Abstract
BACKGROUND: Duodenal cancer is one of the most common subtypes of small intestinal cancer, and distant metastasis (DM) in this type of cancer still leads to poor prognosis. Although nomograms have recently been used in tumor areas, no studies have focused on the diagnostic and prognostic evaluation of DM in patients with primary duodenal cancer. AIM: To develop and evaluate nomograms for predicting the risk of DM and personalized prognosis in patients with duodenal cancer. METHODS: Data on duodenal cancer patients diagnosed between 2010 and 2019 were extracted from the Surveillance, Epidemiology, and End Results database. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for DM in patients with duodenal cancer, and univariate and multivariate Cox proportional hazards regression analyses were used to determine independent prognostic factors in duodenal cancer patients with DM. Two novel nomograms were established, and the results were evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS: A total of 2603 patients with duodenal cancer were included, of whom 457 cases (17.56%) had DM at the time of diagnosis. Logistic analysis revealed independent risk factors for DM in duodenal cancer patients, including gender, grade, tumor size, T stage, and N stage (P < 0.05). Univariate and multivariate COX analyses further identified independent prognostic factors for duodenal cancer patients with DM, including age, histological type, T stage, tumor grade, tumor size, bone metastasis, chemotherapy, and surgery (P < 0.05). The accuracy of the nomograms was validated in the training set, validation set, and expanded testing set using ROC curves, calibration curves, and DCA curves. The results of Kaplan-Meier survival curves (P < 0.001) indicated that both nomograms accurately predicted the occurrence and prognosis of DM in patients with duodenal cancer. CONCLUSION: The two nomograms are expected as effective tools for predicting DM risk in duodenal cancer patients and offering personalized prognosis predictions for those with DM, potentially enhancing clinical decision-making.