Prognostic Nomograms for Predicting Overall Survival and Cancer-Specific Survival of Patients With Early Onset Colon Adenocarcinoma.

BACKGROUND

The incidence of colon cancer in young patients is on the rise, of which adenocarcinoma is the most common pathological type. However, a reliable nomogram for early onset colon adenocarcinoma (EOCA) to predict prognosis is currently lacking. This study aims to develop nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) of patients with EOCA.

METHODS

Patients diagnosed with EOCA from 2010 to 2015 were included and randomly assigned to training set and validation set. Cox regression models were used to evaluate prognosis and identify independent predictive factors, which were then utilized to establish the nomograms for predicting 3- and 5-year OS and CSS. The discrimination and calibration of nomograms were validated using the calibration plots, concordance index, receiver operating characteristics curve, and the decision curve analysis.

RESULTS

A total of 2,348 patients were screened out, with 1,644 categorized into the training set and 704 into the validation set. Multivariate analysis demonstrated that gender, age, tumor size, T stage, M stage, regional node, tumor deposits, lung metastasis and perineural invasion were significantly correlated with OS and CSS. The calibration plots indicated that there was good consistency between the nomogram prediction and actual observation. The C-indices for training set of OS and CSS prediction nomograms were 0.735 (95% CI: 0.708-0.762) and 0.765 (95% CI: 0.739-0.791), respectively, whereas those for validation set were 0.736 (95% CI: 0.696-0.776) and 0.76 (95% CI: 0.722-0.798), respectively. The results of ROC analysis revealed the nomograms showed a good discriminate power. The 3- and 5-year DCA curves displayed superiority over TNM staging system with higher net benefit gains.

CONCLUSIONS

The nomograms established could effectively predict 3- and 5-year OS and CSS in EOCA patients, which assisted clinicians to evaluate prognosis more accurately and optimize treatment strategies.