Could a New Deep Learning Tool Enhance CT Detection of Pancreatic Cancer?

Noting that nearly 40 percent of pancreatic cancer tumors smaller than 2 cm are missed on computed tomography (CT) assessment, the authors of a new study suggest that an emerging deep learning tool could have an impact in improving detection. In the study, conducted in Taiwan and published earlier today in Radiology, researchers examined the effectiveness of a deep learning tool for detecting malignant pancreatic tumors on contrast-enhanced CT in a nationwide validation test set consisting of 669 patients with pancreatic cancer and 804 participants in the control group.1 The deep learning tool was trained with contrast-enhanced CT scans from 546 patients with pancreatic cancer and 733 healthy control patients, according to the study. The researchers found that the deep learning tool had an 89.7 sensitivity rate and a 92.8 percent specificity rate for detecting pancreatic cancer in the nationwide validation test set. In local test set data drawn from 109 patients with pancreatic cancer at a tertiary referral center and 147 control participants, the study authors noted no significant differences in sensitivity between assessment by attending radiologists (96.1 percent) and the deep learning tool (90.2 percent).1 "This study developed an end-to-end, deep learning-based, computer-aided detection (CAD) tool that could accurately and robustly detect pancreatic cancers on contrast-enhanced CT scans. The CAD tool may be a useful supplement for radiologists to enhance detection of (prostate cancer)."