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Purpose <p>To evaluate the diagnostic value of multislice spiral computed tomography (MSCT) in differentiating pancreatic acinar cell carcinoma (PACC) from pancreatic ductal adenocarcinoma (PDAC).</p> Methods <p>The 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 <i>t</i>-test or the Mann-Whitney <i>U</i> 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.</p> Results <p>Univariate 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%.</p> Conclusion <p>Tumor 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.</p>

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Diagnostic performance of multislice spiral computed tomography in differentiating pancreatic acinar cell carcinoma from pancreatic ductal adenocarcinoma

  • 期刊论文

  • 发表日期：

    2026年6月24日

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  • Jie Yu,
  • Yingying Cao,
  • Fan Xia,
  • Qi Lan,
  • Jianyun Zhou,
  • Zhongqiu Wang,
  • Shuai Ren

摘要

Purpose

To evaluate the diagnostic value of multislice spiral computed tomography (MSCT) in differentiating pancreatic acinar cell carcinoma (PACC) from pancreatic ductal adenocarcinoma (PDAC).

Methods

The 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.

Results

Univariate 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%.

Conclusion

Tumor 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.