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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 Tian
  • , Ming Kuang*
  • *Corresponding author for this work
  • The First Affiliated Hospital of Sun Yat-sen University
  • CAS - Institute of Automation
  • Beijing Key Laboratory of Molecular Imaging
  • University of Chinese Academy of Sciences
  • GE Healthcare, China
  • Jinan University

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)4177-4187
Number of pages11
JournalEuropean Radiology
Volume29
Issue number8
DOIs
StatePublished - 1 Aug 2019
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Carcinoma
  • Gadolinium ethoxybenzyl DTPA
  • Hepatocellular
  • Immunotherapy
  • Magnetic resonance imaging

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