Abstract
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.
Highlights
The early detection and accurate histopathological diagnosis of gastric cancer increase the chances of successful treatment
While recent studies have validated the effectiveness of pathology artificial intelligence (AI) for tumor detection in various organ systems, such as lung[20], stomach[21], lymph node metastases in breast cancer[22,23,24], prostate core needle biopsies[24,25,26], and mesothelioma[27], we identify many nontrivial challenges that should be addressed before considering application in the clinical setting
The deep learning model is trained with 2123 pixel-level annotated haematoxylin and eosin (H&E)-stained digital slides from 1500 patients, which include 958 surgical specimens (908 malignancies) and 542 biopsies (102 malignancies) with diverse tumor subtypes; details are illustrated in Fig. 1a
Summary
The early detection and accurate histopathological diagnosis of gastric cancer increase the chances of successful treatment. The deep learning model is trained with 2123 pixel-level annotated haematoxylin and eosin (H&E)-stained digital slides from 1500 patients, which include 958 surgical specimens (908 malignancies) and 542 biopsies (102 malignancies) with diverse tumor subtypes; details are illustrated in Fig. 1a (abbreviations are given in Supplementary Table 1).
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