Differentiation of pancreatic ductal adenocarcinoma and chronic pancreatitis using graph neural networks on histopathology and collagen fiber features.

Publisher: Elsevier Inc Country of Publication: United States NLM ID: 101528849 Publication Model: eCollection Cited Medium: Print ISSN: 2229-5089 (Print) NLM ISO Abbreviation: J Pathol Inform Subsets: PubMed not MEDLINE

Publication: 2022- : [New York] : Elsevier Inc.
Original Publication: Ghatkopar, Mumbai : Medknow Publications and Media, 2010-

Competing Interests: The authors declare no conflict of interest.
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal human cancers. However, the symptoms and radiographic appearance of chronic pancreatitis (CP) mimics that of PDAC, and sometimes the 2 entities can also be difficult to differentiate microscopically. The need for accurate differentiation of PDAC and CP has become a major topic in pancreatic pathology. These 2 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.
(© 2022 The Author(s).)

P41 GM135019 United States GM NIGMS NIH HHS

Keywords: Chronic pancreatitis; Collagen fibers; Deep learning; Graph neural network; Histopathology; Pancreatic cancer; Second-harmonic generation imaging

Date Created: 20230106 Latest Revision: 20230928

20250114

PMC9808020

10.1016/j.jpi.2022.100158

36605110