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

  • Magnetic Resonance
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European Radiology Aims and scope Submit manuscript

Abstract

Objectives

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).

Results

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.

Conclusion

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.

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Abbreviations

AFP:

Alpha-fetoprotein

AST:

Aspartate transaminase

CT:

Center of the tumor

DAB:

Diaminobenzidine

DCA:

Decision curve analysis

Gd-EOB-DTPA:

Gadolinium-ethoxybenzyl-diethylenetriamine

GGT:

γ-Glutamyl transpeptadase

GLCM:

Gray level co-occurrence matrix

GLRCM:

Gray level run-length matrix

HBP:

Hepatobiliary phase

HCC:

Hepatocellular carcinoma

ICB:

Immune checkpoint blockade

ICC:

Intra-class correlation coefficient

IM:

Invasive margin

NPV:

Negative predictive value

PD-1:

Programmed death receptor 1

PD-L1:

Programmed death-ligand 1

PPV:

Positive predictive value

TIL:

Tumor infiltrating lymphocytes

TME:

Tumor microenvironment

VOI:

Volumes of interest

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Funding

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.

Author information

Author notes

  1. Shuling Chen and Shiting Feng contributed equally to this work.

Authors and Affiliations

  1. Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China

    Shuling Chen & Ming Kuang

  2. Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China

    Shiting Feng & Yingmei Jia

  3. Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Jingwei Wei, Fei Liu, Dongsheng Gu & Jie Tian

  4. Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China

    Jingwei Wei, Fei Liu, Dongsheng Gu & Jie Tian

  5. University of Chinese Academy of Sciences, Beijing, 100049, China

    Jingwei Wei, Fei Liu, Dongsheng Gu & Jie Tian

  6. Clinical Trial Unit, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China

    Bin Li & Sui Peng

  7. GE HealthCare China, Shanghai, 200000, China

    Xin Li

  8. Department of Mathematics, Jinan University, Guangzhou, 510632, China

    Yang Hou

  9. Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China

    Mimi Tang, Han Xiao & Sui Peng

  10. Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China

    Ming Kuang

Authors
  1. Shuling Chen
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Corresponding authors

Correspondence to Jie Tian or Ming Kuang.

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The scientific guarantor of this publication is Ming Kuang.

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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.

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Chen, S., Feng, S., Wei, J. et al. Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging. Eur Radiol 29, 4177–4187 (2019). https://doi.org/10.1007/s00330-018-5986-x

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  • DOI: https://doi.org/10.1007/s00330-018-5986-x

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