Related Experiment Video
Updated: Apr 19, 2026
Technical Detail for Robot Assisted Pancreaticoduodenectomy
Published on: September 28, 2019
Automated pancreatic cyst screening using natural language processing: a new tool in the early detection of
Alexandra M Roch1, Saeed Mehrabi, Anand Krishnan
1Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA.
Introduction:
As many as 3% of computed tomography (CT) scans detect pancreatic cysts. Because pancreatic cysts are incidental, ubiquitous and poorly understood, follow-up is often not performed. Pancreatic cysts may have a significant malignant potential and their identification represents a 'window of opportunity' for the early detection of pancreatic cancer. The purpose of this study was to implement an automated Natural Language Processing (NLP)-based pancreatic cyst identification system.
Method:
A multidisciplinary team was assembled. NLP-based identification algorithms were developed based on key words commonly used by physicians to describe pancreatic cysts and programmed for automated search of electronic medical records. A pilot study was conducted prospectively in a single institution.
Results:
From March to September 2013, 566,233 reports belonging to 50,669 patients were analysed. The mean number of patients reported with a pancreatic cyst was 88/month (range 78-98). The mean sensitivity and specificity were 99.9% and 98.8%, respectively.
Conclusion:
NLP is an effective tool to automatically identify patients with pancreatic cysts based on electronic medical records (EMR). This highly accurate system can help capture patients 'at-risk' of pancreatic cancer in a registry.
More Related Videos
13:56Robot Assisted Distal Pancreatectomy with Celiac Axis Resection DP-CAR for Pancreatic Cancer: Surgical Planning and Technique
Published on: August 14, 2021
10:02Standardized SDS-PAGE Workflow for Personalized Protein Corona Profiling in Early Cancer Detection
Published on: December 19, 2025