A machine learning-based model analysis for serum markers of liver fibrosis in chronic hepatitis B patients.

Journal: Scientific reports
Published Date:

Abstract

Early assessment and accurate staging of liver fibrosis may be of great help for clinical diagnosis and treatment in patients with chronic hepatitis B (CHB). We aimed to identify serum markers and construct a machine learning (ML) model to reliably predict the stage of fibrosis in CHB patients. The clinical data of 618 CHB patients between February 2017 and September 2021 from Zhejiang Provincial People's Hospital were retrospectively analyzed, and these data as a training cohort to build the model. Six ML models were constructed based on logistic regression, support vector machine, Bayes, K-nearest neighbor, decision tree (DT) and random forest by using the maximum relevance minimum redundancy (mRMR) and gradient boosting decision tree (GBDT) dimensionality reduction selected features on the training cohort. Then, the resampling method was used to select the optimal ML model. In addition, a total of 571 patients from another hospital were used as an external validation cohort to verify the performance of the model. The DT model constructed based on five serological biomarkers included HBV-DNA, platelet, thrombin time, international normalized ratio and albumin, with the area under curve (AUC) values of the DT model for assessment of liver fibrosis stages (F0-1, F2, F3 and F4) in the training cohort were 0.898, 0.891, 0.907 and 0.944, respectively. The AUC values of the DT model for assessment of liver fibrosis stages (F0-1, F2, F3 and F4) in the external validation cohort were 0.906, 0.876, 0.931 and 0.933, respectively. The simulated risk classification based on the cutoff value showed that the classification performance of the DT model in distinguishing hepatic fibrosis stages can be accurately matched with pathological diagnosis results. ML model of five serum markers allows for accurate diagnosis of hepatic fibrosis stages, and beneficial for the clinical monitoring and treatment of CHB patients.

Authors

  • Congjie Zhang
    Center for Plastic & Reconstructive Surgery, Department of Dermatology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, China.
  • Zhenyu Shu
    Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou.
  • Shanshan Chen
    School of Life Sciences, Jilin University, Changchun, China.
  • Jiaxuan Peng
    Jinzhou Medical University, Jinzhou, Liaoning Province, China.
  • Yueyue Zhao
    Center for General Practice Medicine, Department of Infectious Diseases, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, 158 Shangtang Road, Hangzhou, Zhejiang, China.
  • Xuan Dai
    Center for General Practice Medicine, Department of Infectious Diseases, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, 158 Shangtang Road, Hangzhou, Zhejiang, China.
  • Jie Li
    Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence Application Technology Research Institute, Shenzhen Polytechnic University, Shenzhen, China.
  • Xuehan Zou
    Center for General Practice Medicine, Department of Infectious Diseases, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, 158 Shangtang Road, Hangzhou, Zhejiang, China.
  • Jianhua Hu
    Department of Orthorpaedic Surgery, Peking Union Medical College Hospital, No.1 Shuaifuyuan, Dongcheng district, Beijing, 100730, People's Republic of China. hujianhuapumch@126.com.
  • Haijun Huang
    Department of Infectious Disease, Medical Aiding Team for COVID-19 in Hubei, Zhejiang Provincial People's Hospital & People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, China.