Background: Computed tomography (CT) imaging is routinely obtained for diagnostics, especially in trauma and emergency rooms, often identifying incidental findings. We utilized a natural language processing (NLP) algorithm to quantify the incidence of clinically relevant pancreatic lesions in CT imaging.
Patients and methods: We utilized the electronic medical record to perform a retrospective chart review of adult patients admitted for trauma to a level 1 tertiary care center between 2010 and 2020 who underwent abdominal CT imaging. An open-source NLP software was used to identify patients with intrapapillary mucinous neoplasms (IPMN), pancreatic cysts, pancreatic ductal dilation, or pancreatic masses after optimizing the algorithm using a test group of patients who underwent pancreatic surgery.
Results: The algorithm identified pancreatic lesions in 27 of 28 patients who underwent pancreatic surgery and excluded 1 patient who had a pure ampullary mass. The study cohort consisted of 18,769 patients who met our inclusion criteria admitted to the hospital. Of this population, 232 were found to have pancreatic lesions of interest. There were 48 (20.7%) patients with concern for IPMN, pancreatic cysts in 36 (15.5%), concerning masses in 30 (12.9%), traumatic findings in 44 (19.0%), pancreatitis in 41 (17.7%), and ductal abnormalities in 19 (18.2%) patients. Prior pancreatic surgery and other findings were identified in 14 (6.0%) patients.
Conclusions: In this study, we propose a novel use of NLP software to identify potentially malignant pancreatic lesions annotated in CT imaging performed for other purposes. This methodology can significantly increase the screening and automated referral for the management of precancerous lesions.
© 2022. Society of Surgical Oncology.