Construction of a nomogram to predict overall survival for patients with M1 stage of colorectal cancer: A retrospective cohort study

Int J Surg. 2019 Dec:72:96-101. doi: 10.1016/j.ijsu.2019.10.021. Epub 2019 Oct 31.

Abstract

Background: The M1 stage of colorectal cancer (CRC) has a poor prognosis. The aim of this study is to develop a reliable tool for the prediction for CRC patients with M1 stage, thus assisting the strategy of clinical diagnosis and treatment.

Methods: CRC patient information collected in the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015 was extracted and evaluated. Multivariate analysis with Cox proportional hazards regression identified risk factors that predicted overall survival (OS) and the results were used to construct a nomogram to predict 3-, and 5-year OS in CRC patients with M1 stage. The Kaplan-Meier curve was plotted to evaluate OS differences.

Results: A total of 19,796 patients from the SEER database were included for analysis. All patients were randomly allocated to 2 cohorts, the training cohort (n = 13,860) and the validation cohort (n = 5936). Patients' age at diagnosis; gender; race; tumor site; tumor grade; T and N stage; brain, lung, bone, and liver metastasis status; marital status; and therapy were associated with survival in the multivariate models. All these factors were incorporated to construct a nomogram. Additionally, we divide all 19,796 patients into high-risk group and low-risk group according to our nomogram, and plotted Kaplan-Meier curve. The result indicated that patients with higher risk had worse survival outcomes.

Conclusions: Our predictive model has the potential to provide an individualized risk estimate of survival in CRC patients with M1 stage.

Keywords: Colorectal cancer; M1 stage; Nomogram; SEER.

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Colorectal Neoplasms / mortality*
  • Colorectal Neoplasms / pathology
  • Female
  • Humans
  • Male
  • Middle Aged
  • Neoplasm Staging
  • Nomograms*
  • Proportional Hazards Models
  • Retrospective Studies