No abstract

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.

No abstract

The IHPBA/AHPBA-sponsored 2016 minimally invasive pancreatic resection (MIPR) conference held on April 20th, 2016 included a session designed to evaluate what would be the most appropriate scientific contribution to help define the increasing role of MIPR internationally. Participants in the conference reviewed the assessment of numerous pertinent scientific designs including randomized controlled trial (RCT), pragmatic international RCT, registry-RCT, non-RCT with propensity matching, and various types of clinical registries including those aiming to create a quality improvement data system or a learning health care system. The strengths and weaknesses of each of these designs, the status of trials which are currently recruiting patients, and pragmatic considerations were evaluated. A recommendation was made to establish a clinical registry to collect data prospectively from around the world to assess current practices and provide a framework for future studies in MIPR.

2016-12-23T18:52:10