  Gastric diffuse-type adenocarcinoma represents a disproportionately high
percentage of cases of gastric cancers occurring in the young, and its relative
incidence seems to be on the rise. Usually it affects the body of the stomach,
and presents shorter duration and worse prognosis compared with the
differentiated (intestinal) type adenocarcinoma. The main difficulty
encountered in the differential diagnosis of gastric adenocarcinomas occurs
with the diffuse-type. As the cancer cells of diffuse-type adenocarcinoma are
often single and inconspicuous in a background desmoplaia and inflammation, it
can often be mistaken for a wide variety of non-neoplastic lesions including
gastritis or reactive endothelial cells seen in granulation tissue. In this
study we trained deep learning models to classify gastric diffuse-type
adenocarcinoma from WSIs. We evaluated the models on five test sets obtained
from distinct sources, achieving receiver operator curve (ROC) area under the
curves (AUCs) in the range of 0.95-0.99. The highly promising results
demonstrate the potential of AI-based computational pathology for aiding
pathologists in their diagnostic workflow system.
