Prognostic classification index in Iranian colorectal cancer patients: Survival tree analysis


Malehi A. S., RAHIM F.

SOUTH ASIAN JOURNAL OF CANCER, vol.5, no.1, pp.23-26, 2016 (ESCI, Scopus) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 5 Issue: 1
  • Publication Date: 2016
  • Doi Number: 10.4103/2278-330x.179703
  • Journal Name: SOUTH ASIAN JOURNAL OF CANCER
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus
  • Page Numbers: pp.23-26
  • Kocaeli University Affiliated: No

Abstract

Aims: The aim of this study was to determine the prognostic index for separating homogenous subgroups in colorectal cancer (CRC) patients based on clinicopathological characteristics using survival tree analysis. Methods: The current study was conducted at the Research Center of Gastroenterology and Liver Disease, Shahid Beheshti Medical University in Tehran, between January 2004 and January 2009. A total of 739 patients who already have been diagnosed with CRC based on pathologic report were enrolled. The data included demographic and clinical-pathological characteristic of patients. Tree-structured survival analysis based on a recursive partitioning algorithm was implemented to evaluate prognostic factors. The probability curves were calculated according to the Kaplan-Meier method, and the hazard ratio was estimated as an interest effect size. Result: There were 526 males (71.2%) of these patients. The mean survival time (from diagnosis time) was 42.46 +/- (3.4). Survival tree identified three variables as main prognostic factors and based on their four prognostic subgroups was constructed. The log-rank test showed good separation of survival curves. Patients with Stage I-IIIA and treated with surgery as the first treatment showed low risk (median = 34 months) whereas patients with stage IIIB, IV, and more than 68 years have the worse survival outcome (median = 9.5 months). Conclusion: Constructing the prognostic classification index via survival tree can aid the researchers to assess interaction between clinical variables and determining the cumulative effect of these variables on survival outcome.