Identifying Complex High-Risk Individuals (HRI-C) for Early Detection of Occult Pancreatic Cancers Using Risk Models and Biomarkers.
Year: 2024
Type: article
Abstract: Early detection and diagnosis of pancreatic cancer (pancreatic ductal adenocarcinoma, PDAC) offers the best option for effective treatment and prolonged survival. Image-based screening programs are useful in early detection of pancreatic cancers in high-risk individuals (HRI) defined by strong family history and/or pathogenic variants (PV) in hereditary cancer syndrome (HCS) genes associated with an increased risk for PDAC development. However, this only applies in about 10% of people who eventually develop pancreatic cancer. An additional 15% of PDACs are associated with a pancreatic cystic neoplasms. Thus, the majority (~75%) of patients diagnosed with PDAC are not candidates for surveillance under an existing framework. Based on growing evidence we believe that early detection is also possible in what we call Complex High-Risk Individuals (HRI-C) through frequent, time-limited image-based screening. These HRI-C are identified by combining multiple clinical and genetic risk factors alongside early markers of pancreatic cancer (e.g. family history, past medical history, social history, and multiple genetic factors in individuals with worrisome individual or combinations of biomarkers of occult pancreatic cancer in older individuals (>50 years) such as new onset diabetes (NOD), new idiopathic acute or chronic pancreatitis, maldigestion with exocrine pancreatic insufficiency (EPI), abnormal liver injury test (especially biliary), unexplained weight loss and typical cancer-associated pain patterns). Here we propose a potentially cost-effective clinical decision pathway aimed at improving early detection, diagnosis and outcomes of this large group of individuals at risk for developing PDAC.
DOI: 10.69734/525x3s54
Authors: Randall Brand, David Whitcomb, Bechien Wu
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4405706727