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

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.

SUBMITTER: Song Z

PROVIDER: S-EPMC7453200 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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

Song Zhigang Z Zou Shuangmei S Zhou Weixun W Huang Yong Y Shao Liwei L Yuan Jing J Gou Xiangnan X Jin Wei W Wang Zhanbo Z Chen Xin X Ding Xiaohui X Liu Jinhong J Yu Chunkai C Ku Calvin C Liu Cancheng C Sun Zhuo Z Xu Gang G Wang Yuefeng Y Zhang Xiaoqing X Wang Dandan D Wang Shuhao S Xu Wei W Davis Richard C RC Shi Huaiyin H

Nature communications 20200827 1

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-st ...[more]

PMID: 32855423

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Project description:BackgroundOver half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain.ObjectiveAdopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice.MethodsThe model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions.ResultsWe demonstrated the model's clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model's generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training.ConclusionsDeep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.

Dataset Information

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

Publications

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

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