Sushil Kumar Garg1, Suresh T Chari2. 1. Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota. 2. Division of Gastroenterology and Hepatology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Abstract
PURPOSE OF REVIEW: Pancreatic cancer is the third leading cause of cancer death and with a dismal 5-year survival of 10%. Poor survival of pancreatic cancer is mostly due to its presentation and diagnosis at a late stage. The present article aims to update clinicians with recent progress in the field of early detection of pancreatic cancer. RECENT FINDINGS: Pancreatic cancer screening is not recommended in the general population due to its low prevalence. In this review, we discuss high-risk groups for pancreatic cancer, including inherited predisposition to pancreatic cancer, new-onset diabetes, mucinous pancreatic cyst, and chronic pancreatitis. We discuss methods of enrichment of high-risk groups with clinical models using electronic health records and biomarkers. We also discuss improvements in imaging modalities and emerging role of machine learning and artificial intelligence in the field of imaging and biomarker to aid in early identification of pancreatic cancer. SUMMARY: There are still vast challenges in the field of early detection of pancreatic cancer. We need to develop noninvasive prediagnostic validated biomarkers for longitudinal surveillance of high-risk individuals and imaging modalities that can identify pancreatic cancer early.
PURPOSE OF REVIEW: Pancreatic cancer is the third leading cause of cancer death and with a dismal 5-year survival of 10%. Poor survival of pancreatic cancer is mostly due to its presentation and diagnosis at a late stage. The present article aims to update clinicians with recent progress in the field of early detection of pancreatic cancer. RECENT FINDINGS:Pancreatic cancer screening is not recommended in the general population due to its low prevalence. In this review, we discuss high-risk groups for pancreatic cancer, including inherited predisposition to pancreatic cancer, new-onset diabetes, mucinous pancreatic cyst, and chronic pancreatitis. We discuss methods of enrichment of high-risk groups with clinical models using electronic health records and biomarkers. We also discuss improvements in imaging modalities and emerging role of machine learning and artificial intelligence in the field of imaging and biomarker to aid in early identification of pancreatic cancer. SUMMARY: There are still vast challenges in the field of early detection of pancreatic cancer. We need to develop noninvasive prediagnostic validated biomarkers for longitudinal surveillance of high-risk individuals and imaging modalities that can identify pancreatic cancer early.