Radiomics-based Machine Learning to Predict the Recurrence of Hepatocellular Carcinoma: A Systematic Review and Meta-analysis.

Journal: Academic radiology
Published Date: Oct 20, 2023

Abstract

RATIONALE AND OBJECTIVES: Recurrence of hepatocellular carcinoma (HCC) is a major concern in its management. Accurately predicting the risk of recurrence is crucial for determining appropriate treatment strategies and improving patient outcomes. A certain amount of radiomics models for HCC recurrence prediction have been proposed. This study aimed to assess the role of radiomics models in the prediction of HCC recurrence and to evaluate their methodological quality.

Authors

  • Jin Jin
    Siemens Healthcare Pty Ltd, Brisbane, Queensland, Australia.
  • Ying Jiang
    Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, 230601 Hefei, China; Key Laboratory of Opto-Electronic Information Acquisition and Manipulation of Ministry of Education, Anhui University, 230601 Hefei, China.
  • Yu-Lan Zhao
    Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, P.R. China (J.J., Y.J., Y.-L.Z., P.-L.H.).
  • Pin-Tong Huang
    Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, P.R. China (J.J., Y.J., Y.-L.Z., P.-L.H.); Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, P.R. China (P.-L.H.); Research Center for Life Science and Human Health, Binjiang Institute of Zhejiang University, Hangzhou, P.R. China (P.-L.H.). Electronic address: huangpintong@zju.edu.cn.

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