Modelling the probability of erroneous negative lymph node staging in patients with colon cancer

Abstract

Background: Patients in who with insufficient number of analysed lymph nodes (LNs) are more likely to receive an incorrect LN staging. The ability to calculate the overall probability of undiagnosed LN involvement errors in these patients could be very useful for approximating the real patient prognosis and for giving possible indications for adjuvant treatments. The objective of this work was to establish the predictive capacity and prognostic discriminative ability of the final error probability (FEP) among patients with colon cancer and with a potentially incorrectly-staged LN-negative disease. Methods: This was a retrospective multicentric population study carried out between January 2004 and December 2007. We used a mathematical model based on Bayes' theorem to calculate the probability of LN involvement given a FEP test result. Cumulative sum graphs were used to calculate risk groups and the survival rates were calculated, by month, using the Kaplan-Meier method. Results: A total of 548 patients were analysed and classified into three risk groups according to their FEP score: low-risk (FEP < 2%), intermediate-risk (FEP 2%-15%), and high-risk (FEP > 15%). Patients with LN involvement had the lowest overall survival rate when compared to the three risk groups. This difference was statistically significant for the low- and intermediate-risk groups (P = 0.002 and P = 0.004, respectively), but high-risk group presented similar survival curves to pN+ group (P = 0.505). In terms of disease-free survival, the high-risk group presented similar curves to the intermediate-risk group until approximately 60 months' follow-up (P = 0.906). After 80 months' follow-up, the curve of high-risk group coincided with that of the pN+ group (P = 0.172). Finally, we summarized the FEP according to the number of analysed LNs and accompanied by a contour plot which represents its calculation graphically. Conclusions: The application of Bayes' theorem in the calculation of FEP is useful to delimit risk subgroups from among patients without LN involvement.