ArticleFrontiers in oncology2026
Differentiation of intraductal papillary mucinous neoplasms and pancreatic ductal adenocarcinoma using arterial-phase CT radiomics combined with clinical features.
Article in Frontiers in oncology, 2026. 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
Objective: To investigate the value of arterial-phase CT-based radiomics combined with clinical features in differentiating IPMN (Intraductal Papillary Mucinous Neoplasms) from PDAC (Pancreatic Ductal Adenocarcinoma). Methods: A total of 216 patients with pathologically confirmed IPMN or PDAC were retrospectively enrolled. Clinical data and contrast-enhanced CT images were collected. Patients were divided into a training cohort, a test cohort and the external validation cohort. Univariate and multivariate analyses were performed on clinical variables and CT features to identify independent predictors. Regions of interest (ROIs) were manually delineated using ITK-SNAP software, and radiomics features were extracted with the Pyradiomics package. Feature dimensionality reduction and selection were conducted using the least absolute shrinkage and selection operator (LASSO) method. A radiomics score was calculated, and radiomics and combined models were constructed using a random forest (RF) algorithm. The diagnostic performance and clinical utility of each model were evaluated. Results: Multivariate analysis identified single cystic lesion (OR = 2.33), mural nodules (OR = 2.69), and CA19-9 level (OR = 2.36) as independent factors for differentiating IPMN from PDAC (all Conclusion: A predictive model based on arterial-phase CT radiomics combined with clinical features can effectively differentiate IPMN from PDAC.
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