ArticleAbdominal radiology (New York)2025
Preoperative prediction of pancreatic neuroendocrine tumors grade based on computed tomography, magnetic resonance imaging and endoscopic ultrasonography.
Article in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of artificial intelligence versus conventional imaging for differentiating G2/G3 from G1 pancreatic neuroendocrine tumors: a systematic review and meta-analysis.BMC medical imaging · 2026Pooled it
- Pancreatic neuroendocrine microtumors in the elderly: A retrospective study using cadaveric pancreatic tissue.World journal of gastrointestinal oncology · 2025Article
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8 authors.
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Abstract
purposeTo establish a preoperative prediction model for pathological grade of PanNETs based on computed tomography (CT), magnetic resonance imaging (MRI) and endoscopic ultrasonography (EUS).
methodsClinical data of 58 patients with pathologically confirmed PanNETs were included in this retrospectively study and they were divided into grade 1 and grade 2/3. CT, MRI and EUS images were collected within one week before surgery. A clinical predictive model based on the independent clinical risk factors and significant radiological features was established. The area under receiver operating characteristic curve (AUC) was performed to assess the model.
resultsGender, pancreatic duct dilatation (PDD) and portal enhancement ratio (PER) were the independent predictors for PanNETs grading (P < 0.05). PanNETs grade 1 and grade 2/3 had statistical difference in elastography score (P = 0.001). The combination of gender, PDD and PER had better predictive efficiency than each of these three predictors alone, with a high AUC of 0.925. The elastography score also achieved an AUC of 0.838.
conclusionWe proposed a comprehensive model based on preoperative CT, MRI and EUS to predict grade 1 and grade 2/3 of PanNETs and better informs clinicians on individualized diagnosis and treatment of patients with PanNETs.
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