Lymph node status assessed through the log odds ratio - a better tool in the prognosis of colorectal cancer relapse

Rom J Morphol Embryol. 2014;55(1):97-102.

Abstract

The current literature related to colorectal cancer shows there is a great inhomogeneity in patient outcome, even between patients in the same stage, which means that the TNM staging does not seem enough anymore to make a therapeutic decision. This is why many of the recent studies focus on the study of prognostic and predictive factors that would make the therapeutic decision-making process more accurate. In the current study, we focused on the study of two lymph node based scores - the lymph node ratio and the log odds ratio and the morphological characteristics of the tumor to try to see if any of them can predict a more aggressive tumor behavior in order to approach the patient in an appropriate way. The study included 25 patients presenting over a period of two years (2009-2011) for a local relapse or a metastasis after curative surgery for colorectal cancer. From the morphological characteristics of the tumor, only the protruding character of the tumor positively correlated at a statistically significant level with the recurrence-free time. We also proved that between the two lymph node scores and the pN stage, the log odds ratio was the one that best correlated with both the number of invaded lymph nodes and the number of resected nodes. The log odds ratio also proved to correlate well with the risk of developing a distant metastasis. Our study also shows for the first time that the log odds ratio is able to stratify patients according to their risk of a fast relapse.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Colorectal Neoplasms / pathology*
  • Female
  • Humans
  • Kaplan-Meier Estimate
  • Lymph Nodes / pathology*
  • Male
  • Middle Aged
  • Neoplasm Recurrence, Local / diagnosis*
  • Neoplasm Recurrence, Local / pathology
  • Neoplasm Staging
  • Odds Ratio
  • Prognosis
  • Statistics, Nonparametric