Radiomics-based automated machine learning for differentiating focal liver lesions on unenhanced computed tomography.

Journal: Abdominal radiology (New York)
PMID: 39572431

Abstract

BACKGROUND & AIMS: Enhanced computed tomography (CT) is the primary method for focal liver lesion diagnosis. We aimed to use automated machine learning (AutoML) algorithms to differentiate between benign and malignant focal liver lesions on the basis of radiomics from unenhanced CT images.

Authors

  • Nan Yang
    Department of Infectious Disease, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China. Electronic address: Shmily.1989.2008@stu.xjtu.edu.cn.
  • Zhuangxuan Ma
    Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China.
  • Ling Zhang
  • Wenbin Ji
    Department of Radiology, Taizhou Hospital, Zhejiang University, Taizhou, Zhejiang, China.
  • Qian Xi
    Department of Radiology, Eye & ENT Hospital of Fudan University, Shanghai, China. xiqian1129@163.com.
  • Ming Li
    Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, China.
  • Liang Jin
    Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, China.

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