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A nomogram based on multiparametric magnetic resonance imaging radiomics for prediction of acute pancreatitis
Ting-Ting Liu1, You-Qiang Hu2, Ning-Jun Yu3
1Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, No.1 South Maoyuan Road, Nanchong, Sichuan, 637001, China.
Purpose:
In acute pancreatitis (AP), disease activity is defined as the reversible manifestation of the disease. The aim of this study was to develop a nomogram for predicting disease activity in AP based on multiparametric magnetic resonance imaging (MRI) radiomics.
Methods:
This retrospective study included 310 patients with first-episode AP from two medical centers in China. Patients from the first medical center were randomly divided into a training cohort (n = 122) and an internal validation cohort (n = 123) in a 5:5 ratio. Patients from the second medical center were used as the external independent validation cohort (n = 65). Radiomics features were extracted from multiparametric MRI images based on pancreatic parenchymal regions. The least absolute shrinkage and selection operator (LASSO) was used for feature screening, logistic regression was used to establish radiomic feature, and statistically significant laboratory parameters were incorporated to construct the nomogram. The area under the receiver operator characteristic curve assessed the predictive performance of the nomogram. Furthermore, decision curve analysis (DCA) was used to assess the clinical utility of the nomogram, and the disease activity was validated against follow-up clinical outcomes (e.g., organ failure progression, ICU admission) and imaging-confirmed changes within one-week after MRI.
Results:
The AUCs of the radiomic signature were 0.808 (training cohort), 0.789 (internal validation cohort), and 0.783 (external validation cohort). Radiomic signature, extrapancreatic inflammation on MRI (EPIM) scores, and WBC count were identified as independent risk factors for the activity of AP and were therefore included in the nomogram. The AUC of the nomogram were 0.881 (training cohort), 0.922 (internal validation cohort) and 0.912 (external validation cohort). Additionally, the nomogram model obtained the greatest net benefit, according to the results of decision curves Based on the follow-up results, we also found that AP patients with higher disease activity were more likely to experience exacerbations.
Conclusions:
This nomogram can accurately predict the activity of AP patients, thus providing objective monitoring of the patient's course and potentially improving patient prognosis.
Insights
This study developed a nomogram using multiparametric MRI radiomics to accurately predict acute pancreatitis (AP) disease activity. The tool aids in objective monitoring and potentially improves patient outcomes.
Area of Science:
- Radiology
- Medical Imaging
- Oncology
Background:
- Acute pancreatitis (AP) is a serious condition where disease activity signifies reversible manifestations.
- Accurate assessment of AP disease activity is crucial for timely intervention and improved patient prognosis.
Purpose of the Study:
- To develop and validate a nomogram for predicting disease activity in acute pancreatitis (AP) using multiparametric magnetic resonance imaging (MRI) radiomics.
- To assess the predictive performance and clinical utility of the developed nomogram.
Main Methods:
- A retrospective study of 310 first-episode AP patients from two medical centers.
- Radiomics features were extracted from multiparametric MRI, and LASSO regression was used for feature selection.
- A nomogram was constructed incorporating radiomic features and laboratory parameters, validated internally and externally.
Main Results:
- The radiomic signature showed good predictive performance (AUCs ranging from 0.783 to 0.808).
- The nomogram, including radiomic signature, EPIM scores, and WBC count, demonstrated high predictive accuracy (AUCs ranging from 0.881 to 0.922).
- The nomogram achieved the greatest net benefit in decision curve analysis and correlated with exacerbations.
Conclusions:
- The developed nomogram accurately predicts AP disease activity.
- This tool offers objective monitoring of AP patient course.
- The nomogram has the potential to improve patient prognosis in acute pancreatitis.
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