European Radiology

, Volume 30, Issue 2, pp 1232–1242 | Cite as

Noninvasive prediction of HCC with progenitor phenotype based on gadoxetic acid-enhanced MRI

  • Jie Chen
  • Zhenru Wu
  • Chunchao Xia
  • Hanyu Jiang
  • Xijiao Liu
  • Ting Duan
  • Likun Cao
  • Zheng Ye
  • Zhen Zhang
  • Ling Ma
  • Bin SongEmail author
  • Yujun ShiEmail author



To explore the noninvasive prediction of hepatocellular carcinoma (HCC) with progenitor phenotype based on gadoxetic acid-enhanced magnetic resonance imaging (MRI).


This retrospective study included 115 surgery-proven HCCs with preoperative gadoxetic acid-enhanced MRI from August 2015 to September 2018. Image features were reviewed. Quantitative image analysis was performed using histogram analysis. HCC with progenitor phenotype was defined as positive for either cytokeratin 19 (CK19) or epithelial cell adhesion molecule (EpCAM) expression. Statistically significant variables for identifying HCCs with progenitor phenotype were determined at multivariate analyses. ROC analyses were used to determined cutoff values and the diagnostic performance of significant variables and combinations. Prediction nomogram was constructed based on multivariate analysis.


At multivariate regression analyses, AFP ≥ 155.25 ng/mL (p < 0.001), skewness on T2WI ≤ 1.10 (p = 0.024), uniformity on pre-T1WI ≤ 0.91 (p = 0.024), irregular tumor margin (p = 0.006), targetoid appearance (p = 0.001), and the absence of mosaic architecture (p = 0.014) were significant predictors of HCCs expressing progenitor cell markers. Combing any three of those significant variables, it provides a diagnostic accuracy of 0.86 (95% CI 0.78–0.92) with sensitivity of 0.97 (95% CI 0.86–1.00), and specificity of 0.74 (95% CI 0.63–0.83). The C-index of the regression coefficient-based nomogram was 0.94 (95% CI 0.91–0.98).


Noninvasive prediction of HCCs with progenitor phenotype can be achieved with high accuracy by integrated interpretation of biochemical and radiological information, representing a handy tool for precise patient management and the prediction of prognosis.

Key Points

• Qualitative image features of irregular tumor margin, targetoid appearance, and the absence of mosaic architecture are significant predictors of hepatocellular carcinoma with progenitor phenotype.

• Quantitative analyses using whole-lesion histogram analysis provides additional information for the prediction of hepatocellular carcinoma with progenitor phenotype.

• Noninvasive prediction of hepatocellular carcinoma with progenitor phenotype can be achieved with high accuracy by integrated interpretation of clinical information and qualitative and quantitative imaging analyses.


Hepatocellular carcinoma Cytokeratin 19 Epithelial cell adhesion molecule Magnetic resonance imaging Nomogram 



Apparent diffusion coefficient


Alpha fetal protein


Confidence interval


Cytokeratin 19


Epithelial adhesion molecules


Hepatobiliary phase


Hepatocellular carcinoma


Magnetic resonance imaging


Pre-enhancement T1-weighted imaging


Signal intensity


T2-weighted imaging



This study has received funding from the Science and Technology Support Program of Sichuan Province (Grant Number 2017SZ0003).

Compliance with ethical standards


The scientific guarantor of this publication is Bin Song.

Conflict of interest

The authors declare that they have no conflict of interest.

Statistics and biometry

No complex statistical methods were necessary for this study.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

An Institutional Review Board approval was obtained.


• retrospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2019_6414_MOESM1_ESM.docx (21 kb)
ESM 1 (DOCX 21 kb)


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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.West China School of MedicineSichuan UniversityChengduChina
  2. 2.Laboratory of Pathology, West China HospitalSichuan UniversityChengduChina
  3. 3.Department of Radiology, West China HospitalSichuan UniversityChengduChina
  4. 4.Application Advanced TeamGE HealthcareShanghaiChina

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