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Background <p>Aggressive recurrence (AR) is an important factor affecting prognosis after surgery for hepatocellular carcinoma (HCC). This study aimed to establish and evaluate a visual calculator to predict AR using by machine learning (ML) model.</p> Methods <p>Patients diagnosed with HCC at an early stage were reviewed. The prediction ability of each model was evaluated using accuracy, sensitivity, specificity, precision, F1 score, and the area under the curve (AUC). Then, the model’s prediction performance was evaluated by calibration curves, decision curve analysis (DCA), and precision–recall curves (PRC).</p> Results <p>483 patients were ultimately included in this study. The baseline characteristics indicate that patients in the AR group exhibit poorer liver function and more advanced tumor features. Then, nine risk features were identified and incorporated into the nine development models, respectively. Among the models, the XGBoost model showed the best prediction ability (AUC 0.986, 95% CI: 0.983–0.988). Calibration curves, DCA, and PRC further demonstrated the robust performance and clinical applicability. Then, A web-based calculator was built.</p> Conclusion <p>An explainable XGBoost model to predict AR for patients with early-stage HCC after surgery was feasible and effective, suggesting its superior potential in tailoring surgical strategies and optimizing personalized postoperative treatment plans.</p>

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Development and validation of a personalized web-based calculator of aggressive recurrence after surgery for early-stage hepatocellular carcinoma by machine learning

  • 期刊论文

  • 发表日期：

    2025年11月24日

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  • Zi-Chen Yu,
  • Kai Wang,
  • Wen-Feng Lu,
  • Zheng-Kang Fang,
  • Kai-Di Wang,
  • Yang Yu,
  • Zi-Yang Bao,
  • Zhe-Jin Shi,
  • Jun-Wei Liu,
  • Dong-Sheng Huang,
  • Cheng-Wu Zhang,
  • Lei Liang

摘要

Background

Aggressive recurrence (AR) is an important factor affecting prognosis after surgery for hepatocellular carcinoma (HCC). This study aimed to establish and evaluate a visual calculator to predict AR using by machine learning (ML) model.

Methods

Patients diagnosed with HCC at an early stage were reviewed. The prediction ability of each model was evaluated using accuracy, sensitivity, specificity, precision, F1 score, and the area under the curve (AUC). Then, the model’s prediction performance was evaluated by calibration curves, decision curve analysis (DCA), and precision–recall curves (PRC).

Results

483 patients were ultimately included in this study. The baseline characteristics indicate that patients in the AR group exhibit poorer liver function and more advanced tumor features. Then, nine risk features were identified and incorporated into the nine development models, respectively. Among the models, the XGBoost model showed the best prediction ability (AUC 0.986, 95% CI: 0.983–0.988). Calibration curves, DCA, and PRC further demonstrated the robust performance and clinical applicability. Then, A web-based calculator was built.

Conclusion

An explainable XGBoost model to predict AR for patients with early-stage HCC after surgery was feasible and effective, suggesting its superior potential in tailoring surgical strategies and optimizing personalized postoperative treatment plans.