Personalized prediction of postoperative complication and survival among Colorectal Liver Metastases Patients Receiving Simultaneous Resection using machine learning approaches: A multi-center study.

Journal: Cancer letters
Published Date: May 18, 2024

Abstract

BACKGROUND: To predict clinical important outcomes for colorectal liver metastases (CRLM) patients receiving colorectal resection with simultaneous liver resection by integrating demographic, clinical, laboratory, and genetic data.

Authors

  • Qichen Chen
    Department of Colorectal Surgery, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
  • Jinghua Chen
    Jiangxi Province Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, 330006, People's Republic of China.
  • Yiqiao Deng
    Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Xinyu Bi
    Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China.
  • Jianjun Zhao
    School of Computer Science and Technology, Soochow University, Suzhou 215006, China.
  • Jianguo Zhou
    Department of Imaging, Lianyungang Hospital of Traditional Chinese Medicine, Lianyungang 222000, Jiangsu, China.
  • Zhen Huang
    Division of Medical Technology Development, Hangzhou Zhiwei Information & Technology Ltd., Hangzhou, Hangzhou, China.
  • Jianqiang Cai
    Department of Hepatobiliary Surgery, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
  • Baocai Xing
    Key Laboratory of Carcinogenesis and Translational Research, Hepatopancreatobiliary Surgery Department I, School of Oncology, Beijing Cancer Hospital and Institute, Peking University, Ministry of Education, Beijing, China. Electronic address: xingbaocai88@sina.com.
  • Yuan Li
    NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, China.
  • Kan Li
    State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, PR China.
  • Hong Zhao
    Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China.

Keywords

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