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European Radiology
Published in: European Radiology 8/2019

01-08-2019 | Hepatocellular Carcinoma | Magnetic Resonance

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

Authors: 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

Published in: European Radiology | Issue 8/2019

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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.
Appendix
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Metadata
Title
Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging
Authors
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
Publication date
01-08-2019
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 8/2019
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-018-5986-x

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