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.

Previous studies on deep learning (DL) applications in pathology have focused on pathologist-versus-algorithm comparisons. However, DL will not replace the breadth and contextual knowledge of pathologists; rather, only through their combination may the benefits of DL be achieved. A fully crossed multireader multicase study was conducted to evaluate DL assistance with pathologists’ diagnosis of gastric cancer. A total of 110 whole-slide images (WSI) (50 malignant and 60 benign) were interpreted by 16 board-certified pathologists with or without DL assistance, with a washout period between sessions. DL-assisted pathologists achieved a higher area under receiver operating characteristic curve (ROC-AUC) (0.911 vs. 0.863, P = 0.003) than unassisted in interpreting the 110 WSIs. Pathologists with DL assistance demonstrated higher sensitivity in detection of gastric cancer than without (90.63% vs. 82.75%, P = 0.010). No significant difference was observed in specificity with or without deep learning assistance (78.23% vs. 79.90%, P = 0.468). The average review time per WSI was shortened with DL assistance than without (22.68 vs. 26.37 second, P = 0.033). Our results demonstrated that DL assistance indeed improved pathologists’ accuracy and efficiency in gastric cancer diagnosis and further boosted the acceptance of this new technique.

Objectives The microscopic evaluation of slides has been gradually moving towards all digital in recent years, leading to the possibility for computer-aided diagnosis. It is worthwhile to know the similarities between deep learning models and pathologists before we put them into practical scenarios. The simple criteria of colorectal adenoma diagnosis make it to be a perfect testbed for this study. Design The deep learning model was trained by 177 accurately labelled training slides (156 with adenoma). The detailed labelling was performed on a self-developed annotation system based on iPad. We built the model based on DeepLab v2 with ResNet-34. The model performance was tested on 194 test slides and compared with five pathologists. Furthermore, the generalisation ability of the learning model was tested by extra 168 slides (111 with adenoma) collected from two other hospitals. Results The deep learning model achieved an area under the curve of 0.92 and obtained a slide-level accuracy of over 90% on slides from two other hospitals. The performance was on par with the performance of experienced pathologists, exceeding the average pathologist. By investigating the feature maps and cases misdiagnosed by the model, we found the concordance of thinking process in diagnosis between the deep learning model and pathologists. Conclusions The deep learning model for colorectal adenoma diagnosis is quite similar to pathologists. It is on-par with pathologists’ performance, makes similar mistakes and learns rational reasoning logics. Meanwhile, it obtains high accuracy on slides collected from different hospitals with significant staining configuration variations.

This study describes the clinical, histologic, and immunohistochemical features of four cases of GI PEComa. The size of the tumors ranged from 3.5 to 6.0cm in diameter, and all were located in wall of the large bowel. Microscopically, the tumors were characterized by an epithelioid arrangement of tumor cells, which had abundant clear to pale eosinophilic granular cytoplasm, vesicular nuclei and prominent nucleoli. The stroma was rich in capillaries, a sinusoidal vasculature and thick-walled blood vessels. Mitotic figures were generally rare. Immunohistochemically, the tumors were positive for Vimentin (4/4), HMB-45 (4/4), Melan-A(4/4),alpha-smooth muscle actin (4/4), and desmin (3/4). The patients have been well with no evidence of disease at 8, 15, 32 and 36 months,respectively, after the surgical operation. GI tract PEComas should be regarded as tumors of an uncertain malignant potential until long-term outcome data for a larger number of patients become available.