ArticleEuropean journal of radiology open2025
MRI and CT radiomics for the diagnosis of acute pancreatitis.
Article in European journal of radiology open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Early non-contrast CT morphology at emergency admission in acute pancreatitis: real-world associations with clinical course.Emergency radiology · 2026Observational
- Deep learning-based detection of acute pancreatitis on abdominal contrast-enhanced CT.European radiology experimental · 2026Article
- Acute Pancreatitis and Splenic Infarction as Initial Symptoms of a Male Patient With SLE: A Rare Case Report.Clinical case reports · 2026Article
- Combining MRI dual-sequence radiomics and clinical parameters predicts post-hypertriglyceridemic acute pancreatitis diabetes mellitus.Frontiers in endocrinology · 2026Article
- Current Progress in the CT- and MRI-Based Detection and Evaluation of Acute Pancreatitis Complications.Medical science monitor : international medical journal of experimental and clinical research · 2025Review
- Incidence of pancreatic exocrine insufficiency and efficacy of pancreatic enzyme replacement therapy in acute pancreatitis patients.American journal of translational research · 2025Article
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Abstract
Purpose: To evaluate the single and combined diagnostic performances of CT and MRI radiomics for diagnosis of acute pancreatitis (AP). Materials and methods: We prospectively enrolled 78 patients (mean age 55.7 ± 17 years, 48.7 % male) diagnosed with AP between 2020 and 2022. Patients underwent contrast-enhanced CT (CECT) within 48-72 h of symptoms and MRI ≤ 24 h after CECT. The entire pancreas was manually segmented tridimensionally by two operators on portal venous phase (PVP) CECT images, T2-weighted imaging (WI) MR sequence and non-enhanced and PVP T1-WI MR sequences. A matched control group (n = 77) with normal pancreas was used. Dataset was randomly split into training and test, and various machine learning algorithms were compared. Receiver operating curve analysis was performed. Results: The T2WI model exhibited significantly better diagnostic performance than CECT and non-enhanced and venous T1WI, with sensitivity, specificity and AUC of 73.3 % (95 % CI: 71.5-74.7), 80.1 % (78.2-83.2), and 0.834 (0.819-0.844) for T2WI (p = 0.001), 74.4 % (71.5-76.4), 58.7 % (56.3-61.1), and 0.654 (0.630-0.677) for non-enhanced T1WI, 62.1 % (60.1-64.2), 78.7 % (77.1-81), and 0.787 (0.771-0.810) for venous T1WI, and 66.4 % (64.8-50.9), 48.4 % (46-50.9), and 0.610 (0.586-0.626) for CECT, respectively.The combination of T2WI with CECT enhanced diagnostic performance compared to T2WI, achieving sensitivity, specificity and AUC of 81.4 % (80-80.3), 78.1 % (75.9-80.2), and 0.911 (0.902-0.920) (p = 0.001). Conclusion: The MRI radiomics outperformed the CT radiomics model to detect diagnosis of AP and the combination of MRI with CECT showed better performance than single models. The translation of radiomics into clinical practice may improve detection of AP, particularly MRI radiomics.
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