Development and internal validation of a clinical–CT model for differentiating intestinal-type from pancreatobiliary-type periampullary carcinoma

BACKGROUND: Intestinal-type and pancreatobiliary-type periampullary carcinomas exhibit distinct biological behaviours and prognostic outcomes, yet accurate preoperative subtyping remains a major clinical challenge. This study aimed to evaluate the value of clinical variables and computed tomography (CT) imaging features in the differential diagnosis of intestinal-type and pancreatobiliary-type periampullary carcinoma, and to develop a subtype prediction model. METHODS: This retrospective study included 83 patients with pathologically confirmed periampullary carcinoma, comprising 21 intestinal-type and 62 pancreatobiliary-type periampullary carcinomas. Clinical variables and conventional CT imaging features were evaluated using univariable and multivariable logistic regression analyses to identify predictors associated with PAC subtype. Based on these predictors, clinical, radiologic, and combined prediction models were developed, and a nomogram was constructed from the combined model. Model performance was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC). The DeLong test was employed to compare the diagnostic performance among different models. RESULTS: The AUCs of the clinical model and radiologic model were 0.82 (95%CI, 0.70-0.92) and 0.85 (95%CI, 0.79-0.93), respectively. The combined model showed significantly better diagnostic performance than either model alone, with an AUC of 0.92 (95%CI, 0.84-0.97), a sensitivity of 87.1%, and a specificity of 90.5%. Serum carbohydrate antigen 19 - 9, total bilirubin, tumor location, and enhancement degree of lesion were identified as the most important predictors in the combined model. In addition, the nomogram derived from the combined model demonstrated good discriminative ability for predicting histologic subtype. CONCLUSION: The combined model integrating clinical and CT imaging features enables more accurate preoperative differentiation between intestinal-type and pancreatobiliary-type periampullary carcinoma and yields higher sensitivity and specificity than models based on clinical or imaging features alone.