Objective To develop an abdominal computed tomography (CT)-based adjunctive diagnostic tool for small pancreatic cancer (maximal tumor diameter ≤ 2 cm). Methods The imaging data of 206 patients with small pancreatic cancer confirmed by pathology and 268 normal controls without known pancreatic diseases who were admitted to The First Affiliated Hospital of Naval Medical University (Second Military Medical University) were retrospectively analyzed. The patients were assigned to training set and validation set in chronological order:143 patients with small pancreatic cancer and 188 normal controls admitted from Jan. 2014 to Dec. 2019 were assigned to the training set; and 63 patients with small pancreatic cancer and 80 normal controls admitted from Jan. 2020 to Dec. 2021 were assigned to the validation set. The whole pancreas was automatically delineated on the abdominal CT images by 2 imaging physicians using the nnU-Net automatic segmentation model to extract radiomics features. Variance analysis, Spearman correlation analysis and receiver operating characteristic (ROC) curve were applied to select features. The diagnostic performance of extreme gradient boosting (XGBoost) prediction model was evaluated by ROC curve, and the clinical applicability of XGBoost prediction model was evaluated by decision curve analysis (DCA). Results The tumor size of 206 patients with small pancreatic cancer was (1.69±0.77) cm. The area under curve (AUC), sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the XGBoost prediction model in the training set were 0.99, 0.92, 0.97, 0.91 and 0.98, respectively. The AUC, sensitivity, specificity, PPV and NPV of the XGBoost prediction model in the validation set were 0.99, 0.94, 0.96, 0.93 and 0.97, respectively. DCA analysis showed that patients could benefit from this model. Conclusion The XGBoost prediction model based on radiomics analysis of abdominal CT images can accurately differentiate small pancreatic cancer from normal pancreas. It is expected to be an auxiliary tool for screening small pancreatic cancer.