, Paolo
O'Sullivan, Rachel
Greenhalf, William
Paiella, Salvatore
Aoki, Mateus N
Pastore, Aldo
Birch-Ford, James
Rao, Bhavana Hemantha
Uysal-Onganer, Pinar
Walsh, Caoimhe M
Hanna, George B
Narang, Jagriti
Sharma, Pradakshina
Campa, Daniele
Rizzato, Cosmeri
Turtoi, Andrei
Sever, Elif Arik
Felici, Alessio
Sucularli, Ceren
Peduzzi, Giulia
Öz, Elif
Sezerman, Osman Uğur
Van der Meer, Robert
Thompson, Nathan
Costello, Eithne

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Publication date
2025
Published in
Seminars in Cancer Biology
Publisher / Publication place
Academic Press
Volume / Issue
111 (June)
ISBN / ISSN
ISSN: 1044-579X
ISBN / ISSN
eISSN: 1096-3650
Funding Information
MSM//LX22NPO5102
MZ0//NW24-03-00024
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  • Faculty of Medicine in Pilsen

This publication has a published version with DOI 10.1016/j.semcancer.2025.02.009

Abstract
Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in its late stages when treatment options are limited. Unlike other common cancers, there are no population-wide screening programmes for PDAC. Thus, early disease detection, although urgently needed, remains elusive. Individuals in certain high-risk groups are, however, offered screening or surveillance. Here we explore advances in understanding high-risk groups for PDAC and efforts to implement biomarker-driven detection of PDAC in these groups. We review current approaches to early detection biomarker development and the use of artificial intelligence as applied to electronic health records (EHRs) and social media. Finally, we address the cost-effectiveness of applying biomarker strategies for early detection of PDAC.
Keywords
Pancreatic cancer, artificial intelligence, biomarkers, early detection, omics
Permanent link
https://hdl.handle.net/20.500.14178/3344
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WOS:001441748100001
SCOPUS:2-s2.0-85219081949
PUBMED:39986585
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Full text of this result is licensed under: Creative Commons Uveďte původ 4.0 International

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