Development of PDAC diagnosis and prognosis evaluation models based on machine learning.

Journal: BMC cancer
PMID: 40114140

Abstract

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is difficult to detect early and highly aggressive, often leading to poor patient prognosis. Existing serum biomarkers like CA19-9 are limited in early diagnosis, failing to meet clinical needs. Machine learning (ML)/deep learning (DL) technologies have shown great potential in biomedicine. This study aims to establish PDAC differential diagnosis and prognosis assessment models using ML combined with serum biomarkers for early diagnosis, risk stratification, and personalized treatment recommendations, improving early diagnosis rates and patient survival.

Authors

  • Yingqi Xiao
    Department of Pulmonary and Critical Care Medicine, Dongguan Tungwah Hospital, Dongguan, Guangdong, China.
  • Shixin Sun
    Department of Clinical Laboratory, The Fifth Medical Centre of Chinese PLA General Hospital, Beijing, China.
  • Naxin Zheng
    Department of Clinical Laboratory, The Fifth Medical Centre of Chinese PLA General Hospital, Beijing, China.
  • Jing Zhao
    Department of Pharmacy, Pharmacoepidemiology and Drug Safety Research Group, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway.
  • Xiaohan Li
    School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, China.
  • Jianmin Xu
    Department of Gastrointestinal Surgery, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu Province, 214122, China.
  • Haolian Li
    Department of Clinical Laboratory, The Fifth Medical Centre of Chinese PLA General Hospital, Beijing, China.
  • Chenran Du
    Department of Clinical Laboratory, The Fifth Medical Centre of Chinese PLA General Hospital, Beijing, China.
  • Lijun Zeng
    Department of Clinical Laboratory, The Fifth Medical Centre of Chinese PLA General Hospital, Beijing, China.
  • Juling Zhang
    Department of Clinical Laboratory, The Fifth Medical Centre of Chinese PLA General Hospital, Beijing, China.
  • Xiuyun Yin
    Department of Clinical Laboratory, The Fifth Medical Centre of Chinese PLA General Hospital, Beijing, China.
  • Yuan Huang
    School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, China.
  • Xuemei Yang
    Shandong Institute of Pomology, Taian, Shandong, China.
  • Fang Yuan
    Department of Pharmacy The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology Kunming China.
  • Xingwang Jia
    Department of Clinical Laboratory, Beijing Electric Power Teaching Hospital, Capital Medical University, Beijing, China. jiaxingw301@163.com.
  • Boan Li
    Department of Clinical Laboratory, The Fifth Medical Centre of Chinese PLA General Hospital, Beijing, China. lba@263.net.
  • Bo Li
    Electric Power Research Institute, Yunnan Power Grid Co., Ltd., Kunming, Yunnan, China.

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