Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence

Bao Feng, Jiangfeng Shi, Liebin Huang, Zhiqi Yang, Shi-Ting Feng, Jianpeng Li, Qinxian Chen, Huimin Xue, Xiangguang Chen, Cuixia Wan, Qinghui Hu, Enming Cui, Yehang Chen () and Wansheng Long ()
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Bao Feng: Jiangmen Central Hospital
Jiangfeng Shi: Guilin University of Aerospace Technology
Liebin Huang: Jiangmen Central Hospital
Zhiqi Yang: Meizhou People’s Hospital
Shi-Ting Feng: The First Affiliated Hospital of Sun Yat-sen University
Jianpeng Li: Dongguan People’s Hospital
Qinxian Chen: Jiangmen Central Hospital
Huimin Xue: Jiangmen Central Hospital
Xiangguang Chen: Meizhou People’s Hospital
Cuixia Wan: Meizhou People’s Hospital
Qinghui Hu: Guilin University of Aerospace Technology
Enming Cui: Jiangmen Central Hospital
Yehang Chen: Guilin University of Aerospace Technology
Wansheng Long: Jiangmen Central Hospital

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract The prediction of patient disease risk via computed tomography (CT) images and artificial intelligence techniques shows great potential. However, training a robust artificial intelligence model typically requires large-scale data support. In practice, the collection of medical data faces obstacles related to privacy protection. Therefore, the present study aims to establish a robust federated learning model to overcome the data island problem and identify high-risk patients with postoperative gastric cancer recurrence in a multicentre, cross-institution setting, thereby enabling robust treatment with significant value. In the present study, we collect data from four independent medical institutions for experimentation. The robust federated learning model algorithm yields area under the receiver operating characteristic curve (AUC) values of 0.710, 0.798, 0.809, and 0.869 across four data centres. Additionally, the effectiveness of the algorithm is evaluated, and both adaptive and common features are identified through analysis.

Date: 2024
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DOI: 10.1038/s41467-024-44946-4

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