Accuracy of machine learning in diagnosing microsatellite instability in gastric cancer: A systematic review and meta-analysis.

Journal: International journal of medical informatics
PMID: 39515046

Abstract

BACKGROUND: Significant challenges persist in the early identification of microsatellite instability (MSI) within current clinical practice. In recent years, with the growing utilization of machine learning (ML) in the diagnosis and management of gastric cancer (GC), numerous researchers have explored the effectiveness of ML methodologies in detecting MSI. Nevertheless, the predictive value of these approaches still lacks comprehensive evidence. Accordingly, this study was carried out to consolidate the accuracy of ML in the prompt detection of MSI in GC.

Authors

  • Yuou Ying
    The Second Affiliated College of Zhejiang Chinese Medical University, Hangzhou 310053, Zhejiang Province, China.
  • Ruyi Ju
    Zhejiang Chinese Medical University, Hangzhou 310053, Zhejiang Province, China.
  • Jieyi Wang
    The Basic Medical College of Zhejiang Chinese Medical University, Hangzhou 310053, Zhejiang Province, China.
  • Wenkai Li
    Zhejiang Chinese Medical University, Hangzhou 310053, Zhejiang Province, China.
  • Yuan Ji
    Department of Public Health Sciences, The University of Chicago, 5841 S. Maryland Ave, MC2000, Chicago, IL 60637, USA.
  • Zhenyu Shi
    Department of Vascular Surgery, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
  • Jinhan Chen
    The Second Affiliated College of Zhejiang Chinese Medical University, Hangzhou 310053, Zhejiang Province, China.
  • Mingxian Chen
    Department of Gastroenterology, Tongde Hospital of Zhejiang Province, Street Gucui No. 234, Region Xihu, Hangzhou 310012, Zhejiang Province, China. Electronic address: chenmingxian2005@126.com.

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