ArticleQuantitative imaging in medicine and surgery2023
Early prediction of acute pancreatitis severity based on changes in pancreatic and peripancreatic computed tomography radiomics nomogram.
Article in Quantitative imaging in medicine and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- A systematic review of machine learning-based prognostic models for acute pancreatitis: Towards improving methods and reporting quality.PLoS medicine · 2025Pooled it
- Development and validation of a preoperative CT-based radiomics nomogram for predicting postpancreatectomy acute pancreatitis after pancreatoduodenectomy.Abdominal radiology (New York) · 2026Article
- 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
- Beyond gas bubbles: AI analysis of the "bubble bed" microenvironment improves diagnosis of infected abdominal collections.Abdominal radiology (New York) · 2026Article
- Explainable Machine Learning for Early Prediction of Surgical Necessity in Gastrointestinal Emergencies: A Multimodal Diagnostic Study.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning-based prediction of acute pancreatitis severity from abdominal CT with multicenter external validation.Radiology advances · 2026Article
- Management and Prediction of Acute Pancreatitis Severity Using AI: A Surgical Perspective.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence in pancreatitis: A narrative review on advancing precision diagnosis, prognosis, and therapeutic strategies.World journal of gastroenterology · 2025Review
- MRI and CT radiomics for the diagnosis of acute pancreatitis.European journal of radiology open · 2025Article
- Deep learning radiomics nomogram for preoperatively identifying moderate-to-severe chronic cholangitis in children with pancreaticobiliary maljunction: a multicenter study.BMC medical imaging · 2025Article
- Artificial intelligence models assisting physicians in quantifying pancreatic necrosis in acute pancreatitis.Quantitative imaging in medicine and surgery · 2025Article
- Utilizing MRI Radiomics and Clinical Features to Predict Severe Acute Pancreatitis in Patients with Metabolic Syndrome.Journal of inflammation research · 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
- Managing complicated pancreatitis with more knowledge and a bigger toolbox!Trauma surgery & acute care open · 2025Review
- 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
- Early prediction of acute pancreatitis with acute kidney injury using abdominal contrast-enhanced CT features.iScience · 2024Article
- Radiomics and 256-slice-dual-energy CT in the automated diagnosis of mild acute pancreatitis: the innovation of formal methods and high-resolution CT.La Radiologia medica · 2024Article
- Radiomics models of contrast-enhanced computed tomography for predicting the activity and prognosis of acute pancreatitis.Insights into imaging · 2024Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Early identification of severe acute pancreatitis (SAP) is key to reducing mortality and improving prognosis. We aimed to establish a radiomics model and nomogram for early prediction of acute pancreatitis (AP) severity based on contrast-enhanced computed tomography (CT) images. Methods: We retrospectively analyzed 215 patients with first-episode AP, including 141 in the training cohort (87 men and 54 women, mean age 51.37±16.09 years) and 74 in the test cohort (40 men and 34 women, mean age 55.49±17.83 years). Radiomics features were extracted from portal venous phase images based on pancreatic and peripancreatic regions. The light gradient boosting machine (LightGBM) algorithm was used for feature selection, a logistic regression (LR) model was established and trained by 10-fold cross-validation, and a nomogram was established based on the best features. The model's predictive performance was evaluated according to the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy. Results: A total of 13 optimal radiomics features were selected by LightGBM for LR model building. The AUC of the radiomics (LR) model was 0.992 [95% confidence interval (CI): 0.963-0.996] in the training cohort, 0.965 (95% CI: 0.924-0.981) in the validation cohort, and 0.894 (95% CI: 0.789-0.966) in the test cohort. The sensitivity was 0.862 (95% CI: 0.674-0.954), the specificity was 0.800 (95% CI: 0.649-0.899), and the accuracy was 0.824 (95% CI: 0.720-0.919). The nomogram based on the 13 radiomics features showed that SAP would be predicted when the total score was greater than 124. Conclusions: The radiomics model based on enhanced-CT images of pancreatic and peripancreatic regions performed well in the early prediction of AP severity. The nomogram based on selected radiomics features could provide a reference for AP clinical assessment.
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