Nature Communications (Mar 2021)
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,
- Guanzhen Yu
Affiliations
- Xiaodong Wang
- School of Computer Science and Technology, Xidian University
- Ying Chen
- Department of Pathology Center of Gastroenterology, Changhai Hospital
- Yunshu Gao
- Department of Oncology, General Hospital of PLA
- Huiqing Zhang
- Department of Gastrointestinal Medical Oncology, Jiangxi Provincial Cancer Hospital
- Zehui Guan
- School of Computer Science, Northwestern Polytechnical University
- Zhou Dong
- School of Computer Science, Northwestern Polytechnical University
- Yuxuan Zheng
- School of Computer Science and Technology, Xidian University
- Jiarui Jiang
- School of Computer Science and Technology, Xidian University
- Haoqing Yang
- School of Computer Science and Technology, Xidian University
- Liming Wang
- School of Computer Science and Technology, Xidian University
- Xianming Huang
- Department of Gastrointestinal Medical Oncology, Jiangxi Provincial Cancer Hospital
- Lirong Ai
- School of Computer Science, Northwestern Polytechnical University
- Wenlong Yu
- Department of Surgery Oncology, Eastern Hepatobiliary Surgery Hospital
- Hongwei Li
- Department of Oncology, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine
- Changsheng Dong
- Department of Oncology, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine
- Zhou Zhou
- Department of Oncology, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine
- Xiyang Liu
- School of Computer Science and Technology, Xidian University
- Guanzhen Yu
- Department of Oncology, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine
- DOI
- https://doi.org/10.1038/s41467-021-21674-7
- Journal volume & issue
-
Vol. 12,
no. 1
pp. 1 – 13
Abstract
The ratio of tumour area to metastatic lymph node area (T/MLN) is a clinical indicator that can improve prognosis prediction of gastric cancer. Here, the authors use machine learning on whole slide images to generate a method that can predict metastatic lymph nodes and obtain T/MLN.
