ArticleAbdominal radiology (New York)2026
Diagnostic performance of multislice spiral computed tomography in differentiating pancreatic acinar cell carcinoma from pancreatic ductal adenocarcinoma.
Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Metabolic Response to an Individualized Multimodal Treatment Strategy in Advanced Pancreatic Adenocarcinoma: A Case Report.Current oncology (Toronto, Ont.) · 2026Article
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7 authors.
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Abstract
purposeTo evaluate the diagnostic value of multislice spiral computed tomography (MSCT) in differentiating pancreatic acinar cell carcinoma (PACC) from pancreatic ductal adenocarcinoma (PDAC).
methodsThe clinical, pathological, and imaging data of 17 patients with pathologically confirmed PACC and 62 patients with PDAC were retrospectively analyzed. Quantitative variables were compared between groups using the independent samples t-test or the Mann-Whitney U test, as appropriate. Qualitative variables were compared using the Pearson's chi-square test or Fisher's exact test. Variables showing statistical significance in univariate analysis were entered into multivariate logistic regression analysis to identify independent predictors for distinguishing PACC from PDAC. Diagnostic performance was assessed using receiver operating characteristic curve analysis, with calculation of the area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, and accuracy.
resultsUnivariate analysis demonstrated significant differences between the two groups in tumor shape, margin, pancreatic atrophy, pancreatic duct transection, maximum tumor diameter, CT attenuation values, and enhancement ratios in the pancreatic parenchymal, portal venous, and delayed phases, all of which showed statistically significant differences. Multivariate logistic regression analysis identified tumor margin, pancreatic duct transection, pancreatic parenchymal phase CT attenuation value as independent predictors for distinguishing PACC from PDAC. The combined diagnostic model incorporating these variables achieved the highest diagnostic performance, with an AUC of 0.968. The model demonstrated a sensitivity of 94.1%, specificity of 88.7%, accuracy of 89.9%, positive predictive value of 69.5%, and negative predictive value of 98.2%.
conclusionTumor margin, pancreatic duct transection, and pancreatic parenchymal phase CT attenuation value are significant imaging features for differentiating PACC from PDAC. A combined diagnostic model integrating these imaging features provides excellent diagnostic performance and may aid in improving preoperative differential diagnosis.
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