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-aideddiagnosis and detection (CAD) methods, little attention has been paidto the detection of Pancreatic Cancer. We propose a method for de-tecting pancreatic tumor that utilizes clinically-relevant features in thesurrounding anatomical structures, thereby better aiming to exploit theradiologist’s knowledge compared to other, conventional deep learningapproaches. To this end, we collect a new dataset consisting of 99 caseswith pancreatic ductal adenocarcinoma (PDAC) and 97 control caseswithout 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 thatmay indicate the presence of the tumor. We propose a method based ona U-Net-like Deep CNN that exploits the following external secondaryfeatures: the pancreatic duct, common bile duct and the pancreas, alongwith a processed CT scan. Using these features, the model segmentsthe pancreatic tumor if it is present. This segmentation for classificationand localization approach achieves a performance of 99% sensitivity (onecase missed) and 99% specificity, which realizes a 5% increase in sensi-tivity over the previous state-of-the-art method. The model additionallyprovides location information with reasonable accuracy and a shorterinference time compared to previous PDAC detection methods. Theseresults offer a significant performance improvement and highlight theimportance of incorporating the knowledge of the clinical expert whendeveloping novel CAD methods