Development and Validation of a Machine Learning-Based Model Used for Predicting Hepatocellular Carcinoma Risk in Patients with Hepatitis B-Related Cirrhosis: A Retrospective Study

Abstract

Object

Our objective was to estimate the 5-year cumulative risk of HCC in patients with HBC by utilizing an artificial neural network (ANN).

Methods

We conducted this study with 1589 patients hospitalized at Beijing Ditan Hospital of Capital Medical University and People’s Liberation Army Fifth Medical Center. The training cohort consisted of 913 subjects from Beijing Ditan Hospital of Capital Medical University, while the validation cohort comprised 676 subjects from People’s Liberation Army Fifth Medical Center. Through univariate analysis, we identified factors that independently influenced the occurrence of HCC, which were then used to develop the ANN model. To evaluate the ANN model, we assessed its predictive accuracy, discriminative ability, and clinical net benefit using metrics such as the area under the receiver operating characteristic curve (AUC), concordance index (C-index), calibration curves.

Results

In total, we included nine independent risk factors in the development of the ANN model. Remarkably, the AUC of the ANN model was 0.880, significantly outperforming the AUC values of other existing models including mPAGE-B (0.719) (95% CI 0.670–0.768), PAGE-B (0. 710) (95% CI 0.660–0.759), FIB-4 (0.693) (95% CI 0.640–0.745), and Toronto hepatoma risk index (THRI) (0.705) (95% CI 0.654–0.756) (p<0.001 for all). The ANN model effectively stratified patients into low, medium, and high-risk groups based on their 5-year In the training cohort, the positive predictive value (PPV) for low-risk patients was 26.2% (95% CI 25.0–27.4), and the negative predictive value (NPV) was 98.7% (95% CI 95.2–99.7). For high-risk patients, the PPV was 54.7% (95% CI 48.6–60.7), and the NPV was 91.6% (95% CI 89.4–93.4). These findings were validated in the independent validation cohort.

Conclusion

The ANNs model has good individualized prediction performance and may be helpful to evaluate the probability of the 5-year risk of HCC in patients with HBC.

Keywords:

• machine learning-based model
• hepatocellular carcinoma
• risk
• hepatitis B-related cirrhosis

Summary

This study employed an ANN model to construct a predictive tool for estimating the 5-year risk of HCC development in patients with HBC undergoing antiviral therapy. The ANN model demonstrated promising individualized prediction performance, thereby offering valuable assessment of HCC risk in clinical settings for patients with HBC.

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.

Disclosure

The authors declare that they have no conflicts of interest with regard to the publication of this research report.

Additional information

Funding

This work was supported by Beijing Hospitals Authority Youth Programme (QMl220201802), the Beijing Traditional Chinese medicine science and Technology Development Fund Project (No. Qn-2020-25), Application of Clinical Features of Capital City of Science and Technology commission (z181100001718052),High-level public health technical personnel construction project (The backbone of the discipline-03-07), National Key R&D Program of China(20232023YFC230880).