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Machine learning-based development and validation of a scoring system for progression-free survival in liver cancer

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Hepatology International Aims and scope Submit manuscript

Abstract

Object

Disease progression is an important factor affecting the long-term survival in hepatocellular carcinoma (HCC). The progression-free survival (PFS) has been used as a surrogate endpoint for overall survival (OS) in many solid tumors. However, there were few models to predict the PFS in HCC patients. This study aimed to explore the prognostic factors that affect the PFS in HCC and establish an individualized prediction model.

Methods

We included 2890 patients with hepatitis B-related HCC hospitalized at Beijing Ditan Hospital, Capital Medical University and randomly divided into training and validation cohort. Cox multivariate regression was used to analyze independent risk factors affecting the 1-year PFS of HCC, and an artificial neural networks (ANNs) model was constructed. C-index, calibration curve, and decision curve analysis were used to evaluate the performance of the model.

Results

The median survival time was 26.2 m (95% CI: 24.08–28.32) and the 1-year PFS rate was 52.3% in whole study population. Cox multivariate regression showed smoking history, tumor number ≥ 2, tumor size ≥ 5 cm, portal vein tumor thrombus, WBC, NLR, γ-GGT, ALP, and AFP ≥ 400 ng/mL were risk factors for 1-year progression-free survival, while albumin and CD4 T cell counts were protective factors in HCC patients. A prediction model for 1-year PFS was constructed (https://lixuan.me/annmodel/myg-v3/). The ANNs model’s ability to predict 1-year PFS had an area under the receiver operating characteristic curve (AUROC) of 0.866 (95% CI 0.848–0.884) in HCC patients, which was higher than predicted by TNM, BCLC, Okuda, CLIP, CUPI, JIS, and ALBI scores (p < 0.0001). In addition, the ANNs model could also estimate the probability of 1-year OS and presented a higher AUROC value, 0.877 (95% CI 0.858–0.895), than those other models. All patients were divided into high-, medium-, and low-risk groups, according to the ANNs model scores. Compared with the hazard ratios (HRs) of PFS and OS in low-risk group, those in the high-risk group were 26.42 (95% CI 18.74–37.25; p < 0.0001) and 11.26 (95% CI 9.11–13.93; p < 0.0001), respectively.

Conclusion

The ANNs model has good individualized prediction performance and may be helpful to evaluate the probability of progression-free survival in HCC during clinical practice.

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Funding

This work was supported by the National Science Foundation of China (No. 81874435), Application of Clinical Features of Capital City of Science and Technology commission (No. Z171100001017082), Dengfeng Talent Support Program of Beijing Municipal Administration of Hospitals (No. DFL20191803).

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Author notes

  1. Xiaoli Liu and Yixin Hou have contributed equally to this work.

Authors and Affiliations

  1. Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, No. 8 Jing Shun East Street, Beijing, 100015, China

    Xiaoli Liu, Yixin Hou, Xinhui Wang, Lihua Yu, Xianbo Wang & Zhiyun Yang

  2. Department of Hepatobiliary Surgery, Beijing Ditan Hospital, Capital Medical University, No. 8 Jing Shun East Street, Beijing, 100015, China

    Li Jiang

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  1. Xiaoli Liu
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  2. Yixin Hou
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Correspondence to Li Jiang or Zhiyun Yang.

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The authors confirm that there are no known conflicts of interest associated with this study.

Ethical approval

The study was approved by the Ethics Committee of Beijing Ditan Hospital, Capital Medical University. Written informed consent was obtained from each patient. All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008.

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Liu, X., Hou, Y., Wang, X. et al. Machine learning-based development and validation of a scoring system for progression-free survival in liver cancer. Hepatol Int 14, 567–576 (2020). https://doi.org/10.1007/s12072-020-10046-w

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  • DOI: https://doi.org/10.1007/s12072-020-10046-w

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