Nomogram to Predict the Overall Survival of Colorectal Cancer Patients: A Multicenter National Study

Int J Environ Res Public Health. 2021 Jul 21;18(15):7734. doi: 10.3390/ijerph18157734.

Abstract

Background: Colorectal cancer (CRC) is the third foremost cause of cancer-related death and the fourth most commonly diagnosed cancer globally. The study aimed to evaluate the survival predictors using the Cox Proportional Hazards (CPH) and established a novel nomogram to predict the Overall Survival (OS) of the CRC patients.

Materials and methods: A historical cohort study, included 1868 patients with CRC, was performed using medical records gathered from Iran's three tertiary colorectal referral centers from 2006 to 2019. Two datasets were considered as train set and one set as the test set. First, the most significant prognostic risk factors on survival were selected using univariable CPH. Then, independent prognostic factors were identified to construct a nomogram using the multivariable CPH regression model. The nomogram performance was assessed by the concordance index (C-index) and the time-dependent area under the ROC curve.

Results: The age of patients, body mass index (BMI), family history, tumor grading, tumor stage, primary site, diabetes history, T stage, N stage, and type of treatment were considered as significant predictors of CRC patients in univariable CPH model (p < 0.2). The multivariable CPH model revealed that BMI, family history, grade and tumor stage were significant (p < 0.05). The C-index in the train data was 0.692 (95% CI, 0.650-0.734), as well as 0.627 (0.670, 0.686) in the test data.

Conclusion: We improved a novel nomogram diagram according to factors for predicting OS in CRC patients, which could assist clinical decision-making and prognosis predictions in patients with CRC.

Keywords: colorectal cancer; cox proportional hazards; nomogram; overall survival; risk factors.

Publication types

  • Multicenter Study

MeSH terms

  • Cohort Studies
  • Colorectal Neoplasms* / pathology
  • Humans
  • Neoplasm Staging
  • Nomograms*
  • Proportional Hazards Models