Differentiation of Pancreatic Ductal Adenocarcinoma and Chronic Pancreatitis using Graph Neural Networks on Histopathology and Collagen Fiber Features

Abstract

Abstract Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal human cancers. However, the symptoms and follow-up radiographic and histopathology assessments of PDAC are similar to chronic pancreatitis (CP) and can lead to misdiagnosis. The need for accurate differentiation of PDAC and CP has become a major topic in pancreatic pathology. These two diseases can present similar histomorphological features, such as excessive deposition of fibrotic stroma in the tissue microenvironment and inflammatory cell infiltration. In this paper, we present a quantitative analysis pipeline empowered by graph neural networks (GNN) capable of automatic detection and differentiation of PDAC and CP in human histological specimens. Modeling histological images as graphs and deploying graph convolutions can enable the capture of histomorphological features at different scales, ranging from nuclear size to the organization of ducts. The analysis pipeline combines image features computed from co-registered hematoxylin and eosin (H&E) images and Second-Harmonic Generation (SHG) microscopy images, with the SHG images enabling the extraction of collagen fiber morphological features. Evaluating the analysis pipeline on a human tissue micro-array dataset consisting of 786 cores and a tissue region dataset consisting of 268 images, it attained 86.4% accuracy with an average area under the curve (AUC) of 0.954 and 88.9% accuracy with an average AUC of 0.957, respectively. Moreover, incorporating topological features of collagen fibers computed from SHG images into the model further increases the classification accuracy on the tissue region dataset to 91.3% with an average AUC of 0.962, suggesting that collagen characteristics are diagnostic features in PDAC and CP detection and differentiation.