E-ISSN: 1305-3612
    Radiomics-based nomogram using CT imaging for noninvasive preoperative prediction of early recurrence in patients with hepatocellular carcinoma
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    Abdominal Imaging - Original Article
    VOLUME: 26 ISSUE: 5
    P: 411-419
    September 2020

    Radiomics-based nomogram using CT imaging for noninvasive preoperative prediction of early recurrence in patients with hepatocellular carcinoma

    Diagn Interv Radiol 2020;26(5):411-419
    DOI: 10.5152/dir.2020.19623
    Hong-Bo Zhu 1,2
    Ze-Yu Zheng 3
    Heng Zhao 4
    Jing Zhang 3
    Hong Zhu 5
    Yue-Hua Li 2
    Zhong-Yi Dong 6
    Lu-Shan Xiao 1
    Jun-Jie Kuang 6
    Xiao-Li Zhang 7
    Li Liu 1
    1. Hepatology Unit and Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
    2. Department of Oncology, the First Affiliated Hospital of University of South China, Hengyang, China
    3. Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
    4. Department of Radiology, the First Affiliated Hospital of University of South China, Hengyang, China
    5. Information Management and Big Data Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
    6. Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China
    7. Department of Pathology, the First Affiliated Hospital of University of South China, Hengyang, China
    No information available.
    No information available
    Received Date: 04.12.2019
    Accepted Date: 19.03.2020
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    ABSTRACT

    PURPOSE:

    The aim of this study was to develop and validate a radiomics nomogram based on radiomics features and clinical data for the non-invasive preoperative prediction of early recurrence (≤2 years) in patients with hepatocellular carcinoma (HCC).

    METHODS:

    We enrolled 262 HCC patients who underwent preoperative contrast-enhanced computed tomography and curative resection (training cohort, n=214; validation cohort, n=48). We applied propensity score matching (PSM) to eliminate redundancy between clinical characteristics and image features, and the least absolute shrinkage and selection operator (LASSO) was used to prevent overfitting. Next, a radiomics signature, clinical nomogram, and combined clinical-radiomics nomogram were built to predict early recurrence, and we compared the performance and generalization of these models.

    RESULTS:

    The radiomics signature stratified patients into low-risk and high-risk, which show significantly difference in recurrence free survival and overall survival (P ≤ 0.01). Multivariable analysis identified dichotomised radiomics signature, alpha fetoprotein, and tumour number and size as key early recurrence indicators, which were incorporated into clinical and radiomics nomograms. The radiomics nomogram showed the highest area under the receiver operating characteristic curve (AUC), with significantly superior predictive performance over the clinical nomogram in the training cohort (0.800 vs 0.716, respectively; P = 0.001) and the validation cohort (0.785 vs 0.654, respectively; P = 0.039).

    CONCLUSION:

    The radiomics nomogram is a non-invasive preoperative biomarker for predicting early recurrence in patients with HCC. This model may be of clinical utility for guiding surveillance follow-ups and identifying optimal interventional strategies.

    References

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