Radiomics model of contrast-enhanced computed tomography for predicting the recurrence of acute pancreatitis
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.
• 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.
KeywordsRadiomics Tomography, X-ray computed Acute pancreatitis Recurrence
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.
Written informed consent was waived by the Institutional Review Board.
Institutional Review Board approval was obtained.
• diagnostic or prognostic study
• multicenter study
- 4.Ahmed Ali U, Issa Y, Hagenaars JC et al (2016) Risk of recurrent pancreatitis and progression to chronic pancreatitis after a first episode of acute pancreatitis. Clin Gastroenterol Hepatol 14:738–746Google Scholar
- 16.Hinkle DE, Wiersma W, Jurs SG (2003) Applied statistics for the behavioral sciences, 5th edn. Houghton Mifflin, BostonGoogle Scholar