ArticleBMC medical imaging2025
A nomogram based on multiparametric magnetic resonance imaging radiomics for prediction of acute pancreatitis activity.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeIn 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.
methodsThis 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.
resultsThe 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.
conclusionsThis nomogram can accurately predict the activity of AP patients, thus providing objective monitoring of the patient's course and potentially improving patient prognosis.
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