Variable Patients Male Female Primary Tumor pT1 pT2 pT3 pT4 Lymph node status pN0 pN1 Tumor grade G2 G3 Patients 50 (71) 20 (29) 15 (21) 11 (16) 42 (60) 2 (3) 33 (47) 37 (53) 47 (67) 23 (33) Variable Patients Male Female Primary Tumor pT1 pT2 pT3 Lymph node status pN0 pN1 Tumor grade G2 G3 Patients 39 (74) 14 (26) 15 (28) 8 (15) 30 (57) 27 (51) 26 (49) 38 (72) 15 (28) 0 15 10 (67) 5 (33) 4 (27) 3 (20) 8 (53) 0 (0) 7 (47) 8 (53) 10 (67) 5 (33) 0 10 8 (80) 2 (20) 4 (40) 1 (10) 5 (50) 4 (40) 6 (60) 7 (70) 3 (30) 1+ 18 12 (67) 6 (33) 3 (17) 2 (11) 11 (61) 2 (11) 9 (50) 9 (50) 12 (67) 6 (33) 1+ 15 10 (67) 5 (33) 3 (20) 2 (13) 10 (67) 9 (60) 6 (40) 11 (73) 4 (27) 2+ 8 5 (38) 3 (62) 3 (38) 2 (24) 3 (38) 0 (0) 6 (75) 2 (25) 6 (75) 2 (25) 2+ 7 4 (57) 3 (43) 3 (42) 2 (29) 2 (29) 5 (71) 2 (29) 5 (71) 2 (29) BMC Cancer 2006, 6:165 http://www.biomedcentral.com/1471-2407/6/165 Table 1: Patient and tumor characteristics (The numbers in parenthesis are percentages).* Ep-CAM expression 3+ 29 23 (79) 6 (21) 5 (17) 4 (14) 20 (69) 0 (0) 11 (38) 18 (62) 19 (66) 10 (34) 3+ 21 17 (81) 4 (19) 5 (24) 3 (14) 13 (62) 9 (43) 12 (57) 15 (71) 6 (29) *No statistical significant correlations between Ep-CAM expression and the histopathological parameters were observed. Statistical analysis To test the correlation between the clinico-pathological data and the level of Ep-CAM expression we used the Fisher's exact test and whenever appropriate the χ2-test. All of the variables were dichotomised. For analysis of fol- low-up data, life table curves were calculated using the Kaplan-Meier method, and survival distributions were compared using the log-rank test. The primary end points were disease-specific survival or relapse-free survival, as measured from the date of surgery to the time of the last follow-up or cancer-related death or tumor relapse, respectively. Data of patients who were still alive and without evidence of tumor relapse at the end of the study were censored. For multivariate analysis, a parametric sur- vival regression model based on a Weibull distribution was applied. Model selection was carried out using the Bayesian Information Criterion (BIC) [25,26]. This infor- mation criterion was adopted to search for a parsimoni- ous model that fit the data [27]. The lower the BIC the better the model explains the data. A search in the model space was done with a stepwise algorithm. The range of the models examined in the stepwise search was from the simplest model with a constant term, to the most complex model with all covariates included. The investigated dis- tributions were loglogistic, lognormal, logistic, exponen- tial, Gaussian and Weibull. Both, the exponential and the Weibull distributions fitted the data best. Because the exponential distribution is a particular case of the Weibull when the scale parameter equals one, we decided to use the Weibull distribution. Statistical computations were Table 2: Patient and tumor characteristics of the 53 patients with clinical follow-up data (The numbers in parenthesis are percentages).* Ep-CAM expression *No statistical significant correlations between Ep-CAM expression and histopathological parameters were observed. Page 3 of 8 (page number not for citation purposes)