ArticleFrontiers in medicine2022
Three-Dimensional Radiomics Features of Magnetic Resonance T2-Weighted Imaging Combined With Clinical Characteristics to Predict the Recurrence of Acute Pancreatitis.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 13 citations in OpenAlex.
- Interpretable machine learning model based on non-contrast computed tomography within 24 hours of admission for early prediction of the risk of severe deterioration of acute pancreatitis.Quantitative imaging in medicine and surgery · 2026Article
- Integrating CT-based radiomics and clinical features to better predict the prognosis of acute pancreatitis.Insights into imaging · 2025Article
- AI-powered innovations in pancreatitis imaging: a comprehensive literature synthesis.Abdominal radiology (New York) · 2025Review
- Radiomics-based prediction of recurrent acute pancreatitis in individuals with metabolic syndrome using T2WI magnetic resonance imaging data.Frontiers in medicine · 2025Article
- A Nomogram for Early Prediction of Infected Pancreatic Necrosis Based on Contrast-Enhanced CT Radiomics and Inflammatory Indicators.Journal of inflammation research · 2025Article
- Prediction of acute pancreatitis severity based on early CT radiomics.BMC medical imaging · 2024Article
- Radiomics-Based Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography.Diagnostics (Basel, Switzerland) · 2024Article
- Analysis of risk factors for acute pancreatitis complicated with pancreatic sinistral portal hypertension and construction of predictive model.Frontiers in physiology · 2023Article
- Radiomics and Its Applications and Progress in Pancreatitis: A Current State of the Art Review.Frontiers in medicine · 2022Review
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Authors and funding
7 authors at 1 institution in 1 country.
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Abstract
Objective: To explore the diagnostic value of radiomics model based on magnetic resonance T2-weighted imaging for predicting the recurrence of acute pancreatitis. Methods: We retrospectively collected 190 patients with acute pancreatitis (AP), including 122 patients with initial acute pancreatitis (IAP) and 68 patients with recurrent acute pancreatitis (RAP). At the same time, the clinical characteristics of the two groups were collected. They were randomly divided into training group and validation group in the ratio of 7:3. One hundred thirty-four cases in the training group, including 86 cases of IAP and 48 cases of RAP. There were 56 cases in the validation group, including 36 cases of IAP and 20 cases of RAP. Least absolute shrinkage and selection operator (LASSO) were used for feature screening. Logistic regression was used to establish the radiomics model, clinical model and combined model for predicting AP recurrence. The predictive ability of the three models was evaluated by the area under the curve (AUC). The recurrence risk in patients with AP was assessed using the nomogram. Results: The AUCs of radiomics model in training group and validation group were 0.804 and 0.788, respectively. The AUCs of the combined model in the training group and the validation group were 0.833 and 0.799, respectively. The AUCs of the clinical model in training group and validation group were 0.677 and 0.572, respectively. The sensitivities of the radiomics model, combined model, and clinical model were 0.646, 0.691, and 0.765, respectively. The specificities of the radiomics model, combined model, and clinical model were 0.791, 0.828, and 0.590, respectively. There was no significant difference in AUC between the radiomics model and the combined model for predicting RAP ( Conclusions: Radiomics features based on magnetic resonance T2WI could be used as biomarkers to predict the recurrence of AP, and radiomics model and combined model can provide new directions for predicting recurrence of acute pancreatitis.
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