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An imaging-based machine learning model outperforms clinical risk scores for prognosis of cirrhotic variceal bleeding

  • Gastrointestinal
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To develop and validate a machine learning model based on contrast-enhanced CT to predict the risk of occurrence of the composite clinical endpoint (hospital-based intervention or death) in cirrhotic patients with acute variceal bleeding (AVB).

Methods

This retrospective study enrolled 330 cirrhotic patients with AVB between January 2017 and December 2020 from three clinical centers. Contrast-enhanced CT and clinical data were collected. Centers A and B were divided 7:3 into a training set and an internal test set, and center C served as a separate external test set. A well-trained deep learning model was applied to segment the liver and spleen. Then, we extracted 106 original features of the liver and spleen separately based on the Image Biomarker Standardization Initiative (IBSI). We constructed the Liver-Spleen (LS) model based on the selected radiomics features. The performance of LS model was evaluated by receiver operating characteristics and calibration curves. The clinical utility of models was analyzed using decision curve analyses (DCA).

Results

The LS model demonstrated the best diagnostic performance in predicting the composite clinical endpoint of AVB in patients with cirrhosis, with an AUC of 0.782 (95% CI 0.650–0.882) and 0.789 (95% CI 0.674–0.878) in the internal test and external test groups, respectively. Calibration curves and DCA indicated the LS model had better performance than traditional clinical scores.

Conclusion

A novel machine learning model outperforms previously known clinical risk scores in assessing the prognosis of cirrhotic patients with AVB

Clinical relevance statement

The Liver-Spleen model based on contrast-enhanced CT has proven to be a promising tool to predict the prognosis of cirrhotic patients with acute variceal bleeding, which can facilitate decision-making and personalized therapy in clinical practice.

Key Points

• The Liver-Spleen machine learning model (LS model) showed good performance in assessing the clinical composite endpoint of cirrhotic patients with AVB (AUC ≥ 0.782, sensitivity ≥ 80%).

• The LS model outperformed the clinical scores (AUC ≤ 0.730, sensitivity ≤ 70%) in both internal and external test cohorts.

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Abbreviations

ALBI:

Albumin–bilirubin

ARS:

Admission Rockall score

AUC:

Area under the curve

AVB:

Acute variceal bleeding

CTP:

Child-Turcotte-Pugh score

DCA:

Decision curve analysis

GBS:

Glasgow-Blatchford score

IBSI:

Image Biomarker Standardization Initiative

LS:

Liver-Spleen

MELD:

Model for End-Stage Liver Disease

ROC:

Receiver operating characteristics

UGIB:

Upper gastrointestinal bleeding

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Funding

This study has received funding by the National Key R&D Program of China (2021YFF0501504), the National Natural Science Foundation of China (NSFC, Nos. 81830053, 92059202, and 61821002), and the Key Research and Development Program of Jiangsu Province (BE2020717).

Author information

Authors and Affiliations

  1. Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nan**g, 210009, Jiangsu, China

    Yin Gao, Qian Yu, Cong **a, Jiaying Zhou, Tianyi **a, Ben Zhao, Yue Qiu, Jun-hao Zha, Yuancheng Wang, Tianyu Tang & Shenghong Ju

  2. The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China

    **aohuan Li

  3. Department of Medical Imaging, Subei People’s Hospital, Medical School of Yangzhou University, Yangzhou, China

    Yan Lv & **g Ye

  4. Department of Radiology, The Second Hospital of Nan**g, Nan**g University of Chinese Medicine, Nan**g, China

    Chuanjun Xu

Authors
  1. Yin Gao
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  2. Qian Yu
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  3. **aohuan Li
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  4. Cong **a
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  15. Shenghong Ju
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Corresponding author

Correspondence to Shenghong Ju.

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Guarantor

The scientific guarantor of this publication is Prof. Shenghong Ju.

Conflict of interest

The authors declare no competing interests.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

  • retrospective

  • diagnostic or prognostic study

  • multicenter study

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Yin Gao and Qian Yu are primary authors and contributed equally.

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Gao, Y., Yu, Q., Li, X. et al. An imaging-based machine learning model outperforms clinical risk scores for prognosis of cirrhotic variceal bleeding. Eur Radiol 33, 8965–8973 (2023). https://doi.org/10.1007/s00330-023-09938-w

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  • DOI: https://doi.org/10.1007/s00330-023-09938-w

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