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CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma

Abdominal Radiology volume 42, pages 1695–1704 (2017)Cite this article

Abstract

Purpose

To develop a CT-based radiomics signature and assess its ability for preoperatively predicting the early recurrence (≤1 year) of hepatocellular carcinoma (HCC).

Methods

A total of 215 HCC patients who underwent partial hepatectomy were enrolled in this retrospective study, and all the patients were followed up at least within 1 year. Radiomics features were extracted from arterial- and portal venous-phase CT images, and a radiomics signature was built by the least absolute shrinkage and selection operator (LASSO) logistic regression model. Preoperative clinical factors associated with early recurrence were evaluated. A radiomics signature, a clinical model, and a combined model were built, and the area under the curve (AUC) of operating characteristics (ROC) was used to explore their performance to discriminate early recurrence.

Results

Twenty-one radiomics features were chosen from 300 candidate features to build a radiomics signature that was significantly associated with early recurrence (P < 0.001), and they presented good performance in the discrimination of early recurrence alone with an AUC of 0.817 (95% CI: 0.758–0.866), sensitivity of 0.794, and specificity of 0.699. The AUCs of the clinical and combined models were 0.781 (95% CI: 0.719–0.834) and 0.836 (95% CI: 0.779–0.883), respectively, with the sensitivity being 0.784 and 0.824, and the specificity being 0.619 and 0.708, respectively. Adding a radiomics signature into conventional clinical variables can significantly improve the accuracy of the preoperative model in predicting early recurrence (P = 0.01).

Conclusions

The radiomics signature was a significant predictor for early recurrence in HCC. Incorporating radiomics signature into conventional clinical factors performed better for preoperative estimation of early recurrence than with clinical variables alone.

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Acknowledgments

We deeply appreciated the Medical Record Management Center for its close cooperation in data collection, sorting, verification, and database creation.

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    Affiliations

    1. Graduate College, Southern Medical University, Guangzhou, 510515, China

      Ying Zhou, Shuting Chen, Penqi Wu, Zaiyi Liu & Changhong Liang

    2. Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China

      Ying Zhou, Yanqi Huang, Shuting Chen, Penqi Wu, Weitao Ye, Zaiyi Liu & Changhong Liang

    3. Department of Radiology, Mianyang Central Hospital, Mianyang, 621000, Sichuan Province, China

      Ying Zhou

    4. School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China

      Lan He

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    1. Ying Zhou
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    Correspondence to Zaiyi Liu or Changhong Liang.

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    Funding

    This study was funded by the National Natural Scientific Foundation of China (Grant Numbers: 81271569, 81271654 and U1301258).

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    The authors declare that they have no conflict of interest.

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    All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this type of study, formal consent is not required.

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    Ying Zhou and Lan He contributed equally to this work.

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    Zhou, Y., He, L., Huang, Y. et al. CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma. Abdom Radiol 42, 1695–1704 (2017). https://doi.org/10.1007/s00261-017-1072-0

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    Keywords

    • Hepatocellular carcinoma
    • Computed tomography
    • Radiomics signature
    • Predictor
    • Recurrence