Developing prognostic nomograms to predict overall survival and cancer-specific survival in synchronous multiple primary colorectal cancer based on the SEER database

Abstract

Abstract Background: Synchronous multiple primary colorectal cancer (SMPCC) is a rare subtype of CRC, characterized by the presence of two or more primary CRC lesionssimultaneously or within 6 months from the detection of the first lesion. We aim to develope a novel nomogram to predict OS and CSS for SMPCC patients using data from the SEER database. Methods: The clinical variables and survival data of SMPCC patients between 2004 and 2018 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Appropriate inclusion and exclusion criteria were established to screen the enrolled patients. Univariate and multivariate cox regression analyses were used to identify the independent risk factors for OS and CSS. The performance of the nomogram was evaluated using the concordance index (C-index), calibration curves, and the area under the curve (AUC) of a receiver operating characteristics curve (ROC). A decision curve analysis (DCA) was generated to compare the net benefits of the nomogram with those of the TNM staging system. Results: A total of 6772 SMPCC patients were enrolled in the study and randomly assigned to the training (n = 4670) and validation (n = 2002) cohorts. Multivariate cox analysis confirmed that race, marital status, age, histology, tumor position, T stage, N stage, M stage, chemotherapy, and the number of dissected LNs were independent prognostic factors.The C-index values for OS and CSS prediction were 0.716 (95%CI: 0.705–0.727) and 0.718 (95%CI: 0.702–0.734) in the training cohort, and 0.760 (95%CI: 0.747–0.773) and 0.749 (95%CI: 0.728–0.769) in the validation cohort. The ROC and calibration curves indicated that the model had good stability and reliability. Decision curve analysis revealed that the nomograms provided more significant clinical net benefit than the TNM staging system. Conclusion: We developed a novel nomogram for clinicians to predict OS and CSS and could be used to optimize the treatment in SMPCC patients.