Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning

By Zhigang Song, S. Zou, Weixun Zhou, Yong Huang, Liwei Shao, Jing Yuan, X. Gou, Wei Jin, Zhanbo Wang, Xin Chen and 13 more. Nature Communications, 2020

350 citations recorded by the source corpus.

The early detection and accurate histopathological diagnosis of gastric cancer increase the chances of successful treatment. The worldwide shortage of pathologists offers a unique opportunity for the use of artificial intelligence assistance systems to alleviate the workload and increase diagnostic accuracy. Here, we report a clinically applicable system developed at the Chinese PLA General Hospital, China, using a deep convolutional neural network trained with 2,123 pixel-level annotated H&E-stained whole slide images. The model achieves a sensitivity near 100% and an average specificity of 80.6% on a real-world test dataset with 3,212 whole slide images digitalized by three scanners. We show that the system could aid pathologists in improving diagnostic accuracy and preventing misdiagnoses. Moreover, we demonstrate that our system performs robustly with 1,582 whole slide images from two other medical centres. Our study suggests the feasibility and benefits of using histopathological artificial intelligence assistance systems in routine practice scenarios. The early detection and accurate histopathological diagnosis of gastric cancer are essential factors that can help increase the chances of successful treatment. Here, the authors report on a digital pathology tool achieving high performance on a real world test dataset and show that the system can aid pathologists in improving diagnostic accuracy.

Subfields: Artificial Intelligence, Oncology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/ea18791085819c9c9d831c0018c7a97f9e7c7241

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Citations in the corpus

From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment. (2023, background): “42,43 DL models sometimes predict more than a binary cancer versus no cancer label in order to provide clinicians with additional diagnostic information.”
Deep learning in histopathology: the path to the clinic (2021, methodology): “This solution was adopted by a study in 2020 78 , in which 12 senior pathologists were involved in exhaustively manually annotating > 2,000 WSIs of gastric cancer; the agreement among all experts involved was used as a reference standard.”