Nomograms for predicting specific distant metastatic sites and overall survival of colorectal cancer patients: A large population‐based real‐world study

• Published in: Clinical and translational medicine 2020-03, Vol.10 (1), p.169-181
• Main Authors: Mo, Shaobo, Cai, Xin, Zhou, Zheng, Li, Yaqi, Hu, Xiang, Ma, Xiaoji, Zhang, Long, Cai, Sanjun, Peng, Junjie
• Format: Article
• Language: English
• Subjects: colorectal cancer; decision curve analysis; distant metastasis; nomogram; overall survival
• ISSN: 2001-1326; 2001-1326
• DOI: 10.1002/ctm2.20

Background This study aims to develop functional nomograms to predict specific distant metastatic sites and overall survival (OS) of colorectal cancer (CRC) patients. Methods CRC case data were retrospectively recruited from a large population‐based public dataset. Nomograms were developed to predict the probabilities of specific distant metastatic sites and OS of CRC patients. The performance of nomogram was evaluated with the concordance index (C‐index), calibration curves, area under the curve (AUC), and decision curve analysis (DCA). Results A total of 142 343 cases were included in the current study. On the basis of univariate and multivariate analyses, clinicopathological features were correlated with specific distant metastatic sites and survival outcomes and were used to establish nomograms. The nomograms showed excellent accuracy in predicting specific distant metastatic sites. The C‐indexes for the prediction of liver, lung, bone, and brain metastases were 0.82 (95% confidence interval (CI), 0.81‐0.83), 0.80 (95% CI, 0.78‐0.81), 0.83 (95% CI, 0.79‐0.86), and 0.73 (95% CI, 0.72‐0.84), respectively. Then, a prognostic nomogram integrating clinicopathological features and specific distant metastatic sites was established to predict 1‐, 3‐, and 5‐year OS of CRC, with AUCs of 0.764 (95% CI, 0.741‐0.783), 0.762 (95% CI, 0.745‐0.781), and 0.745 (95% CI, 0.730‐0.761), respectively. DCA showed that the prognostic nomogram had a better clinical application value than current TNM staging system. Conclusions Based on clinicopathological features, original nomograms were constructed for clinicians to predict specific distant metastatic sites and OS of CRC patients. These models could help to support the postoperative personalized assessment.