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

  • Yong Chen
  • Tian-wu Chen
  • Chang-qiang Wu
  • Qiao Lin
  • Ran Hu
  • Chao-lian Xie
  • Hou-dong Zuo
  • Jia-long Wu
  • Qi-wen Mu
  • Quan-shui Fu
  • Guo-qing Yang
  • Xiao Ming ZhangEmail author



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


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.


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).


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.


Radiomics Tomography, X-ray computed Acute pancreatitis Recurrence 



Acute pancreatitis


Area under the receiver operating characteristic curve


Contrast-enhanced computed tomography


Computed tomography severity index


Gray-level co-occurrence matrix


Gray-level run length matrix


Intraclass correlation coefficient


Least absolute shrinkage and selection operator


Negative predictive value


Picture archiving and communication system


Positive predictive value


Revised Atlanta Criteria


Recurrent acute pancreatitis


Receiver operating characteristic curve


Region of interest


Support vector machine



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


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).

Compliance with ethical standards


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.


• retrospective

• diagnostic or prognostic study

• multicenter study

Supplementary material

330_2018_5824_MOESM1_ESM.docx (1.3 mb)
ESM 1 (DOCX 1348 kb)


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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Yong Chen
    • 1
  • Tian-wu Chen
    • 1
  • Chang-qiang Wu
    • 2
  • Qiao Lin
    • 1
  • Ran Hu
    • 1
  • Chao-lian Xie
    • 1
  • Hou-dong Zuo
    • 1
  • Jia-long Wu
    • 3
  • Qi-wen Mu
    • 4
  • Quan-shui Fu
    • 5
  • Guo-qing Yang
    • 5
  • Xiao Ming Zhang
    • 1
    Email author
  1. 1.Sichuan Key Laboratory of Medical Imaging and Department of RadiologyAffiliated Hospital of North Sichuan Medical CollegeNanchongChina
  2. 2.Sichuan Key Laboratory of Medical Imaging and School of Medical ImagingNorth Sichuan Medical CollegeNanchongChina
  3. 3.Department of RadiologyThe Second Clinical Medical College of North Sichuan Medical College Nanchong Central HospitalNanchongChina
  4. 4.Department of Medical Imaging & Imaging Institute of Rehabilitation and Development of Brain FunctionThe Second Clinical Medical College of North Sichuan Medical College Nanchong Central HospitalNanchongChina
  5. 5.Department of RadiologySuining Central HospitalSuiningChina

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