Interpretable machine learning for predicting the response duration to Sintilimab plus chemotherapy in patients with advanced gastric or gastroesophageal junction cancer.

Journal: Frontiers in immunology
PMID: 38840913

Abstract

BACKGROUND: Sintilimab plus chemotherapy has proven effective as a combination immunotherapy for patients with advanced gastric and gastroesophageal junction adenocarcinoma (GC/GEJC). A multi-center study conducted in China revealed a median progression-free survival (PFS) of 7.1 months. However, the prediction of response duration to this immunotherapy has not been thoroughly investigated. Additionally, the potential of baseline laboratory features in predicting PFS remains largely unexplored. Therefore, we developed an interpretable machine learning (ML) framework, iPFS-SC, aimed at predicting PFS using baseline (pre-treatment) laboratory features and providing interpretations of the predictions.

Authors

  • Dan-Qi Wang
    Big Data Center, Affiliated Hospital of Jiangnan University, Wuxi, China.
  • Wen-Huan Xu
    Department of Oncology, Affiliated Hospital of Jiangnan University, Wuxi, China.
  • Xiao-Wei Cheng
    Department of Oncology, Affiliated Hospital of Jiangnan University, Wuxi, China.
  • Lei Hua
    Department of Computer and Information Science, Hefei University of Technology, Hefei, China.
  • Xiao-Song Ge
    Department of Oncology, Affiliated Hospital of Jiangnan University, Wuxi, China.
  • Li Liu
    Metanotitia Inc., Shenzhen, China.
  • Xiang Gao
    Department of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China.

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