Gastric cancer and the search for a good prognostic classification: a challenge

Abstract

Esther Uña CidónMedical Oncology Service, Clinical University Hospital of Valladolid, Valladolid, SpainIntroduction: Gastric cancer is the second most common cancer worldwide. The standard ­treatment is radical surgical resection, but 60% of patients will present recurrence. TNM ­classification (6th edition, American Joint Committee on Cancer) recommends the removal of at least 15 regional lymph nodes to carry out an adequate pathological staging. But in our ­environment, only 20% of cases have the minimum recommended, so it would be very ­important to have an alternative prognostic. We designed a retrospective study to evaluate different ­prognostic factors in patients inadequately staged.Material and methods: We focused on patients with serosal involvement (pT3). The evaluation included general parameters such as age, sex, tumor site, histological type, type of gastrectomy, histological grade, number of nodes analyzed </≥ 10, lymph node ratio (LNR) with a 20% cutoff value, and adjuvant treatment. The association between clinicopathological variables and recurrence was investigated by univariate and multivariate logistic regression. It was considered statistically significant with P < 0.05. Results: A total of 92 patients who met the criteria were studied. Median age 65 years; 68% men, 32% women; median follow-up time for the overall population, 44 months (range 15–119 months); number of nodes analyzed, median 7 (range 0–14 nodes); recurrence in 59%; median time to recurrence, 15 months (range 3–48 months); the cumulative risk of relapse at five years, 64%. Multivariate statistical analysis showed that the LNR (P = 0.03) and total number of nodes analyzed </≥ 10 (P = 0.04) were independent predictors for the risk of recurrence.Conclusion: LNR and total number of nodes analyzed with a threshold of 10 (</≥ 10 nodes analyzed) were independent predictors of recurrence in patients with gastric carcinoma pT3 and an insufficient number of nodes examined.Keyword: pathological classification