Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging

  • Shuling Chen
  • Shiting Feng
  • Jingwei Wei
  • Fei Liu
  • Bin Li
  • Xin Li
  • Yang Hou
  • Dongsheng Gu
  • Mimi Tang
  • Han Xiao
  • Yingmei Jia
  • Sui Peng
  • Jie TianEmail author
  • Ming KuangEmail author
Magnetic Resonance



Immunoscore evaluates the density of CD3+ and CD8+ T cells in both the tumor core and invasive margin. Pretreatment prediction of immunoscore in hepatocellular cancer (HCC) is important for precision immunotherapy. We aimed to develop a radiomics model based on gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced MRI for pretreatment prediction of immunoscore (0–2 vs. 3–4) in HCC.

Materials and methods

The study included 207 (training cohort: n = 150; validation cohort: n = 57) HCC patients with hepatectomy who underwent preoperative Gd-EOB-DTPA-enhanced MRI. The volumes of interest enclosing hepatic lesions including intratumoral and peritumoral regions were manually delineated in the hepatobiliary phase of MRI images, from which 1044 quantitative features were extracted and analyzed. Extremely randomized tree method was used to select radiomics features for building radiomics model. Predicting performance in immunoscore was compared among three models: (1) using only intratumoral radiomics features (intratumoral radiomics model); (2) using combined intratumoral and peritumoral radiomics features (combined radiomics model); (3) using clinical data and selected combined radiomics features (combined radiomics-based clinical model).


The combined radiomics model showed a better predicting performance in immunoscore than intratumoral radiomics model (AUC, 0.904 (95% CI 0.855–0.953) vs. 0.823 (95% CI 0.747–0.899)). The combined radiomics-based clinical model showed an improvement over the combined radiomics model in predicting immunoscore (AUC, 0·926 (95% CI 0·884–0·967) vs. 0·904 (95% CI 0·855–0·953)), although differences were not statistically significant. Results were confirmed in validation cohort and calibration curves showed good agreement.


The MRI-based combined radiomics nomogram is effective in predicting immunoscore in HCC and may help making treatment decisions.

Key Points

• Radiomics obtained from Gd-EOB-DTPA-enhanced MRI help predicting immunoscore in hepatocellular carcinoma.

• Combined intratumoral and peritumoral radiomics are superior to intratumoral radiomics only in predicting immunoscore.

• We developed a combined clinical and radiomicsnomogram to predict immunoscore in hepatocellular carcinoma.


Carcinoma Hepatocellular Gadolinium ethoxybenzyl DTPA Magnetic resonance imaging Immunotherapy 





Aspartate transaminase


Center of the tumor




Decision curve analysis




γ-Glutamyl transpeptadase


Gray level co-occurrence matrix


Gray level run-length matrix


Hepatobiliary phase


Hepatocellular carcinoma


Immune checkpoint blockade


Intra-class correlation coefficient


Invasive margin


Negative predictive value


Programmed death receptor 1


Programmed death-ligand 1


Positive predictive value


Tumor infiltrating lymphocytes


Tumor microenvironment


Volumes of interest



This study has received funding by grants from the Guangzhou Science and Technology Program key projects (No. 201803010057) and the National Natural Science Foundation of China (No. 81771908, 81571750). This work was supported by Ministry of Science and Technology of China under Grant No. 2017YFA0205200, National Natural Science Foundation of China under Grant No. 81227901, 81527805, Chinese Academy of Sciences under Grant No. GJJSTD20170004 and QYZDJ-SSW-JSC005, Beijing Municipal Science & Technology Commission under Grant No. Z161100002616022, Z171100000117023, the Key International Cooperation Projects of the Chinese Academy of Sciences under Grant No. 173211KYSB20160053. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Compliance with ethical standards


The scientific guarantor of this publication is Ming Kuang.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

Two of the authors (Fei Liu, Bin Li) have significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.


• retrospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2018_5986_MOESM1_ESM.docx (12.2 mb)
ESM 1 (DOCX 12520 kb)


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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Department of Medical Ultrasonics, Institute of Diagnostic and Interventional UltrasoundThe First Affiliated Hospital of Sun Yat-sen UniversityGuangzhouChina
  2. 2.Department of RadiologyThe First Affiliated Hospital of Sun Yat-sen UniversityGuangzhouChina
  3. 3.Key Laboratory of Molecular Imaging, Institute of AutomationChinese Academy of SciencesBeijingChina
  4. 4.Beijing Key Laboratory of Molecular ImagingBeijingChina
  5. 5.University of Chinese Academy of SciencesBeijingChina
  6. 6.Clinical Trial UnitThe First Affiliated Hospital of Sun Yat-sen UniversityGuangzhouChina
  7. 7.GE HealthCare ChinaShanghaiChina
  8. 8.Department of MathematicsJinan UniversityGuangzhouChina
  9. 9.Department of Gastroenterology and HepatologyThe First Affiliated Hospital of Sun Yat-sen UniversityGuangzhouChina
  10. 10.Department of Liver SurgeryThe First Affiliated Hospital of Sun Yat-sen UniversityGuangzhouChina

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