Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer
Kate Murray, Lucy Oldfield, Irena Stefanova, Manuel Gentiluomo, Paolo Aretini, Rachel O’Sullivan, William Greenhalf, Salvatore Paiella, Mateus Nóbrega Aoki, Aldo Pastore, James Birch-Ford, Bhavana Hemantha Rao, Pinar Uysal‐Onganer, Caoimhe Walsh, George B. Hanna, Jagriti Narang, Pradakshina Sharma, Daniele Campa, Cosmeri Rizzato, Andrei Turtoï, Elif Sever, Alessio Felici, Ceren Sucularlı, Giulia Peduzzi, Efkan Öz, Osman Uğur Sezerman, Robert Van Der Meer, Nathan E. Thompson, Eithne Costello
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.