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Development and validation of a model to predict the risk of distant metastases from hepatocellular carcinoma: a real-world retrospective study

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Abstract

Purpose

This study aimed to construct a novel clinical prediction model to predict the risk of distant metastases (DM) in hepatocellular carcinoma (HCC).

Methods

We included 3869 HCC patients, comprising 3076 patients from the Surveillance, Epidemiology, and End Results (SEER) database and 793 patients from a hospital in China. Variables with a P-value < 0.05 in the univariate logistic analysis were entered into the multivariate analysis to determine the independent predictive factors for DM in HCC. A nomogram was created based on the independent predictive factors. The predictive performance of the model was assessed using the receiver operating characteristics (ROCs) curve, decision curve analysis (DCA), calibration curves, and clinical impact curve analysis (CIC). Additionally, we developed a user-friendly web-based calculator based on the model.

Results

The multivariate logistic regression analysis revealed that tumor size (P < 0.001), type of treatment (P < 0.001), T stage (P = 0.001), N stage (P < 0.001), and grade (P = 0.043) were identified as independent predictive factors. A nomogram was constructed based on these factors. The area under the ROC curves (AUC) value was 0.845 (95% CI 0.815–0.874) for the training set, 0.818 (95% CI 0.774–0.863) for the internal validation set, and 0.823 (95% CI 0.770–0.876) for the external validation set. Moreover, DCA analysis, calibration curves, and CIC analysis demonstrated the favorable predictive performance of the nomogram. Finally, a more user-friendly web-based calculator was developed.

Conclusion

We developed a nomogram and showed its favorable predictive performance in predicting DM in HCC. Furthermore, we developed a more user-friendly web-based calculator, which has the potential to aid clinicians in individualized diagnosis and make better clinical decisions for HCC patients.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

DM:

Distant metastases

SEER:

Surveillance, Epidemiology, and End Results database

ROC:

Receiver operating characteristic

DCA:

Decision curve analysis

CI:

Confidence interval

AUC:

Area under the curve

OR:

Odds ratio

HCC:

Hepatocellular carcinoma

AFP:

Alpha-fetoprotein

SR:

Surgical resection

LT:

Liver transplantation

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. General Surgery Center, First Hospital of Jilin University, Changchun, China

    Guangzhao Shao, Zhongqi Fan, Wei Qiu & Guoyue Lv

Authors
  1. Guangzhao Shao
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  2. Zhongqi Fan
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  3. Wei Qiu
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  4. Guoyue Lv
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Contributions

Material preparation, data collection, and analysis were performed by GS, ZF, and WQ. The first draft of the manuscript was written by GS. GL was responsible for revising and finalizing the manuscript. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Guoyue Lv.

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Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

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This is an observational study. The ethics committee of the First Hospital of Jilin University has confirmed that no ethical approval is required.

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Informed consent was obtained from all individual participants included in the study.

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Shao, G., Fan, Z., Qiu, W. et al. Development and validation of a model to predict the risk of distant metastases from hepatocellular carcinoma: a real-world retrospective study. J Cancer Res Clin Oncol 149, 16489–16499 (2023). https://doi.org/10.1007/s00432-023-05361-2

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  • DOI: https://doi.org/10.1007/s00432-023-05361-2

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