Prediction of distant metastasis and specific survival prediction of small intestine cancer patients with metastasis: a population-based study

Abstract

Abstract Background: Small intestine cancer (SIC) is a rarely found gastrointestinal malignancy, however early diagnosis of SIC is difficult as patients often present poor prognoses due to distant metastasis (DM) of the tumor by the time of diagnosis. Although nomograms for SIC have been developed, there are no relevant studies on the diagnosis and prognostic assessment of DM in patients with SIC. Methods: The data of patients diagnosed with SIC between 2010 and 2015 was extracted from the Surveillance, Epidemiology and End Results (SEER) database. All patients were randomly assigned into the training and validation sets (7:3). Independent risk factors for DM in SIC patients were then determined by univariate and multifactor logistic regression analysis. In addition, independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS) in patients with DM were analyzed by univariate and multifactor Cox regression analysis, respectively. We then constructed the corresponding three nomograms and assessed the clinical efficacy of the nomograms by receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves. Result: The entire cohort consisted of 6773 SIC patients, of whom 1327 had DM at diagnosis. The results of multifactorial logistic regression analysis showed that T-stage, N-stage, tumor collaborative stage (CS) extension and histological type acted as independent risk factors for DM in patients with SIC. The results of multifactorial Cox regression analysis indicated that age, sex, histological type, N stage and tumor CS extension were independent predictors of OS; sex, histological type, N stage and tumor CS extension served as independent predictors of CSS. The results of ROC curves, DCA, calibration curves, and Kaplan-Meier (K-M) survival curves in the training and validation sets further confirmed the excellent accuracy of the three nomograms in predicting DM and prognosis in SIC patients. Conclusion: This study constructed and validated nomograms for predicting DM in SIC patients as well as OS and CSS in patients with DM, which appear to function, as excellent tools to aid the physicians make more rational and personalized clinical decisions.