Correlation analysis and recurrence evaluation system for patients with recurrent hepatolithiasis: a multicentre retrospective study Article Swipe
Zihan Li
,
Yibo Zhang
,
Zixiang Chen
,
Jiangming Chen
,
Hui Hou
,
Cheng Wang
,
Zheng Lu
,
Xiaoming Wang
,
Xiaoping Geng
,
Fubao Liu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fdgth.2024.1510674
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fdgth.2024.1510674
Background Methods for accurately predicting the prognosis of patients with recurrent hepatolithiasis (RH) after biliary surgery are lacking. This study aimed to develop a model that dynamically predicts the risk of hepatolithiasis recurrence using a machine-learning (ML) approach based on multiple clinical high-order correlation data. Materials and methods Data from patients with RH who underwent surgery at five centres between January 2015 and December 2020 were collected and divided into training and testing sets. Nine predictive models, which we named the Correlation Analysis and Recurrence Evaluation System (CARES), were developed and compared using machine learning (ML) methods to predict the patients’ dynamic recurrence risk within 5 post-operative years. We adopted a k-fold cross validation with k = 10 and tested model performance on a separate testing set. The area under the receiver operating characteristic curve was used to evaluate the performance of the models, and the significance and direction of each predictive variable were interpreted and justified based on Shapley Additive Explanations. Results Models based on ML methods outperformed those based on traditional regression analysis in predicting the recurrent risk of patients with RH, with Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) showing the best performance, both yielding an AUC (Area Under the receiver operating characteristic Curve) of∼0.9 or higher at predictions. These models were proved to have even better performance on testing sets than in a 10-fold cross validation, indicating that the model was not overfitted. The SHAP method revealed that immediate stone clearance, final stone clearance, number of previous surgeries, and preoperative CA19-9 index were the most important predictors of recurrence after reoperation in RH patients. An online version of the CARES model was implemented. Conclusion The CARES model was firstly developed based on ML methods and further encapsulated into an online version for predicting the recurrence of patients with RH after hepatectomy, which can guide clinical decision-making and personalised postoperative surveillance.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fdgth.2024.1510674
- OA Status
- gold
- Cited By
- 2
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404775150
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4404775150Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fdgth.2024.1510674Digital Object Identifier
- Title
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Correlation analysis and recurrence evaluation system for patients with recurrent hepatolithiasis: a multicentre retrospective studyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-27Full publication date if available
- Authors
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Zihan Li, Yibo Zhang, Zixiang Chen, Jiangming Chen, Hui Hou, Cheng Wang, Zheng Lu, Xiaoming Wang, Xiaoping Geng, Fubao LiuList of authors in order
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https://doi.org/10.3389/fdgth.2024.1510674Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3389/fdgth.2024.1510674Direct OA link when available
- Concepts
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Hepatolithiasis, Medicine, Retrospective cohort study, Correlation, Internal medicine, Surgery, Resection, Hepatectomy, Geometry, MathematicsTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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