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A Bayesian Network Prediction Model for Microvascular Invasion in Patients with Intrahepatic Cholangiocarcinoma: A Multi-institutional Study

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Abstract

Background

Microvascular invasion (MVI) has been reported to be an independent prognostic factor of recurrence and poor overall survival in patients with intrahepatic cholangiocarcinoma (ICC). This study aimed to explore the preoperative independent risk factors of MVI and establish a Bayesian network (BN) prediction model to provide a reference for surgical diagnosis and treatment.

Methods

A total of 531 patients with ICC who underwent radical resection between 2010 and 2018 were used to establish and validate a BN model for MVI. The BN model was established based on the preoperative independent variables. The ROC curves and confusion matrix were used to assess the performance of the model.

Results

MVI was an independent risk factor for relapse-free survival (RFS) (P < 0.05). MVI has a correlation with postoperative recurrence, early recurrence (< 6 months), median RFS and median overall survival (all P < 0.05). The preoperative independent risk variables of MVI included obstructive jaundice, prognostic nutritional index, CA19-9, tumor size, and major vascular invasion, which were used to establish the BN model. The AUC of the BN model was 78.92% and 83.01%, and the accuracy was 70.85% and 77.06% in the training set and testing set, respectively.

Conclusion

The BN model established based on five independent risk variables for MVI is an effective and practical model for predicting MVI in patients with ICC.

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Funding

This study was supported by the National Natural Science Foundation of China (No. 62076194, No. 81772521); Multicenter Clinical Research Project of Shanghai Jiaotong University, School of Medicine (DLY201807); Clinical Training Program of Shanghai Xinhua Hospital Affiliated to Shanghai Jiaotong University, School of Medicine (17CSK06).

Author information

Author notes

  1. Qi Li and Jingwei Zhang are contributed equally to this work.

Authors and Affiliations

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

    Qi Li, Chen Chen, Dong Zhang & Zhimin Geng

  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 and Pancreatic Surgery, West China Hospital of Sichuan University, Chengdu, 610041, China

    Hong Wu

  4. Department of Biliary Surgery, Oriental Hepatobiliary Hospital Affiliated to Naval Medical University, Shanghai, 200433, China

    Yinghe Qiu

  5. Department of Hepatobiliary Oncology, Tianjin Medical University Cancer Hospital, Tianjin, 300060, China

    Tianqiang Song

  6. Department of Hepatobiliary Surgery, Hunan Provincial People’s Hospital, Changsha, 410005, China

    Xianhai Mao

  7. Department of Hepatobiliary Surgery, The First Hospital Affiliated to Army Medical University, Chongqing, 400038, China

    Yu He

  8. Department of Hepatobiliary Surgery, Zhongda Hospital of Southeast University, Nanjing, 210009, China

    Zhangjun Cheng

  9. Hepatobiliary Pancreas and Liver Transplantation Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China

    Wenlong Zhai

  10. Department of Hepatobiliary Surgery, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China

    Jingdong Li

  11. Department of General Surgery, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, 200092, China

    Zhaohui Tang

Authors
  1. Qi Li
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  14. Dong Zhang
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Contributions

ZG and ZT conceived and designed the experiments. Q L and JZ performed the experiments. HW, YQ, TS, XM, YH, ZC, WZ, and JL collected and offered the data. QL and JZ contributed analysis tools. QL, JZ, ZC, CC, SS and DZ conducted statistical analysis. QL and JZ wrote the paper. ZG and ZT reviewed the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Zhimin Geng or Zhaohui Tang.

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Li, Q., Zhang, J., Cai, Z. et al. A Bayesian Network Prediction Model for Microvascular Invasion in Patients with Intrahepatic Cholangiocarcinoma: A Multi-institutional Study. World J Surg 47, 773–784 (2023). https://doi.org/10.1007/s00268-022-06867-5

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