Early detection of pancreatic cancer



Pereira, Stephen P, Oldfield, Lucy ORCID: 0000-0002-0839-402X, Ney, Alexander, Hart, Phil A, Keane, Margaret G, Pandol, Stephen J, Li, Debiao, Greenhalf, William, Jeon, Christie Y, Koay, Eugene J
et al (show 4 more authors) (2020) Early detection of pancreatic cancer. The Lancet Gastroenterology and Hepatology, 5 (7). pp. 698-710.

[img] Text
Early Detection of Pancreatic Cancer CHANGES TRACKED 25 10 19.docx - Author Accepted Manuscript

Download (318kB)

Abstract

Pancreatic ductal adenocarcinoma is most frequently detected at an advanced stage. Such late detection restricts treatment options and contributes to a dismal 5-year survival rate of 3–15%. Pancreatic ductal adenocarcinoma is relatively uncommon and screening of the asymptomatic adult population is not feasible or recommended with current modalities. However, screening of individuals in high-risk groups is recommended. Here, we review groups at high risk for pancreatic ductal adenocarcinoma, including individuals with inherited predisposition and patients with pancreatic cystic lesions. We discuss studies aimed at finding ways of identifying pancreatic ductal adenocarcinoma in high-risk groups, such as among individuals with new-onset diabetes mellitus and people attending primary and secondary care practices with symptoms that suggest this cancer. We review early detection biomarkers, explore the potential of using social media for detection, appraise prediction models developed using electronic health records and research data, and examine the application of artificial intelligence to medical imaging for the purposes of early detection.

Item Type: Article
Uncontrolled Keywords: Humans, Pancreatic Cyst, Carcinoma, Pancreatic Ductal, Pancreatic Neoplasms, Diabetes Mellitus, Type 2, Genetic Predisposition to Disease, Diagnostic Imaging, Mass Screening, Risk Factors, Artificial Intelligence, Adult, Aged, Aged, 80 and over, Middle Aged, Female, Male, Early Detection of Cancer, Electronic Health Records, Social Media, Biomarkers, Tumor, Deep Learning
Depositing User: Symplectic Admin
Date Deposited: 16 Mar 2020 16:28
Last Modified: 18 Jan 2023 23:57
DOI: 10.1016/S2468-1253(19)30416-9
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3079313