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Radiomics-based model using gadoxetic acid disodium-enhanced MR images: associations with recurrence-free survival of patients with hepatocellular carcinoma treated by surgical resection

  • Hepatobiliary
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Abstract

Purpose

To develop a prediction model that combined magnetic resonance images (MRI)-based radiomics features with clinical factors to predict recurrence-free survival (RFS) of hepatocellular carcinoma (HCC) patients treated with surgical resection.

Methods

HCC patients treated with surgical resection (n = 153) were randomly divided into training (n = 107) and validation (n = 46) datasets. The volumes of interest were manually outlined around the lesion and additional 2 mm and 5 mm peritumoral areas were created with automated dilatation in MRI to extract tumoral (T) and peritumoral (PT) radiomics features. The radiomics models were constructed using least absolute shrinkage and selection operator Cox regression. The combined model incorporated clinical factors and radiomics features using multivariable Cox regression based on the Akaike information criterion principle. Predictive performance of different models were evaluated by receiver operating characteristic (ROC) curves, decision curves, and calibration curves.

Results

Among the radiomics models, similar performance was observed in the 2 mm and 5 mm PT models (C-index both 0.657), which were better than the T model or T + PT model (C-index 0.607 and 0.641, respectively) in the validation dataset, whereas the model combined with the three identified clinical risk factors showed the best performance (C-index 0.725). Results of the ROC curves, decision curves, and the calibration curves indicated that the combined model and the derived nomogram had better prediction performance, greater clinical benefits, and fair calibration efficiency.

Conclusion

The prediction model that combined MRI radiomics signatures with clinical factors can effectively predict the prognosis of patients with HCC treated with surgical resection.

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Data availability

The raw dataset contained comprehensive and detailed basic information of patents, which was backed up concretely and critically in the research department of Sun Yat-sen University Cancer Center. The datasets during or analyzed during the current study are available from the corresponding author on reasonable request, except private information of participants.

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Authors and Affiliations

  1. Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China

    Ling Zhang, Jianming Hu, Xinhua Jiang & Li Tian

  2. Department of Liver Surgery, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China

    Jingyu Hou

  3. Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China

    Lei Guo

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  1. Ling Zhang
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  2. Jianming Hu
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This study was approved by the institutional review board of the Sun Yat-sen University Cancer Center.

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Zhang, L., Hu, J., Hou, J. et al. Radiomics-based model using gadoxetic acid disodium-enhanced MR images: associations with recurrence-free survival of patients with hepatocellular carcinoma treated by surgical resection. Abdom Radiol 46, 3845–3854 (2021). https://doi.org/10.1007/s00261-021-03034-7

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