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Radiomics model of contrast-enhanced computed tomography for predicting the recurrence of acute pancreatitis

European Radiology volume 29, pages 4408–4417 (2019)Cite this article

Abstract

Objectives

To predict the recurrence of acute pancreatitis (AP) by constructing a radiomics model of contrast-enhanced computed tomography (CECT) at AP first attack.

Methods

We retrospectively enrolled 389 first-attack AP patients (271 in the primary cohort and 118 in the validation cohort) from three tertiary referral centers; 126 and 55 patients endured recurrent attacks in each cohort. Four hundred twelve radiomics features were extracted from arterial and venous phase CECT images, and clinical characteristics were gathered to develop a clinical model. An optimal radiomics signature was chosen using a multivariable logistic regression or support vector machine. The radiomics model was developed and validated by incorporating the optimal radiomics signature and clinical characteristics. The performance of the radiomics model was assessed based on its calibration and classification metrics.

Results

The optimal radiomics signature was developed based on a multivariable logistic regression with 10 radiomics features. The classification accuracy of the radiomics model well predicted the recurrence of AP for both the primary and validation cohorts (87.1% and 89.0%, respectively). The area under the receiver operating characteristic curve (AUC) of the radiomics model was significantly better than that of the clinical model for both the primary (0.941 vs. 0.712, p = 0.000) and validation (0.929 vs. 0.671, p = 0.000) cohorts. Good calibration was observed for all the models (p > 0.05).

Conclusions

The radiomics model based on CECT performed well in predicting AP recurrence. As a quantitative method, radiomics exhibits promising performance in terms of alerting recurrent patients to potential precautions.

Key Points

• The incidence of recurrence after an initial episode of acute pancreatitis is high, and quantitative methods for predicting recurrence are lacking.

• The radiomics model based on contrast-enhanced computed tomography performed well in predicting the recurrence of acute pancreatitis.

• As a quantitative method, radiomics exhibits promising performance in terms of alerting recurrent patients to the potential need to take precautions.

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Abbreviations

AP:

Acute pancreatitis

AUC:

Area under the receiver operating characteristic curve

CECT:

Contrast-enhanced computed tomography

CTSI:

Computed tomography severity index

GLCM:

Gray-level co-occurrence matrix

GLRLM:

Gray-level run length matrix

ICC:

Intraclass correlation coefficient

LASSO:

Least absolute shrinkage and selection operator

NPV:

Negative predictive value

PACS:

Picture archiving and communication system

PPV:

Positive predictive value

RAC:

Revised Atlanta Criteria

RAP:

Recurrent acute pancreatitis

ROC:

Receiver operating characteristic curve

ROI:

Region of interest

SVM:

Support vector machine

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Acknowledgements

Thanks are due to Dr. Xin Li for the assistance with statistics and data visualization.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 81871440) and the Training Program for Science and Technology Innovation of Sichuan Province (Grant No. 2018036).

Author information

Author notes

  1. Yong Chen and Tian-wu Chen contributed equally to this work.

Affiliations

  1. Sichuan Key Laboratory of Medical Imaging and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, No. 63, Wenhua Road, Nanchong, 637000, Sichuan, China

    Yong Chen, Tian-wu Chen, Qiao Lin, Ran Hu, Chao-lian Xie, Hou-dong Zuo & Xiao Ming Zhang

  2. Sichuan Key Laboratory of Medical Imaging and School of Medical Imaging, North Sichuan Medical College, Nanchong, Sichuan, China

    Chang-qiang Wu

  3. Department of Radiology, The Second Clinical Medical College of North Sichuan Medical College Nanchong Central Hospital, Nanchong, Sichuan, China

    Jia-long Wu

  4. Department of Medical Imaging & Imaging Institute of Rehabilitation and Development of Brain Function, The Second Clinical Medical College of North Sichuan Medical College Nanchong Central Hospital, Nanchong, Sichuan, China

    Qi-wen Mu

  5. Department of Radiology, Suining Central Hospital, Suining, Sichuan, China

    Quan-shui Fu & Guo-qing Yang

Authors
  1. Yong Chen
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  2. Tian-wu Chen
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  5. Ran Hu
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  8. Jia-long Wu
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  12. Xiao Ming Zhang
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Corresponding author

Correspondence to Xiao Ming Zhang.

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Guarantor

The scientific guarantor of this publication is Xiao Ming Zhang, MD.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

Dr. Xin Li kindly provided statistical advice for this manuscript.

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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Chen, Y., Chen, Tw., Wu, Cq. et al. Radiomics model of contrast-enhanced computed tomography for predicting the recurrence of acute pancreatitis. Eur Radiol 29, 4408–4417 (2019). https://doi.org/10.1007/s00330-018-5824-1

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  • DOI: https://doi.org/10.1007/s00330-018-5824-1

Keywords

  • Radiomics
  • Tomography, X-ray computed
  • Acute pancreatitis
  • Recurrence