Radiomics of Dynamic Contrast-Enhanced MRI for Predicting Radiation-Induced Hepatic Toxicity After Intensity Modulated Radiotherapy for Hepatocellular Carcinoma: A Machine Learning Predictive Model Based on the SHAP Methodology.

Journal: Journal of hepatocellular carcinoma
Published Date: May 17, 2025

Abstract

OBJECTIVE: To develop an interpretable machine learning (ML) model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic data, dosimetric parameters, and clinical data for predicting radiation-induced hepatic toxicity (RIHT) in patients with hepatocellular carcinoma (HCC) following intensity-modulated radiation therapy (IMRT).

Authors

  • Fushuang Liu
    Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530001, People's Republic of China.
  • Lijun Chen
    Department of Obstetrics and Gynecology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong China.
  • Qiaoyuan Wu
    Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530001, People's Republic of China.
  • Liqing Li
    Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530001, People's Republic of China.
  • Jizhou Li
  • Tingshi Su
    Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530001, People's Republic of China.
  • Jianxu Li
    Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530001, People's Republic of China.
  • Shixiong Liang
    Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530001, People's Republic of China.
  • Liping Qing
    Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530001, People's Republic of China.

Keywords

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