Gd-EOB-DTPA-enhanced MRI radiomics and deep learning models to predict microvascular invasion in hepatocellular carcinoma: a multicenter study.

Journal: BMC medical imaging
PMID: 40165094

Abstract

BACKGROUND: Microvascular invasion (MVI) is an important risk factor for early postoperative recurrence of hepatocellular carcinoma (HCC). Based on gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) images, we developed a novel radiomics model. It combined bi-regional features and two machine learning algorithms. The aim of this study was to validate its potential value for preoperative prediction of MVI.

Authors

  • Zhu Zhu
    Department of Data and Information, The Children's Hospital Zhejiang University School of Medicine, Hangzhou 310052, China.
  • Kaiying Wu
    Department of Radiology, The First People's Hospital of Taicang, Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215400, China.
  • Jian Lu
    Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China.
  • Sunxian Dai
    Soochow university, Suzhou, Jiangsu, 215000, China.
  • Dabo Xu
  • Wei Fang
    GNSS Research Center, Wuhan University, Wuhan, 430079, China.
  • Yixing Yu
    Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215000, China. yuyixing@163.com.
  • Wenhao Gu
    Guangdong Jiyin Biotech Co. Ltd, Shenzhen, Guangdong, China.

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