Interpretable Machine Learning Models for Differentiating Glioblastoma From Solitary Brain Metastasis Using Radiomics.

Journal: Academic radiology
Published Date: May 27, 2025

Abstract

PURPOSE: To develop and validate interpretable machine learning models for differentiating glioblastoma (GB) from solitary brain metastasis (SBM) using radiomics features from contrast-enhanced T1-weighted MRI (CE-T1WI), and to compare the impact of low-order and high-order features on model performance.

Authors

  • Xueming Xia
    Division of Head & Neck Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China (X.X., Q.G.).
  • Wenjun Wu
    Department of Medical Education and Biomedical Informatics, University of Washington, Seattle, WA.
  • Qiaoyue Tan
    Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
  • Qiheng Gou
    Division of Head & Neck Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China (X.X., Q.G.). Electronic address: gouqiheng513@wchscu.cn.

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