ArticleCancer imaging : the official publication of the International Cancer Imaging Society2025
Differentiating pancreatic from periampullary non-pancreatic cancer: a nomogram-based prediction model utilizing CT imaging.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2025. 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
backgroundTo develop a predictive nomogram for differentiating pancreatic cancer from periampullary non-pancreatic cancers based on computed tomography (CT) imaging features.
methodsThis retrospective study included 171 patients diagnosed with periampullary carcinoma (90 pancreatic cancer and 81 non-pancreatic cancer). Variables assessed included CT imaging features along with relevant clinical data. Statistically significant variables were identified through multivariable logistic regression analysis, and a predictive nomogram was developed and internally validated based on these factors.
resultsMultivariable analysis identified the following independent risk factors: the distance from the distal end of the dilated pancreatic duct to the medial wall of the papilla (DPDP) (odds ratio [OR] 8.76, P < 0.05), the distance from the distal end of the dilated bile duct to the medial wall of the papilla (DBDP) (OR 31.83, P < 0.05), papillary enlargement (OR 0.03, P < 0.05), and visibility of pancreatic and/or bile ducts between the tumor and the papilla (VPBD) (OR 3.97, P < 0.05). A nomogram was constructed based on these four significant features. In both the development and validation cohorts, the nomogram demonstrated robust predictive performance, with areas under the receiver operating characteristic curve (AUCs) of 0.84 (95% CI, 0.77-0.91) and 0.81 (95% CI, 0.67-0.96), respectively.
conclusionsThis study underscores the value of CT imaging features in distinguishing pancreatic cancer from periampullary non-pancreatic cancers. The identification of key imaging markers with significant diagnostic value facilitated the development and validation of a nomogram that integrates these features, providing a more reliable tool for clinical decision-making.
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