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A Bayesian network prediction model for gallbladder polyps with malignant potential based on preoperative ultrasound

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Abstract

Background

It is important to identify gallbladder polyps (GPs) with malignant potential and avoid unnecessary cholecystectomy by constructing prediction model. The aim of the study is to develop a Bayesian network (BN) prediction model for GPs with malignant potential in a long diameter of 8–15 mm based on preoperative ultrasound.

Methods

The independent risk factors for GPs with malignant potential were screened by χ2 test and Logistic regression model. Prediction model was established and validated using data from 1296 patients with GPs who underwent cholecystectomy from January 2015 to December 2019 at 11 tertiary hospitals in China. A BN model was established based on the independent risk variables.

Results

Independent risk factors for GPs with malignant potential included age, number of polyps, polyp size (long diameter), polyp size (short diameter), and fundus. The BN prediction model identified relationships between polyp size (long diameter) and three other variables [polyp size (short diameter), fundus and number of polyps]. Each variable was assigned scores under different status and the probabilities of GPs with malignant potential were classified as [0–0.2), [0.2–0.5), [0.5–0.8) and [0.8–1] according to the total points of [− 337, − 234], [− 197, − 145], [− 123, − 108], and [− 62,500], respectively. The AUC was 77.38% and 75.13%, and the model accuracy was 75.58% and 80.47% for the BN model in the training set and testing set, respectively.

Conclusion

A BN prediction model was accurate and practical for predicting GPs with malignant potential patients in a long diameter of 8–15 mm undergoing cholecystectomy based on preoperative ultrasound.

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Funding

Supported by the National Natural Science Foundation of China, No. 62076194; the Key Research and Development Program of Shaanxi Province, No. 2017ZDXM-SF-055 and No.2017KW-060; Clinical Research Fund of the First Affiliated Hospital of Xi'an Jiaotong University, No. XJTU1AF-CRF-2018–022.

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Authors and Affiliations

  1. Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, Shaanxi, China

    Qi Li, Zhimin Geng & Dong Zhang

  2. Department of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, China

    Jingwei Zhang, Zhiqiang Cai & Shubin Si

  3. Department of Hepatobiliary Surgery, The First People’s Hospital of Xianyang City, Xianyang, 712000, Shaanxi, China

    Pengbo Jia & Xintuan Wang

  4. Department of Hepatobiliary Surgery, Shaanxi Provincial People’s Hospital, Xi’an, 710068, Shaanxi, China

    Xilin Geng & Yu Zhang

  5. Department of Hepatobiliary Surgery, Central Hospital of Baoji City, Baoji, 721000, Shaanxi, China

    Da Lei

  6. Department of General Surgery, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710004, Shaanxi, China

    Junhui Li & Wenbin Yang

  7. Department of Hepatobiliary Surgery, Central Hospital of Hanzhong City, Hanzhong, 723000, Shaanxi, China

    Rui Yang

  8. Department of General Surgery, No. 215 Hospital of Shaanxi Nuclear Industry, Xianyang, 712000, Shaanxi, China

    Xiaodi Zhang

  9. Department of General Surgery, Central Hospital of Ankang City, Ankang, 725000, Shaanxi, China

    Chenglin Yang

  10. Department of General Surgery, Xianyang Hospital of Yan’an University, Xianyang, 712000, Shaanxi, China

    Chunhe Yao

  11. Department of Hepatobiliary Surgery, The Second Hospital of Yulin City, Yulin, 719000, Shaanxi, China

    Qiwei Hao

  12. Department of Hepatobiliary Surgery, People’s Hospital of Baoji City, Baoji, 721000, Shaanxi, China

    Yimin Liu & Zhihua Guo

Authors
  1. Qi Li
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  2. Jingwei Zhang
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  3. Zhiqiang Cai
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  4. Pengbo Jia
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  7. Yu Zhang
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  9. Junhui Li
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  13. Chenglin Yang
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  14. Chunhe Yao
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  16. Yimin Liu
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  17. Zhihua Guo
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  18. Shubin Si
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  19. Zhimin Geng
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  20. Dong Zhang
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Corresponding authors

Correspondence to Zhimin Geng or Dong Zhang.

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Disclosures

Qi Li, Jingwei Zhang, Zhiqiang Cai, Pengbo Jia, Xintuan Wang, Xilin Geng, Yu Zhang, Da Lei, Junhui Li, Wenbin Yang, Rui Yang, Xiaodi Zhang, Chenglin Yang, Chunhe Yao, Qiwei Hao, Yimin Liu, Zhihua Guo, Shubin Si, Zhimin Geng and Dong Zhang have no conflicts of interest or financial ties to disclose.

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Li, Q., Zhang, J., Cai, Z. et al. A Bayesian network prediction model for gallbladder polyps with malignant potential based on preoperative ultrasound. Surg Endosc 37, 518–527 (2023). https://doi.org/10.1007/s00464-022-09532-z

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  • DOI: https://doi.org/10.1007/s00464-022-09532-z

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