Correlation analysis and recurrence evaluation system for patients with recurrent hepatolithiasis: a multicentre retrospective study

Zihan Li ^1,2, Yibo Zhang ³, Zixiang Chen ¹

⁰Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.

Frontiers in Digital Health +

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December 12, 2024

View abstract on PubMed

Summary

Machine learning models accurately predict recurrent hepatolithiasis (RH) risk after surgery. The Correlation Analysis and Recurrence Evaluation System (CARES) offers personalized surveillance guidance.

Area Of Science

Hepatobiliary surgery
Medical artificial intelligence
Clinical prognosis

Background

Accurate prognosis prediction for recurrent hepatolithiasis (RH) post-biliary surgery is limited.
Existing methods lack dynamic risk assessment capabilities.
Need for advanced predictive models incorporating complex clinical data.

Purpose Of The Study

Develop a machine learning (ML) model for dynamic risk prediction of RH recurrence.
Utilize high-order correlation data for improved accuracy.
Create a user-friendly tool for clinical decision support.

Main Methods

Collected data from RH patients across five centers (2015-2020).
Developed nine ML models, including Extreme Gradient Boosting and LightGBM, forming the CARES system.
Employed k-fold cross-validation and a separate testing set for robust evaluation.
Utilized Shapley Additive Explanations (SHAP) for variable importance interpretation.

Main Results

ML models significantly outperformed traditional regression for RH recurrence prediction.
XGBoost and LightGBM achieved an Area Under the ROC Curve (AUC) > 0.9.
Models demonstrated strong performance on testing sets, indicating no overfitting.
Key predictors identified: immediate/final stone clearance, prior surgeries, and preoperative CA19-9.

Conclusions

The CARES model, based on ML, effectively predicts RH recurrence post-hepatectomy.
An online version of CARES facilitates clinical decision-making and personalized surveillance.
The study provides a valuable tool for managing patients with recurrent hepatolithiasis.

Keywords:

high-order correlation data machine learning machine learning operations prediction model recurrent hepatolithiasis