Chronic pancreatitis, often associated with alcohol use, smoking, or genetic risk factors, may be complicated by pseudocysts, biliary strictures, pancreatic insufficiency, bone loss, and pancreatic cancer. Aspects of management include structural and nonstructural interventions for pain and treatment with pancreatic-enzyme replacement.

Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. In response to the maturity of AI, Kenner Family Research Fund conducted the 2020 AI and Early Detection of Pancreatic Cancer Virtual Summit (www.pdac-virtualsummit.org) in conjunction with the American Pancreatic Association, with a focus on the potential of AI to advance early detection efforts in this disease. This comprehensive presummit article was prepared based on information provided by each of the interdisciplinary participants on one of the 5 following topics: Progress, Problems, and Prospects for Early Detection; AI and Machine Learning; AI and Pancreatic Cancer—Current Efforts; Collaborative Opportunities; and Moving Forward—Reflections from Government, Industry, and Advocacy. The outcome from the robust Summit conversations, to be presented in a future white paper, indicate that significant progress must be the result of strategic collaboration among investigators and institutions from multidisciplinary backgrounds, supported by committed funders.

The potential of artificial intelligence (AI) applied to clinical data from electronic health records (EHRs) to improve early detection for pancreatic and other cancers remains underexplored. The Kenner Family Research Fund, in collaboration with the Cancer Biomarker Research Group at the National Cancer Institute, organized the workshop entitled: “Early Detection of Pancreatic Cancer: Opportunities and Challenges in Utilizing Electronic Health Records (EHR)” in March 2021. The workshop included a select group of panelists with expertise in pancreatic cancer, EHR data mining, and AI-based modeling. This review article reflects the findings from the workshop and assesses the feasibility of AI-based data extraction and modeling applied to EHRs. It highlights the increasing role of data sharing networks and common data models in improving the secondary use of EHR data. Current efforts using EHR data for AI-based modeling to enhance early detection of pancreatic cancer show promise. Specific challenges (biology, limited data, standards, compatibility, legal, quality, AI chasm, incentives) are identified, with mitigation strategies summarized and next steps identified.

Purpose of review: Type 3 auto-immune pancreatitis (AIP) is a rare immune-related adverse event (irAE) because of immune checkpoint inhibitor (ICI) therapy employed in the management of advanced malignancies. The evaluation and management of this disease entity is not well documented in the literature. We summarize the available information on the clinical profile, diagnosis, and treatment of this disorder. Recent findings: ICI-pancreatic injury (ICI-PI) is a form of AIP, recently termed type 3 AIP, which may present as an asymptomatic lipase elevation or clinical pancreatitis, that is, abdominal pain and elevated lipase. CT findings of pancreatitis may be absent in some cases. Diagnosis is based on a temporal relationship to ICI exposure and the absence of other cause of pancreatitis. Combination ICIs increase the risk of type 3 AIP compared with ICI monotherapy. Though corticosteroids are used for ICIP, their role and benefit remain unclear to date. Holding immunotherapy carries the risk of progression of underlying cancer. Summary: ICI-PI is a unique form of AIP (type 3) with a distinct disease profile. The majority of patients with ICIPI are asymptomatic and steroid therapy has unclear benefits.