Successful development of deep learning models to classify gastric diffuse-type adenocarcinoma in histopathological whole slide images – Published in Scientific Reports –
Medmain Inc. (Headquarters: Fukuoka City, Fukuoka Prefecture, CEO: Osamu Iizuka, hereinafter “Medmain”), a provider of “PidPort” pathological diagnosis support solutions, has succeeded in developing deep learning models to classify gastric diffuse-type adenocarcinoma in whole slide images of endoscopic biopsy specimens.
We would also like to announce that a paper on this development was published in “Scientific Reports”, a natural science journal issued by Nature on October 14, 2021.
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 it 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.