Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning

Xiaodong Wang, Ying Chen, Yunshu Gao, Huiqing Zhang, Zehui Guan, Zhou Dong, Yuxuan Zheng, Jiarui Jiang, Haoqing Yang, Liming Wang, Xianming Huang, Lirong Ai, Wenlong Yu, Hongwei Li, Changsheng Dong, Zhou Zhou, Xiyang Liu () and Guanzhen Yu ()
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Xiaodong Wang: Xidian University
Ying Chen: Changhai Hospital
Yunshu Gao: General Hospital of PLA
Huiqing Zhang: Jiangxi Provincial Cancer Hospital
Zehui Guan: Northwestern Polytechnical University
Zhou Dong: Northwestern Polytechnical University
Yuxuan Zheng: Xidian University
Jiarui Jiang: Xidian University
Haoqing Yang: Xidian University
Liming Wang: Xidian University
Xianming Huang: Jiangxi Provincial Cancer Hospital
Lirong Ai: Northwestern Polytechnical University
Wenlong Yu: Eastern Hepatobiliary Surgery Hospital
Hongwei Li: Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine
Changsheng Dong: Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine
Zhou Zhou: Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine
Xiyang Liu: Xidian University
Guanzhen Yu: Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: Abstract N-staging is a determining factor for prognostic assessment and decision-making for stage-based cancer therapeutic strategies. Visual inspection of whole-slides of intact lymph nodes is currently the main method used by pathologists to calculate the number of metastatic lymph nodes (MLNs). Moreover, even at the same N stage, the outcome of patients varies dramatically. Here, we propose a deep-learning framework for analyzing lymph node whole-slide images (WSIs) to identify lymph nodes and tumor regions, and then to uncover tumor-area-to-MLN-area ratio (T/MLN). After training, our model’s tumor detection performance was comparable to that of experienced pathologists and achieved similar performance on two independent gastric cancer validation cohorts. Further, we demonstrate that T/MLN is an interpretable independent prognostic factor. These findings indicate that deep-learning models could assist not only pathologists in detecting lymph nodes with metastases but also oncologists in exploring new prognostic factors, especially those that are difficult to calculate manually.

Date: 2021
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DOI: 10.1038/s41467-021-21674-7

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