Abstract
Early detection of Pancreatic Ductal Adenocarcinoma (PDAC), one of the most aggressive malignancies of the pancreas, is crucial to avoid metastatic spread to other body regions. Detection of pancreatic cancer is typically carried out by assessing the distribution and arrangement of tumor and immune cells in histology images. This is further complicated due to morphological similarities with chronic pancreatitis (CP), and the co-occurrence of precursor lesions in the same tissue. Most of the current automated methods for grading pancreatic cancers rely on extensive feature engineering involving accurate identification of cell features or utilising single number spatially informed indices for grading purposes. Moreover, sophisticated methods involving black-box approaches, such as neural networks, do not offer insights into the model’s ability to accurately identify the correct disease grade. In this paper, we develop a novel cell-graph based Cell-Graph Attention (CGAT) network for the precise classification of pancreatic cancer and its precursors from multiplexed immunofluorescence histology images into the six different types of pancreatic diseases. The issue of class imbalance is addressed through bootstrapping multiple CGAT-nets, while the self-attention mechanism facilitates visualization of cell-cell features that are likely responsible for the predictive capabilities of the model. It is also shown that the model significantly outperforms the decision tree classifiers built using spatially informed metric, such as the Morisita-Horn (MH) indices.
Highlights
In recent years, there has been an increase in the incidence of pancreatic cancers cases [1]
There have been precursor lesions that have been identified and associated with sequential progression to Pancreatic Ductal Adenocarcinoma (PDAC), with the most important being intraductal papillary mucinous neoplasm (IPMN), pancreatic intraductal neoplasia (PanIN), and mucinous cystic neoplasm (MCN), all of which have been well-documented in recent years [4, 5]
It is possible for MCN and IPMN to be concomitant with PDAC, with both histologies being present in a patient [6]
Summary
There has been an increase in the incidence of pancreatic cancers cases [1]. There have been precursor lesions that have been identified and associated with sequential progression to PDAC, with the most important being intraductal papillary mucinous neoplasm (IPMN), pancreatic intraductal neoplasia (PanIN), and mucinous cystic neoplasm (MCN), all of which have been well-documented in recent years [4, 5]. It is possible for MCN and IPMN to be concomitant with PDAC, with both histologies being present in a patient [6]. It has been observed that a dense inflammatory mass formation is present in around 30% of CP diagnoses, mimicking the appearance of PDAC, posing additional challenges in differential diagnosis [7]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.