Prediction model of gastrointestinal tumor malignancy based on coagulation indicators such as TEG and neural networks.

Journal: Frontiers in immunology
PMID: 40201179

Abstract

OBJECTIVES: Accurate determination of gastrointestinal tumor malignancy is a crucial focus of clinical research. Constructing coagulation index models using big data is feasible to achieve this goal. This study builds various prediction models through machine learning methods based on the different coagulation statuses under varying malignancy levels of gastrointestinal tumors. The aim is to use coagulation indicators to predict the malignancy of gastrointestinal tumors, expand the methods and ideas for coagulation index tumor prediction, and identify independent risk factors for gastrointestinal tumor malignancy.

Authors

  • Fulong Yu
    Department of General Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
  • Chudi Sun
    School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu, China.
  • Liang Li
    School of Psychological and Cognitive Sciences, Peking University, Beijing, 100871, China.
  • Xiaoyu Yu
    Department of Gastroenterology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
  • Shumin Shen
    Department of Oncology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
  • Hao Qiang
    Department of General Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
  • Song Wang
    Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, China.
  • Xianghua Li
    Department of Molecular Pathology, Hefei Da'an Medical Laboratory Co., Ltd., Hefei, Anhui, China.
  • Lin Zhang
    Laboratory of Molecular Translational Medicine, Centre for Translational Medicine, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Clinical Research Center for Birth Defects of Sichuan Province, West China Second Hospital, Sichuan University, Chengdu, Sichuan, 610041, China. Electronic address: zhanglin@scu.edu.cn.
  • Zhining Liu
    Department of General Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.

Keywords

External Resources

Popular Topics
Recent Journals