Improved Pancreatic Tumor Detection by Utilizing Clinically-Relevant Secondary Features

Pancreatic cancer is one of the global leading causes of cancer-
related deaths. Despite the success of Deep Learning in computer-aided
diagnosis and detection (CAD) methods, little attention has been paid
to the detection of Pancreatic Cancer. We propose a method for de-
tecting pancreatic tumor that utilizes clinically-relevant features in the
surrounding anatomical structures, thereby better aiming to exploit the
radiologist’s knowledge compared to other, conventional deep learning
approaches. To this end, we collect a new dataset consisting of 99 cases
with pancreatic ductal adenocarcinoma (PDAC) and 97 control cases
without any pancreatic tumor. Due to the growth pattern of pancre-
atic cancer, the tumor may not be always visible as a hypodense lesion,
therefore experts refer to the visibility of secondary external features that
may indicate the presence of the tumor. We propose a method based on
a U-Net-like Deep CNN that exploits the following external secondary
features: the pancreatic duct, common bile duct and the pancreas, along
with a processed CT scan. Using these features, the model segments
the pancreatic tumor if it is present. This segmentation for classification
and localization approach achieves a performance of 99% sensitivity (one
case missed) and 99% specificity, which realizes a 5% increase in sensi-
tivity over the previous state-of-the-art method. The model additionally
provides location information with reasonable accuracy and a shorter
inference time compared to previous PDAC detection methods. These
results offer a significant performance improvement and highlight the
importance of incorporating the knowledge of the clinical expert when
developing novel CAD methods.